From f7a301c18b3431d3c9afd81e96d8a7e40ee16ec8 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 16:14:35 -0400 Subject: [PATCH 01/72] Add files via upload --- docs/source/Legate_Boost.rst | 785 +++++++++++++++++++++++++++++++++++ 1 file changed, 785 insertions(+) create mode 100644 docs/source/Legate_Boost.rst diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst new file mode 100644 index 00000000..618ac7dc --- /dev/null +++ b/docs/source/Legate_Boost.rst @@ -0,0 +1,785 @@ +This article assumes familiarity with the basic usage of gradient +boosting libraries such as XGBoost or LightGBM, as well as cuPyNumeric +for GPU-accelerated array computations. + +**What is legate boost?** +------------------------- + +In scenarios where high-performance training is needed across large +datasets or distributed hardware, Legate Boost offers a scalable +alternative. Legate Boost is an advanced gradient boosting library built +on the Legate and Legion parallel programming frameworks. Unlike +traditional boosting libraries such as XGBoost or LightGBM, Legate Boost +provides a unified infrastructure that seamlessly scales across CPUs and +GPUs, supporting both single-node and distributed training while +integrating naturally with cuPyNumeric workflows for efficient +end-to-end data processing. It enables users to define not only +conventional boosted decision trees but also hybrid ensembles combining +trees, kernel ridge regression, linear models, or neural networks, all +written in Python with minimal code changes. + +These models are automatically parallelized and executed efficiently +without manual data movement or partitioning. Legate Boost emphasizes +architectural simplicity, extensibility, and performance, delivering +state-of-the-art results on tabular data while leveraging the full +computational power of modern heterogeneous hardware. + +Please refer to `Distributed computing +cuPyNumeric `__ +and `Legate +boost `__ for more +information and detailed instructions on installation. + +**Quick installation and setup** +-------------------------------- + ++--------------------------------------------------------------------------------------------+ +| # create a new env and install legate boost and dependencies | +| | +| $ conda create -n legate-boost -c legate -c conda-forge -c nvidia legate-boost | +| | +| # activate env | +| | +| $ conda activate legate-boost | +| | +| # install wrappers | +| | +| | $ conda install -c legate/label/gex-experimental realm-gex-wrapper legate-mpi-wrapper | +| | # install cmake | +| | +| $ conda install -c conda-forge cmake>=3.26.4 | +| | +| # build wrappers | +| | +| $ | +| /global/homes/**n/ngraddon**/.conda/envs/legate-boost-new/mpi-wrapper/build-mpi-wrapper.sh | +| | +| $ | +| /global/homes/**n/ngraddon**/.conda/envs/legate-boost-new/gex-wrapper/build-gex-wrapper.sh | +| | +| # reactivate env | +| | +| $ conda activate legate-boost | +| | +| # install legate-dataframe | +| | +| $ conda install -c legate -c rapidsai -c conda-forge legate-dataframe | ++============================================================================================+ + +**Usage** +--------- + +Legate Boost offers two main estimator types: + +- LBRegressor for regression tasks + +- LBClassifier for classification tasks + +These estimators follow a similar interface to those in **XGboost**, +making them easy to integrate into existing machine learning pipelines. + +**Regression with LBRegressor** +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The LBRegressor estimator is used to predict continuous values such as +house prices, temperature, or sales forecasting. The following code +demonstrates how to create an instance of the LBRegressor model, use the +fit() function to train it on a dataset, and then apply the predict() +function to generate predictions on new data. Here’s how to set it up: + +import legateboost as lb + +from sklearn.datasets import make_regression + +#creating synthetic dataset + +x,y = make_regression(n_samples=100, n_features=4, noise=8, +random_state=42) + +#splitting the data into training and testing sets + +X_train, X_test, y_train, y_test = train_test_split( + +data.data, data.target, test_size=0.2, random_state=42) + +#regression model with 100 estimators + +regresion_model = lb.LBRegressor(n_estimators=100) + +#fit the model + +regression_model.fit(X_train, y_train) + +#predict using the trained model + +y_pred = regression_model.predict(X_test) + + + +In this example: + +- LBRegressor is initialized with 100 boosting estimators. + +- The fit() method trains the model using the input features (X_train) + and target values (y_train). + +- After training, the predict() method is used to make predictions on + the test set (X_test). + +This represents a typical workflow for applying a regression model using +Legate Boost. The LBRegressor estimator offers several configurable +options, such as base_model and learning_rate, to help optimize model +performance. For a comprehensive list of features and parameters, refer +to the official +`documentation `__. + +**Classification with LBClassifier** +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The LBClassifier is designed for predicting categorical outcomes and +supports both binary and multi-class classification tasks. It is ideal +for a wide range of applications, including spam detection, image +classification, and sentiment analysis. + +The example below demonstrates how to implement a classification model +using the LBClassifier estimator from Legate Boost: + +import legateboost as lb + +from sklearn.datasets import make_regression + +from sklearn.model_selection import train_test_split + +#creating synthetic dataset + +x,y = make_regression(n_samples=100, n_features=4, n_informative=3, +n_redundant=0, n_classes=2, random_state=42) + +#splitting the data into training and testing sets + +X_train, X_test, y_train, y_test = train_test_split( + +data.data, data.target, test_size=0.2, random_state=42) + +# Create a classification model with 50 boosting estimators + +classification_model = lb.LBClassifier(n_estimators=50) + +# Train the model on labeled data + +classification_model.fit(X_train, y_train) + +# Predict class labels on new data + +y_pred = classification_model.predict(X_test) + +In this example: + +- LBClassifier(n_estimators=50) sets up a classifier with 50 boosting + rounds. + +- fit(X_train, y_train) learns the patterns from your training dataset. + +- predict(X_test) outputs predicted class labels for the test dataset. + +Just like the regressor, the LBClassifier follows a clean and intuitive +workflow. It provides additional options and advanced configurations to +optimize model performance. For more detailed information, refer to the +Legate Boost estimators +`documentation `__. + +**Example 1** +------------- + +Here is an example of using Legate Boost to build a regression model on +the California housing dataset. + +It showcases key features like scalable training across GPUs/nodes, +customizable base models, and adjustable learning rates. + +**About dataset** +~~~~~~~~~~~~~~~~~~ + +The California housing dataset is a classic benchmark dataset containing +information collected from California districts in the 1990 census. Each +record describes a block group (a neighborhood-level area), including +predictors such as: + +- Median income of residents + +- Average house age + +- Average number of rooms and bedrooms + +- Population and household count + +- Latitude and longitude + +The target variable is the **median house value** in that block group. +This dataset is often used to illustrate regression techniques and +assess predictive performance on real-world tabular data. + +**About this implementation** +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The following code creates a Legate Boost regression model using +LBRegressor, which trains a gradient boosting model optimized for +multi-GPU and multi-node environments. The model is configured to use +100 boosting rounds (n_estimators=100), with each round adding a +decision tree (lb.models.Tree) limited to a maximum depth of 5. The loss +function is set to "squared_error", suitable for regression tasks as it +minimizes mean squared error. A learning_rate of 0.1 controls how much +each tree contributes to the final prediction, balancing speed and +stability. The verbose=True flag enables logging during training, +allowing to monitor progress and internal operations. + +**Code module** +~~~~~~~~~~~~~~~~ + +import cupynumeric as cn + +import legateboost as lb + +from legate.timing import time + +from sklearn.metrics import mean_squared_error + +from sklearn.model_selection import train_test_split + +from sklearn.datasets import fetch_california_housing + +#load and split data + +data = fetch_california_housing() + +X_train, X_test, y_train, y_test = train_test_split( + +data.data, data.target, test_size=0.2, random_state=42) + +#create Legate Boost regressor + +model = lb.LBRegressor(n_estimators=100, +base_models=(lb.models.Tree(max_depth=5),), + +objective = **"squared_error"**, learning_rate = 0.1, verbose=\ **True** + +) + +#training + +start = time() + +model.fit(X_train, y_train) + +end = time() + +#prediction + +istart = time() + +y_pred = model.predict(X_test) + +iend = time() + +#evaluate + +mse = mean_squared_error(y_test, y_pred) + +print(f"Test MSE: {mse:.4f}") + +print(f"\\n The training time for housing exp is: {(end - +start)/1000:.6f}ms") + +print(f"\\n The inference time for housing exp is {(iend - +istart)/1000:.6f}") + + +- + +This simple example demonstrates how to train a regression model on the +California Housing dataset using Legate Boost. Although the code looks +similar to standard XGBoost, Legate Boost automatically enables +multi-GPU and multi-node computation. Legate Boost achieves multi-GPU +and multi-node scaling through its integration with cupynumeric and the +Legion runtime. Unlike traditional GPU libraries that allocate data to a +single device, cupynumeric creates Logical Arrays and abstract +representations of the data that are not bound to one GPU. The Legate +automatically partitions these logical arrays into physical chunks and +maps them across all available GPUs and nodes. + +During training, operations such as histogram building, gradient +computation, and tree construction are expressed as parallel tasks. +Legate schedules these tasks close to where the data resides, minimizing +communication overhead. When synchronization is needed (e.g., to combine +histograms from multiple GPUs), it is handled by legate-mpi-wrapper and +realm-gex-wrapper, so we never have to write MPI or manage explicit GPU +memory transfers. + +**Running on CPU and GPU** +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +**CPU** - To run with CPU, use the following command. + ++-----------------------------------------------------------------------+ +| **legate --cpus 1 --gpus 0 ./housing.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **The training time for housing exp is: 7846.303000 milliseconds** | ++=======================================================================+ + +**GPU** - To run with GPU, use the following command. + ++-----------------------------------------------------------------------+ +| **legate --gpus 2 ./housing.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **The training time for housing exp is : 846.949000 milliseconds** | ++=======================================================================+ + +**To Do: Multi Node and Multi GPU** + +**Example 2** +------------- + +This example demonstrates how Legate Boost can be applied to the *“Give +Me Some Credit”* dataset (OpenML data_id: 46929) to build a +classification model using ensemble learning by combining different +model types. It also highlights the integration of Legate DataFrame with +Legate Boost to enable distributed training across multi-GPU and +multi-node environments, showcasing scalable machine learning on the +Credit Score dataset. + +**About the dataset** +~~~~~~~~~~~~~~~~~~~~~ + +The Give Me Some Credit dataset is a financial risk prediction dataset +originally introduced in a Kaggle competition. It includes anonymized +credit and demographic data for individuals, with the goal of predicting +whether a person is likely to experience serious financial distress +within the next two years. + +Each record represents an individual and includes features such as: + +- Revolving utilization of unsecured credit lines + +- Age + +- Number of late payments (30–59, 60–89, and 90+ days past due) + +- Debt ratio + +- Monthly income + +- Number of open credit lines and loans + +- Number of dependents\ ** + ** + +The target variable is binary (0 = no distress, 1 = distress), +indicating the likelihood of future financial trouble. + +.. _about-this-implementation-1: + +**About this implementation** +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This implementation will focus on demonstrating Legate Boost’s flexible +model ensembling capabilities, specifically: + +- Tree-based gradient boosting models, ideal for structured/tabular + data. + +- Neural network-based classifiers, allowing hybrid or deep learning + approaches. + +By leveraging Legate Boost, we can ensemble these two models and +efficiently train and evaluate both model types on GPUs or CPUs, +showcasing scalable performance for large tabular datasets in financial +risk prediction. + +The pipeline begins with importing required libraries and its functions +and also loading the dataset using fetch_openml. Depending on hardware +availability, the data is initially handled either with cuDF (for GPU +execution) or pandas (for CPU execution). The dataset is then wrapped +into a LogicalTable, the distributed data representation used by Legate +DataFrame. LogicalTables internally break data into logical columns, +enabling Legate’s runtime to partition, distribute, and schedule +computations across multiple GPUs and nodes. + + + +import cudf + +import pandas + +import cupy as cp + +import pyarrow as pa + +import legate_dataframe + +import legateboost as lb + +import cupynumeric as cpn + +from legate.timing import time + +from sklearn.datasets import fetch_openml + +from sklearn.metrics import accuracy_score + +from sklearn.model_selection import train_test_split + +from legate_dataframe.lib.replace import replace_nulls + +from legate_dataframe.lib.core.table import LogicalTable + +from legate_dataframe.lib.core.column import LogicalColumn + +#import data + +data = fetch_openml(data_id= 46929, as_frame=True) + +xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas # based on +CPU or GPU + +df = xd.DataFrame(data.data, columns=data.feature_names) + +df['Target'] = data.target + +#covert to logicalTable + +if cp.cuda.runtime.getDeviceCount() > 0: + +ldf = LogicalTable.from_cudf(df) + +else: + +df = pa.Table.from_pandas(df) + +ldf = LogicalTable.from_arrow(df) + + + +Let’s see how data preprocessing is performed directly on the +LogicalTable. Missing values in key columns (MonthlyIncome and +NumberOfDependents) are filled using median imputation through the +replace_nulls operation. These operations are executed in parallel +across distributed partitions of the LogicalTable, avoiding centralized +bottlenecks. Because LogicalTables are immutable, a new LogicalTable +with updated LogicalColumn’s is created after preprocessing. The cleaned +data is then converted into a cuPyNumeric array, Legate’s +GPU-accelerated array type that leverages logical partitioning for +distributed computation. This enables the subsequent machine learning +tasks to execute efficiently across multiple GPUs or nodes. + +#replace nulls + +median_salary = df["MonthlyIncome"].median() + +median_dependents = df["NumberOfDependents"].median() + +mni = +LogicalColumn(replace_nulls(LogicalColumn(ldf["MonthlyIncome"]),median_salary)) + +mnd = +LogicalColumn(replace_nulls(LogicalColumn(ldf["NumberOfDependents"]),median_dependents)) + +#create a new logical Table with updated columns + +features = ldf.get_column_names() + +nldf = LogicalTable( [ ldf[0], ldf[1], ldf[2], ldf[3], mmi, ldf[5], +ldf[6], ldf[7], ldf[8], mnd, ldf[10]], features) + +#coverting LogicalTable to cupyNumeric array + +data_arr = nldf.to_array() + + + +As we have a data_arr backed by cuPyNumeric, we first split the dataset +into training and testing subsets, which are then passed to Legate Boost +for efficient training across available hardware resources. The model is +built using Legate Boost’s ensemble framework (LBClassifier), which +allows combining multiple types of base learners into a single unified +model. + +In this example, the ensemble consists of a Decision Tree +(lb.models.Tree) with max_depth=8, enabling the capture of complex +non-linear decision boundaries by splitting the feature space +hierarchically up to 8 levels deep, and a Neural Network (lb.models.NN) +with two hidden layers of 10 neurons each (hidden_layer_sizes=(10,10)), +trained for max_iter=10 epochs with verbose=True to monitor progress. By +combining a tree-based model with a neural network, Legate Boost +leverages the interpretability and rule-based decision-making of trees +together with the ability of neural networks to model intricate, +high-dimensional relationships. This ensemble design results in a more +accurate and robust classifier than either model could achieve +individually. + +#preparing data for training and testing + +x = data_arr[:, :-1] + +y = data_arr[:, -1] + +num_samples = x.shape[0] + +split_ratio = 0.8 + +split_index = int(num_samples \* split_ratio) + +x_train = x[:split_index] + +y_train = y[:split_index] + +x_test = x[split_index:] + +y_test = y[split_index:] + +start=time() + +#create model and trian it + +model = +lb.LBClassifier(base_models=(lb.models.Tree(max_depth=8),lb.models.NN(max_iter=10,hidden_layer_sizes=(10,10),verbose=True))) + +model.fit(x_train,y_train) + +end=time() + +The trained ensemble model is used to generate predictions on the test +set, and its accuracy is evaluated using accuracy_score. Finally, the +model is saved with Joblib for future inference without retraining. + +#predict + +predictions = model.predict(x_test) + +#evalution + +acc = accuracy_score(y_test, predictions) + +print("Accuracy:", acc) + +print(f"\\n The training time for creditscore exp is: {(end - +start)/1000:.6f}ms") + +#model save + +dump(model, "legate_boost_model.joblib") + +#save the test sets to CSV for easy inference performance calculation + +x_test_cpu = x_test.get() if hasattr(x_test, "get") else +np.array(x_test) + +y_test_cpu = y_test.get() if hasattr(y_test, "get") else +np.array(y_test) + +pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) + +pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", +index=False) + + + +This workflow illustrates how Legate DataFrame provides a scalable +preprocessing layer, cupynumeric arrays enable distributed GPU +computation, and Legate Boost delivers a flexible ensemble learning +framework capable of leveraging multi-node, multi-GPU infrastructure +efficiently. + +.. _running-on-cpu-and-gpu-1: + +**Running on CPU and GPU** +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +**CPU** - To run with CPU, use the following command. +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ++-----------------------------------------------------------------------+ +| **legate --cpus 1 --gpus 0 ./creditscore.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **Accuracy: 0.9343** | +| | +| **The training time for creditscore exp is: 45337.714000 ms** | ++=======================================================================+ + +**GPU** - To run with GPU, use the following command. +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ++-----------------------------------------------------------------------+ +| **legate --gpus 2 ./creditscore.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **Accuracy: 0.9353** | +| | +| **The training time for creditscore exp is: 2688.233000 ms** | ++=======================================================================+ + +**To Do: Multi Node and Multi GPU** + +**Inference performance** +------------------------- + +Let’s explore how cuPyNumeric can be leveraged to measure inference +performance statistics seamlessly across both CPU and GPU all without +modifying the code. In this example, we evaluate a pre-trained machine +learning model by calculating key metrics such as mean, median, minimum, +maximum, variance, and standard deviation of inference times. The model +is loaded using joblib, and predictions are executed multiple times on +the test dataset. By utilizing cuPyNumeric arrays, the timing results +are efficiently processed while ensuring compatibility with both CPU and +GPU environments. This approach provides a simple yet powerful way to +compare inference performance across hardware, offering clear insights +into the speedup and variability achieved with GPU acceleration. + +import cupynumeric as cp + +from joblib import load + +from legate.timing import time + +import pandas as pd + +import legate.core as lg + +timings = [] + +#load pre-trained model + +model = load("legate_boost_model.joblib") + +X = pd.read_csv("x_test.csv") + +rt = lg.get_legate_runtime() + +for \_ in range(10): + +rt.issue_execution_fence() + +start = time() + +model.predict(X) + +rt.issue_execution_fence() + +end = time() + +timings.append(end - start) + +#first iteration is not considered here because, it includes env setup +time for gpu usage + +timings = timings[1:] + +timings_gpu = cp.array(timings) + +mean_time = cp.mean(timings_gpu) + +median_time = cp.median(timings_gpu) + +min_time = cp.min(timings_gpu) + +max_time = cp.max(timings_gpu) + +var_time = cp.var(timings_gpu) + +std = cp.sqrt(var_time) + +print(f"Mean: {float(mean_time)/1000:.2f} ms") + +print(f"Median: {float(median_time)/1000:.2f} ms") + +print(f"Min: {float(min_time)/1000:.2f} ms") + +print(f"Max: {float(max_time)/1000:.2f} ms") + +print(f"Variance: {float(var_time)/1000:.2f} ms") + +print(f"standard deviation: {float(std)/1000:.2f} ms") + + + +.. _running-on-cpu-and-gpu-2: + +**Running on CPU and GPU** +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. _cpu---to-run-with-cpu-use-the-following-command.-1: + +**CPU** - To run with CPU, use the following command. +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ++-----------------------------------------------------------------------+ +| **legate --cpus 1 --gpus 0 ./inference.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **Mean: 265.66 ms** | +| | +| **Median: 262.97 ms** | +| | +| **Min: 249.78 ms** | +| | +| **Max: 284.44 ms** | +| | +| **Variance: 117319.15 ms** | +| | +| **standard deviation: 10.83 ms** | ++=======================================================================+ + +.. _gpu---to-run-with-gpu-use-the-following-command.-1: + +**GPU** - To run with GPU, use the following command. +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ++-----------------------------------------------------------------------+ +| **legate --gpus 1 ./inference.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **Mean: 122.35 ms** | +| | +| **Median: 122.11 ms** | +| | +| **Min: 121.28 ms** | +| | +| **Max: 125.97 ms** | +| | +| **Variance: 1793.76 ms** | +| | +| **standard deviation: 1.34 ms** | ++=======================================================================+ + +These results clearly show the performance benefits of running inference +on a GPU compared to a CPU using cuPyNumeric arrays. On the CPU, the +model achieved a mean inference time of approximately **265.66 ms**, +with relatively low variability (standard deviation ~\ **10.83 ms**). In +contrast, the GPU significantly reduced the mean inference time to +around **122.35 ms**, representing more than a **2x speedup**, with even +lower variability (standard deviation ~\ **1.34 ms**). This highlights +how cuPyNumeric enables the same code to seamlessly scale across CPU and +GPU, allowing both accurate performance benchmarking and efficient model +deployment across heterogeneous hardware. + +**To Do: Multi Node and Multi GPU** From 6ae5a9b540ea46b5c71a37a1dddaf60361323fe7 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 17:15:36 -0400 Subject: [PATCH 02/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 738 ++++++++++++++--------------------- 1 file changed, 301 insertions(+), 437 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index 618ac7dc..30844230 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -1,9 +1,16 @@ +.. _legate-boost: + +============= +Legate Boost +============= + + This article assumes familiarity with the basic usage of gradient boosting libraries such as XGBoost or LightGBM, as well as cuPyNumeric for GPU-accelerated array computations. -**What is legate boost?** -------------------------- +What is legate boost? +====================== In scenarios where high-performance training is needed across large datasets or distributed hardware, Legate Boost offers a scalable @@ -24,62 +31,56 @@ architectural simplicity, extensibility, and performance, delivering state-of-the-art results on tabular data while leveraging the full computational power of modern heterogeneous hardware. -Please refer to `Distributed computing -cuPyNumeric `__ -and `Legate -boost `__ for more +Please refer to `Distributed Computing with cuPyNumeric`_ +and `Legate boost`_ for more information and detailed instructions on installation. -**Quick installation and setup** --------------------------------- - -+--------------------------------------------------------------------------------------------+ -| # create a new env and install legate boost and dependencies | -| | -| $ conda create -n legate-boost -c legate -c conda-forge -c nvidia legate-boost | -| | -| # activate env | -| | -| $ conda activate legate-boost | -| | -| # install wrappers | -| | -| | $ conda install -c legate/label/gex-experimental realm-gex-wrapper legate-mpi-wrapper | -| | # install cmake | -| | -| $ conda install -c conda-forge cmake>=3.26.4 | -| | -| # build wrappers | -| | -| $ | -| /global/homes/**n/ngraddon**/.conda/envs/legate-boost-new/mpi-wrapper/build-mpi-wrapper.sh | -| | -| $ | -| /global/homes/**n/ngraddon**/.conda/envs/legate-boost-new/gex-wrapper/build-gex-wrapper.sh | -| | -| # reactivate env | -| | -| $ conda activate legate-boost | -| | -| # install legate-dataframe | -| | -| $ conda install -c legate -c rapidsai -c conda-forge legate-dataframe | -+============================================================================================+ - -**Usage** ---------- +.. _Distributed Computing with cuPyNumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cuPyNumeric.ipynb + +.. _Legate boost: https://github.com/rapidsai/legate-boost/tree/main + + +Quick installation and setup +---------------------------- + +.. code-block:: sh + + # create a new env and install legate boost and dependencies + conda create -n legate-boost -c legate -c conda-forge -c nvidia legate-boost + + # activate env + conda activate legate-boost + + # install wrappers + conda install -c legate/label/gex-experimental realm-gex-wrapper legate-mpi-wrapper + + # install cmake + conda install -c conda-forge cmake>=3.26.4 + + # build wrappers + /global/homes/n/ngraddon/.conda/envs/legate-boost-new/mpi-wrapper/build-mpi-wrapper.sh + /global/homes/n/ngraddon/.conda/envs/legate-boost-new/gex-wrapper/build-gex-wrapper.sh + + # reactivate env + conda activate legate-boost + + # install legate-dataframe + conda install -c legate -c rapidsai -c conda-forge legate-dataframe + + +Usage +===== Legate Boost offers two main estimator types: - LBRegressor for regression tasks - - LBClassifier for classification tasks These estimators follow a similar interface to those in **XGboost**, making them easy to integrate into existing machine learning pipelines. -**Regression with LBRegressor** -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Regression with LBRegressor +--------------------------- The LBRegressor estimator is used to predict continuous values such as house prices, temperature, or sales forecasting. The following code @@ -87,42 +88,36 @@ demonstrates how to create an instance of the LBRegressor model, use the fit() function to train it on a dataset, and then apply the predict() function to generate predictions on new data. Here’s how to set it up: -import legateboost as lb - -from sklearn.datasets import make_regression -#creating synthetic dataset +.. code-block:: python -x,y = make_regression(n_samples=100, n_features=4, noise=8, -random_state=42) + import legateboost as lb + from sklearn.datasets import make_regression + from sklearn.model_selection import train_test_split -#splitting the data into training and testing sets + # creating synthetic dataset + X, y = make_regression(n_samples=100, n_features=4, noise=8, random_state=42) -X_train, X_test, y_train, y_test = train_test_split( + # splitting the data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) -data.data, data.target, test_size=0.2, random_state=42) + # regression model with 100 estimators + regression_model = lb.LBRegressor(n_estimators=100) -#regression model with 100 estimators + # fit the model + regression_model.fit(X_train, y_train) -regresion_model = lb.LBRegressor(n_estimators=100) + # predict + y_pred = regression_model.predict(X_test) -#fit the model -regression_model.fit(X_train, y_train) - -#predict using the trained model - -y_pred = regression_model.predict(X_test) - - In this example: +~~~~~~~~~~~~~~~~ - LBRegressor is initialized with 100 boosting estimators. - - The fit() method trains the model using the input features (X_train) and target values (y_train). - - After training, the predict() method is used to make predictions on the test set (X_test). @@ -130,11 +125,12 @@ This represents a typical workflow for applying a regression model using Legate Boost. The LBRegressor estimator offers several configurable options, such as base_model and learning_rate, to help optimize model performance. For a comprehensive list of features and parameters, refer -to the official -`documentation `__. +to the `official documentation`_. + +.. _official documentation: https://rapidsai.github.io/legate-boost/api/estimators.html -**Classification with LBClassifier** -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Classification with LBClassifier +--------------------------------- The LBClassifier is designed for predicting categorical outcomes and supports both binary and multi-class classification tasks. It is ideal @@ -144,36 +140,26 @@ classification, and sentiment analysis. The example below demonstrates how to implement a classification model using the LBClassifier estimator from Legate Boost: -import legateboost as lb - -from sklearn.datasets import make_regression - -from sklearn.model_selection import train_test_split - -#creating synthetic dataset +.. code-block:: python -x,y = make_regression(n_samples=100, n_features=4, n_informative=3, -n_redundant=0, n_classes=2, random_state=42) + import legateboost as lb + from sklearn.datasets import make_classification + from sklearn.model_selection import train_test_split -#splitting the data into training and testing sets + # creating synthetic dataset + X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42) -X_train, X_test, y_train, y_test = train_test_split( + # splitting the data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) -data.data, data.target, test_size=0.2, random_state=42) + # classification model with 50 estimators + classification_model = lb.LBClassifier(n_estimators=50) -# Create a classification model with 50 boosting estimators + classification_model.fit(X_train, y_train) + y_pred = classification_model.predict(X_test) -classification_model = lb.LBClassifier(n_estimators=50) - -# Train the model on labeled data - -classification_model.fit(X_train, y_train) - -# Predict class labels on new data - -y_pred = classification_model.predict(X_test) - -In this example: +In this example: +~~~~~~~~~~~~~~~~ - LBClassifier(n_estimators=50) sets up a classifier with 50 boosting rounds. @@ -185,11 +171,12 @@ y_pred = classification_model.predict(X_test) Just like the regressor, the LBClassifier follows a clean and intuitive workflow. It provides additional options and advanced configurations to optimize model performance. For more detailed information, refer to the -Legate Boost estimators -`documentation `__. +Legate Boost `estimators`_ documentation. -**Example 1** -------------- +.. _estimators: https://rapidsai.github.io/legate-boost/api/estimators.html#legateboost.LBClassifier + +Example 1 +========= Here is an example of using Legate Boost to build a regression model on the California housing dataset. @@ -197,8 +184,8 @@ the California housing dataset. It showcases key features like scalable training across GPUs/nodes, customizable base models, and adjustable learning rates. -**About dataset** -~~~~~~~~~~~~~~~~~~ +About dataset +------------- The California housing dataset is a classic benchmark dataset containing information collected from California districts in the 1990 census. Each @@ -219,8 +206,8 @@ The target variable is the **median house value** in that block group. This dataset is often used to illustrate regression techniques and assess predictive performance on real-world tabular data. -**About this implementation** -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +About this implementation +------------------------- The following code creates a Legate Boost regression model using LBRegressor, which trains a gradient boosting model optimized for @@ -233,68 +220,39 @@ each tree contributes to the final prediction, balancing speed and stability. The verbose=True flag enables logging during training, allowing to monitor progress and internal operations. -**Code module** -~~~~~~~~~~~~~~~~ - -import cupynumeric as cn - -import legateboost as lb - -from legate.timing import time - -from sklearn.metrics import mean_squared_error - -from sklearn.model_selection import train_test_split - -from sklearn.datasets import fetch_california_housing - -#load and split data - -data = fetch_california_housing() - -X_train, X_test, y_train, y_test = train_test_split( - -data.data, data.target, test_size=0.2, random_state=42) - -#create Legate Boost regressor - -model = lb.LBRegressor(n_estimators=100, -base_models=(lb.models.Tree(max_depth=5),), - -objective = **"squared_error"**, learning_rate = 0.1, verbose=\ **True** - -) - -#training - -start = time() +Code module +----------- -model.fit(X_train, y_train) +.. code-block:: python -end = time() + import cupynumeric as cn + import legateboost as lb + from legate.timing import time + from sklearn.metrics import mean_squared_error + from sklearn.model_selection import train_test_split + from sklearn.datasets import fetch_california_housing -#prediction + data = fetch_california_housing() + X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42) -istart = time() + model = lb.LBRegressor( + n_estimators=100, + base_models=(lb.models.Tree(max_depth=5),), + objective="squared_error", + learning_rate=0.1, + verbose=True + ) -y_pred = model.predict(X_test) + start = time() + model.fit(X_train, y_train) + end = time() -iend = time() + y_pred = model.predict(X_test) -#evaluate + mse = mean_squared_error(y_test, y_pred) + print(f"Test MSE: {mse:.4f}") + print(f"Training time: {(end - start)/1000:.6f} ms") -mse = mean_squared_error(y_test, y_pred) - -print(f"Test MSE: {mse:.4f}") - -print(f"\\n The training time for housing exp is: {(end - -start)/1000:.6f}ms") - -print(f"\\n The inference time for housing exp is {(iend - -istart)/1000:.6f}") - - -- This simple example demonstrates how to train a regression model on the California Housing dataset using Legate Boost. Although the code looks @@ -315,37 +273,39 @@ histograms from multiple GPUs), it is handled by legate-mpi-wrapper and realm-gex-wrapper, so we never have to write MPI or manage explicit GPU memory transfers. -**Running on CPU and GPU** -~~~~~~~~~~~~~~~~~~~~~~~~~~ +Running on CPU and GPU +---------------------- -**CPU** - To run with CPU, use the following command. +CPU - To run with CPU, use the following command. -+-----------------------------------------------------------------------+ -| **legate --cpus 1 --gpus 0 ./housing.py** | -+=======================================================================+ -Output: +.. code-block:: sh -+-----------------------------------------------------------------------+ -| **The training time for housing exp is: 7846.303000 milliseconds** | -+=======================================================================+ + legate --cpus 1 --gpus 0 ./housing.py -**GPU** - To run with GPU, use the following command. +This produces the following output: -+-----------------------------------------------------------------------+ -| **legate --gpus 2 ./housing.py** | -+=======================================================================+ +.. code-block:: text -Output: + The training time for housing exp is: 7846.303000 milliseconds + + +GPU - To run with GPU, use the following command. + +.. code-block:: sh -+-----------------------------------------------------------------------+ -| **The training time for housing exp is : 846.949000 milliseconds** | -+=======================================================================+ + legate --gpus 2 ./housing.py + +This produces the following output: + +.. code-block:: text + + The training time for housing exp is: 846.949000 milliseconds **To Do: Multi Node and Multi GPU** -**Example 2** -------------- +Example 2 +========= This example demonstrates how Legate Boost can be applied to the *“Give Me Some Credit”* dataset (OpenML data_id: 46929) to build a @@ -355,8 +315,8 @@ Legate Boost to enable distributed training across multi-GPU and multi-node environments, showcasing scalable machine learning on the Credit Score dataset. -**About the dataset** -~~~~~~~~~~~~~~~~~~~~~ +About the dataset +----------------- The Give Me Some Credit dataset is a financial risk prediction dataset originally introduced in a Kaggle competition. It includes anonymized @@ -367,34 +327,25 @@ within the next two years. Each record represents an individual and includes features such as: - Revolving utilization of unsecured credit lines - - Age - - Number of late payments (30–59, 60–89, and 90+ days past due) - - Debt ratio - - Monthly income - - Number of open credit lines and loans - -- Number of dependents\ ** - ** +- Number of dependents The target variable is binary (0 = no distress, 1 = distress), indicating the likelihood of future financial trouble. -.. _about-this-implementation-1: -**About this implementation** -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +About this implementation +------------------------- This implementation will focus on demonstrating Legate Boost’s flexible model ensembling capabilities, specifically: - Tree-based gradient boosting models, ideal for structured/tabular data. - - Neural network-based classifiers, allowing hybrid or deep learning approaches. @@ -412,60 +363,36 @@ DataFrame. LogicalTables internally break data into logical columns, enabling Legate’s runtime to partition, distribute, and schedule computations across multiple GPUs and nodes. - - -import cudf - -import pandas - -import cupy as cp - -import pyarrow as pa - -import legate_dataframe - -import legateboost as lb - -import cupynumeric as cpn - -from legate.timing import time - -from sklearn.datasets import fetch_openml - -from sklearn.metrics import accuracy_score - -from sklearn.model_selection import train_test_split - -from legate_dataframe.lib.replace import replace_nulls - -from legate_dataframe.lib.core.table import LogicalTable - -from legate_dataframe.lib.core.column import LogicalColumn - -#import data - -data = fetch_openml(data_id= 46929, as_frame=True) -xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas # based on -CPU or GPU - -df = xd.DataFrame(data.data, columns=data.feature_names) - -df['Target'] = data.target - -#covert to logicalTable - -if cp.cuda.runtime.getDeviceCount() > 0: - -ldf = LogicalTable.from_cudf(df) - -else: - -df = pa.Table.from_pandas(df) - -ldf = LogicalTable.from_arrow(df) - - +.. code-block:: python + + import cudf + import pandas + import cupy as cp + import pyarrow as pa + import legate_dataframe + import legateboost as lb + import cupynumeric as cpn + from legate.timing import time + from sklearn.datasets import fetch_openml + from sklearn.metrics import accuracy_score + from legate_dataframe.lib.replace import replace_nulls + from legate_dataframe.lib.core.table import LogicalTable + from legate_dataframe.lib.core.column import LogicalColumn + + # load dataset + data = fetch_openml(data_id=46929, as_frame=True) + + xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas + df = xd.DataFrame(data.data, columns=data.feature_names) + df['Target'] = data.target + + # convert to LogicalTable + if cp.cuda.runtime.getDeviceCount() > 0: + ldf = LogicalTable.from_cudf(df) + else: + df = pa.Table.from_pandas(df) + ldf = LogicalTable.from_arrow(df) Let’s see how data preprocessing is performed directly on the LogicalTable. Missing values in key columns (MonthlyIncome and @@ -479,30 +406,27 @@ GPU-accelerated array type that leverages logical partitioning for distributed computation. This enables the subsequent machine learning tasks to execute efficiently across multiple GPUs or nodes. -#replace nulls +.. code-block:: python -median_salary = df["MonthlyIncome"].median() + # median imputation + median_salary = df["MonthlyIncome"].median() + median_dependents = df["NumberOfDependents"].median() -median_dependents = df["NumberOfDependents"].median() + mni = LogicalColumn( + replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary) + ) + mnd = LogicalColumn( + replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) + ) -mni = -LogicalColumn(replace_nulls(LogicalColumn(ldf["MonthlyIncome"]),median_salary)) + # rebuild logical table + features = ldf.get_column_names() + nldf = LogicalTable( + [ldf[0], ldf[1], ldf[2], ldf[3], mni, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], + features ) -mnd = -LogicalColumn(replace_nulls(LogicalColumn(ldf["NumberOfDependents"]),median_dependents)) - -#create a new logical Table with updated columns - -features = ldf.get_column_names() - -nldf = LogicalTable( [ ldf[0], ldf[1], ldf[2], ldf[3], mmi, ldf[5], -ldf[6], ldf[7], ldf[8], mnd, ldf[10]], features) - -#coverting LogicalTable to cupyNumeric array - -data_arr = nldf.to_array() - - + # convert to cuPyNumeric + data_arr = nldf.to_array() As we have a data_arr backed by cuPyNumeric, we first split the dataset into training and testing subsets, which are then passed to Legate Boost @@ -524,72 +448,56 @@ high-dimensional relationships. This ensemble design results in a more accurate and robust classifier than either model could achieve individually. -#preparing data for training and testing - -x = data_arr[:, :-1] - -y = data_arr[:, -1] - -num_samples = x.shape[0] +.. code-block:: python -split_ratio = 0.8 + #preparing data for training and testing + x = data_arr[:, :-1] + y = data_arr[:, -1] -split_index = int(num_samples \* split_ratio) + split_index = int(x.shape[0] * 0.8) + x_train, y_train = x[:split_index], y[:split_index] + x_test, y_test = x[split_index:], y[split_index:] -x_train = x[:split_index] + start = time() -y_train = y[:split_index] + # ensemble model + model = lb.LBClassifier( + base_models=( + lb.models.Tree(max_depth=8), + lb.models.NN(max_iter=10, hidden_layer_sizes=(10, 10), verbose=True), + ) + ) + model.fit(x_train, y_train) -x_test = x[split_index:] + end = time() -y_test = y[split_index:] - -start=time() - -#create model and trian it - -model = -lb.LBClassifier(base_models=(lb.models.Tree(max_depth=8),lb.models.NN(max_iter=10,hidden_layer_sizes=(10,10),verbose=True))) - -model.fit(x_train,y_train) - -end=time() - -The trained ensemble model is used to generate predictions on the test +The trained ensemble model is used to generate predictions on the test set, and its accuracy is evaluated using accuracy_score. Finally, the model is saved with Joblib for future inference without retraining. -#predict - -predictions = model.predict(x_test) - -#evalution - -acc = accuracy_score(y_test, predictions) - -print("Accuracy:", acc) +.. code-block:: python -print(f"\\n The training time for creditscore exp is: {(end - -start)/1000:.6f}ms") + # predict + predictions = model.predict(x_test) -#model save + # evaluate + from sklearn.metrics import accuracy_score + acc = accuracy_score(y_test, predictions) + print("Accuracy:", acc) + print(f"Training time: {(end - start)/1000:.6f} ms") -dump(model, "legate_boost_model.joblib") + # save model + from joblib import dump + dump(model, "legate_boost_model.joblib") -#save the test sets to CSV for easy inference performance calculation + # save test data for inference + import numpy as np, pandas as pd + x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) + y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) -x_test_cpu = x_test.get() if hasattr(x_test, "get") else -np.array(x_test) + pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) + pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) -y_test_cpu = y_test.get() if hasattr(y_test, "get") else -np.array(y_test) - -pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) - -pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", -index=False) - - This workflow illustrates how Legate DataFrame provides a scalable preprocessing layer, cupynumeric arrays enable distributed GPU @@ -597,45 +505,42 @@ computation, and Legate Boost delivers a flexible ensemble learning framework capable of leveraging multi-node, multi-GPU infrastructure efficiently. -.. _running-on-cpu-and-gpu-1: -**Running on CPU and GPU** -~~~~~~~~~~~~~~~~~~~~~~~~~~ +Running on CPU and GPU +---------------------- -**CPU** - To run with CPU, use the following command. -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +CPU - To run with CPU, use the following command. +^^^ -+-----------------------------------------------------------------------+ -| **legate --cpus 1 --gpus 0 ./creditscore.py** | -+=======================================================================+ +.. code-block:: sh + + legate --cpus 1 --gpus 0 ./creditscore.py Output: -+-----------------------------------------------------------------------+ -| **Accuracy: 0.9343** | -| | -| **The training time for creditscore exp is: 45337.714000 ms** | -+=======================================================================+ +:: + + Accuracy: 0.9343 + The training time for credit score exp is : 45337.714000 ms -**GPU** - To run with GPU, use the following command. -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +GPU - To run with GPU, use the following command. +^^^^^^^ -+-----------------------------------------------------------------------+ -| **legate --gpus 2 ./creditscore.py** | -+=======================================================================+ +.. code-block:: sh + + legate --gpus 2 ./creditscore.py Output: -+-----------------------------------------------------------------------+ -| **Accuracy: 0.9353** | -| | -| **The training time for creditscore exp is: 2688.233000 ms** | -+=======================================================================+ +:: + + Accuracy: 0.9353 + The training time for credit score exp is : 2688.233000 ms **To Do: Multi Node and Multi GPU** -**Inference performance** -------------------------- +Inference performance +===================== Let’s explore how cuPyNumeric can be leveraged to measure inference performance statistics seamlessly across both CPU and GPU all without @@ -649,127 +554,86 @@ GPU environments. This approach provides a simple yet powerful way to compare inference performance across hardware, offering clear insights into the speedup and variability achieved with GPU acceleration. -import cupynumeric as cp - -from joblib import load +.. code-block:: python -from legate.timing import time + import cupynumeric as cp + from joblib import load + from legate.timing import time + import pandas as pd + import legate.core as lg -import pandas as pd + timings = [] -import legate.core as lg + # load model and test data + model = load("legate_boost_model.joblib") + X = pd.read_csv("x_test.csv") -timings = [] + rt = lg.get_legate_runtime() -#load pre-trained model + for _ in range(10): + rt.issue_execution_fence() + start = time() + model.predict(X) + rt.issue_execution_fence() + end = time() + timings.append(end - start) -model = load("legate_boost_model.joblib") + timings = timings[1:] # ignore first run + timings_gpu = cp.array(timings) -X = pd.read_csv("x_test.csv") + mean_time = cp.mean(timings_gpu) + median_time = cp.median(timings_gpu) + min_time = cp.min(timings_gpu) + max_time = cp.max(timings_gpu) + var_time = cp.var(timings_gpu) + std = cp.sqrt(var_time) -rt = lg.get_legate_runtime() + print(f"Mean: {float(mean_time)/1000:.2f} ms") + print(f"Median: {float(median_time)/1000:.2f} ms") + print(f"Min: {float(min_time)/1000:.2f} ms") + print(f"Max: {float(max_time)/1000:.2f} ms") + print(f"Variance: {float(var_time)/1000:.2f} ms") + print(f"Standard deviation: {float(std)/1000:.2f} ms") -for \_ in range(10): -rt.issue_execution_fence() +Running on CPU and GPU +---------------------- -start = time() +CPU - To run with CPU, use the following command. -model.predict(X) +.. code-block:: sh -rt.issue_execution_fence() + legate --cpus 1 --gpus 0 ./inference.py -end = time() - -timings.append(end - start) - -#first iteration is not considered here because, it includes env setup -time for gpu usage - -timings = timings[1:] - -timings_gpu = cp.array(timings) - -mean_time = cp.mean(timings_gpu) - -median_time = cp.median(timings_gpu) - -min_time = cp.min(timings_gpu) - -max_time = cp.max(timings_gpu) - -var_time = cp.var(timings_gpu) - -std = cp.sqrt(var_time) - -print(f"Mean: {float(mean_time)/1000:.2f} ms") - -print(f"Median: {float(median_time)/1000:.2f} ms") - -print(f"Min: {float(min_time)/1000:.2f} ms") - -print(f"Max: {float(max_time)/1000:.2f} ms") - -print(f"Variance: {float(var_time)/1000:.2f} ms") +Output: -print(f"standard deviation: {float(std)/1000:.2f} ms") +.. code-block:: text - + Mean: 265.66 ms + Median: 262.97 ms + Min: 249.78 ms + Max: 284.44 ms + Variance: 117319.15 ms + Standard deviation: 10.83 ms -.. _running-on-cpu-and-gpu-2: -**Running on CPU and GPU** -~~~~~~~~~~~~~~~~~~~~~~~~~~ +GPU - To run with GPU, use the following command. -.. _cpu---to-run-with-cpu-use-the-following-command.-1: -**CPU** - To run with CPU, use the following command. -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. code-block:: sh -+-----------------------------------------------------------------------+ -| **legate --cpus 1 --gpus 0 ./inference.py** | -+=======================================================================+ + legate --gpus 1 ./inference.py Output: -+-----------------------------------------------------------------------+ -| **Mean: 265.66 ms** | -| | -| **Median: 262.97 ms** | -| | -| **Min: 249.78 ms** | -| | -| **Max: 284.44 ms** | -| | -| **Variance: 117319.15 ms** | -| | -| **standard deviation: 10.83 ms** | -+=======================================================================+ - -.. _gpu---to-run-with-gpu-use-the-following-command.-1: - -**GPU** - To run with GPU, use the following command. -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -+-----------------------------------------------------------------------+ -| **legate --gpus 1 ./inference.py** | -+=======================================================================+ - -Output: +.. code-block:: text -+-----------------------------------------------------------------------+ -| **Mean: 122.35 ms** | -| | -| **Median: 122.11 ms** | -| | -| **Min: 121.28 ms** | -| | -| **Max: 125.97 ms** | -| | -| **Variance: 1793.76 ms** | -| | -| **standard deviation: 1.34 ms** | -+=======================================================================+ + Mean: 122.35 ms + Median: 122.11 ms + Min: 121.28 ms + Max: 125.97 ms + Variance: 1793.76 ms + Standard deviation: 1.34 ms These results clearly show the performance benefits of running inference on a GPU compared to a CPU using cuPyNumeric arrays. On the CPU, the From 37cbb2524eb05989aa181443d7ad00b5ae1e5a7f Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 17:18:14 -0400 Subject: [PATCH 03/72] Add files via upload --- docs/source/Legate_Boost2.rst | 785 ++++++++++++++++++++++++++++++++++ 1 file changed, 785 insertions(+) create mode 100644 docs/source/Legate_Boost2.rst diff --git a/docs/source/Legate_Boost2.rst b/docs/source/Legate_Boost2.rst new file mode 100644 index 00000000..618ac7dc --- /dev/null +++ b/docs/source/Legate_Boost2.rst @@ -0,0 +1,785 @@ +This article assumes familiarity with the basic usage of gradient +boosting libraries such as XGBoost or LightGBM, as well as cuPyNumeric +for GPU-accelerated array computations. + +**What is legate boost?** +------------------------- + +In scenarios where high-performance training is needed across large +datasets or distributed hardware, Legate Boost offers a scalable +alternative. Legate Boost is an advanced gradient boosting library built +on the Legate and Legion parallel programming frameworks. Unlike +traditional boosting libraries such as XGBoost or LightGBM, Legate Boost +provides a unified infrastructure that seamlessly scales across CPUs and +GPUs, supporting both single-node and distributed training while +integrating naturally with cuPyNumeric workflows for efficient +end-to-end data processing. It enables users to define not only +conventional boosted decision trees but also hybrid ensembles combining +trees, kernel ridge regression, linear models, or neural networks, all +written in Python with minimal code changes. + +These models are automatically parallelized and executed efficiently +without manual data movement or partitioning. Legate Boost emphasizes +architectural simplicity, extensibility, and performance, delivering +state-of-the-art results on tabular data while leveraging the full +computational power of modern heterogeneous hardware. + +Please refer to `Distributed computing +cuPyNumeric `__ +and `Legate +boost `__ for more +information and detailed instructions on installation. + +**Quick installation and setup** +-------------------------------- + ++--------------------------------------------------------------------------------------------+ +| # create a new env and install legate boost and dependencies | +| | +| $ conda create -n legate-boost -c legate -c conda-forge -c nvidia legate-boost | +| | +| # activate env | +| | +| $ conda activate legate-boost | +| | +| # install wrappers | +| | +| | $ conda install -c legate/label/gex-experimental realm-gex-wrapper legate-mpi-wrapper | +| | # install cmake | +| | +| $ conda install -c conda-forge cmake>=3.26.4 | +| | +| # build wrappers | +| | +| $ | +| /global/homes/**n/ngraddon**/.conda/envs/legate-boost-new/mpi-wrapper/build-mpi-wrapper.sh | +| | +| $ | +| /global/homes/**n/ngraddon**/.conda/envs/legate-boost-new/gex-wrapper/build-gex-wrapper.sh | +| | +| # reactivate env | +| | +| $ conda activate legate-boost | +| | +| # install legate-dataframe | +| | +| $ conda install -c legate -c rapidsai -c conda-forge legate-dataframe | ++============================================================================================+ + +**Usage** +--------- + +Legate Boost offers two main estimator types: + +- LBRegressor for regression tasks + +- LBClassifier for classification tasks + +These estimators follow a similar interface to those in **XGboost**, +making them easy to integrate into existing machine learning pipelines. + +**Regression with LBRegressor** +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The LBRegressor estimator is used to predict continuous values such as +house prices, temperature, or sales forecasting. The following code +demonstrates how to create an instance of the LBRegressor model, use the +fit() function to train it on a dataset, and then apply the predict() +function to generate predictions on new data. Here’s how to set it up: + +import legateboost as lb + +from sklearn.datasets import make_regression + +#creating synthetic dataset + +x,y = make_regression(n_samples=100, n_features=4, noise=8, +random_state=42) + +#splitting the data into training and testing sets + +X_train, X_test, y_train, y_test = train_test_split( + +data.data, data.target, test_size=0.2, random_state=42) + +#regression model with 100 estimators + +regresion_model = lb.LBRegressor(n_estimators=100) + +#fit the model + +regression_model.fit(X_train, y_train) + +#predict using the trained model + +y_pred = regression_model.predict(X_test) + + + +In this example: + +- LBRegressor is initialized with 100 boosting estimators. + +- The fit() method trains the model using the input features (X_train) + and target values (y_train). + +- After training, the predict() method is used to make predictions on + the test set (X_test). + +This represents a typical workflow for applying a regression model using +Legate Boost. The LBRegressor estimator offers several configurable +options, such as base_model and learning_rate, to help optimize model +performance. For a comprehensive list of features and parameters, refer +to the official +`documentation `__. + +**Classification with LBClassifier** +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The LBClassifier is designed for predicting categorical outcomes and +supports both binary and multi-class classification tasks. It is ideal +for a wide range of applications, including spam detection, image +classification, and sentiment analysis. + +The example below demonstrates how to implement a classification model +using the LBClassifier estimator from Legate Boost: + +import legateboost as lb + +from sklearn.datasets import make_regression + +from sklearn.model_selection import train_test_split + +#creating synthetic dataset + +x,y = make_regression(n_samples=100, n_features=4, n_informative=3, +n_redundant=0, n_classes=2, random_state=42) + +#splitting the data into training and testing sets + +X_train, X_test, y_train, y_test = train_test_split( + +data.data, data.target, test_size=0.2, random_state=42) + +# Create a classification model with 50 boosting estimators + +classification_model = lb.LBClassifier(n_estimators=50) + +# Train the model on labeled data + +classification_model.fit(X_train, y_train) + +# Predict class labels on new data + +y_pred = classification_model.predict(X_test) + +In this example: + +- LBClassifier(n_estimators=50) sets up a classifier with 50 boosting + rounds. + +- fit(X_train, y_train) learns the patterns from your training dataset. + +- predict(X_test) outputs predicted class labels for the test dataset. + +Just like the regressor, the LBClassifier follows a clean and intuitive +workflow. It provides additional options and advanced configurations to +optimize model performance. For more detailed information, refer to the +Legate Boost estimators +`documentation `__. + +**Example 1** +------------- + +Here is an example of using Legate Boost to build a regression model on +the California housing dataset. + +It showcases key features like scalable training across GPUs/nodes, +customizable base models, and adjustable learning rates. + +**About dataset** +~~~~~~~~~~~~~~~~~~ + +The California housing dataset is a classic benchmark dataset containing +information collected from California districts in the 1990 census. Each +record describes a block group (a neighborhood-level area), including +predictors such as: + +- Median income of residents + +- Average house age + +- Average number of rooms and bedrooms + +- Population and household count + +- Latitude and longitude + +The target variable is the **median house value** in that block group. +This dataset is often used to illustrate regression techniques and +assess predictive performance on real-world tabular data. + +**About this implementation** +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The following code creates a Legate Boost regression model using +LBRegressor, which trains a gradient boosting model optimized for +multi-GPU and multi-node environments. The model is configured to use +100 boosting rounds (n_estimators=100), with each round adding a +decision tree (lb.models.Tree) limited to a maximum depth of 5. The loss +function is set to "squared_error", suitable for regression tasks as it +minimizes mean squared error. A learning_rate of 0.1 controls how much +each tree contributes to the final prediction, balancing speed and +stability. The verbose=True flag enables logging during training, +allowing to monitor progress and internal operations. + +**Code module** +~~~~~~~~~~~~~~~~ + +import cupynumeric as cn + +import legateboost as lb + +from legate.timing import time + +from sklearn.metrics import mean_squared_error + +from sklearn.model_selection import train_test_split + +from sklearn.datasets import fetch_california_housing + +#load and split data + +data = fetch_california_housing() + +X_train, X_test, y_train, y_test = train_test_split( + +data.data, data.target, test_size=0.2, random_state=42) + +#create Legate Boost regressor + +model = lb.LBRegressor(n_estimators=100, +base_models=(lb.models.Tree(max_depth=5),), + +objective = **"squared_error"**, learning_rate = 0.1, verbose=\ **True** + +) + +#training + +start = time() + +model.fit(X_train, y_train) + +end = time() + +#prediction + +istart = time() + +y_pred = model.predict(X_test) + +iend = time() + +#evaluate + +mse = mean_squared_error(y_test, y_pred) + +print(f"Test MSE: {mse:.4f}") + +print(f"\\n The training time for housing exp is: {(end - +start)/1000:.6f}ms") + +print(f"\\n The inference time for housing exp is {(iend - +istart)/1000:.6f}") + + +- + +This simple example demonstrates how to train a regression model on the +California Housing dataset using Legate Boost. Although the code looks +similar to standard XGBoost, Legate Boost automatically enables +multi-GPU and multi-node computation. Legate Boost achieves multi-GPU +and multi-node scaling through its integration with cupynumeric and the +Legion runtime. Unlike traditional GPU libraries that allocate data to a +single device, cupynumeric creates Logical Arrays and abstract +representations of the data that are not bound to one GPU. The Legate +automatically partitions these logical arrays into physical chunks and +maps them across all available GPUs and nodes. + +During training, operations such as histogram building, gradient +computation, and tree construction are expressed as parallel tasks. +Legate schedules these tasks close to where the data resides, minimizing +communication overhead. When synchronization is needed (e.g., to combine +histograms from multiple GPUs), it is handled by legate-mpi-wrapper and +realm-gex-wrapper, so we never have to write MPI or manage explicit GPU +memory transfers. + +**Running on CPU and GPU** +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +**CPU** - To run with CPU, use the following command. + ++-----------------------------------------------------------------------+ +| **legate --cpus 1 --gpus 0 ./housing.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **The training time for housing exp is: 7846.303000 milliseconds** | ++=======================================================================+ + +**GPU** - To run with GPU, use the following command. + ++-----------------------------------------------------------------------+ +| **legate --gpus 2 ./housing.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **The training time for housing exp is : 846.949000 milliseconds** | ++=======================================================================+ + +**To Do: Multi Node and Multi GPU** + +**Example 2** +------------- + +This example demonstrates how Legate Boost can be applied to the *“Give +Me Some Credit”* dataset (OpenML data_id: 46929) to build a +classification model using ensemble learning by combining different +model types. It also highlights the integration of Legate DataFrame with +Legate Boost to enable distributed training across multi-GPU and +multi-node environments, showcasing scalable machine learning on the +Credit Score dataset. + +**About the dataset** +~~~~~~~~~~~~~~~~~~~~~ + +The Give Me Some Credit dataset is a financial risk prediction dataset +originally introduced in a Kaggle competition. It includes anonymized +credit and demographic data for individuals, with the goal of predicting +whether a person is likely to experience serious financial distress +within the next two years. + +Each record represents an individual and includes features such as: + +- Revolving utilization of unsecured credit lines + +- Age + +- Number of late payments (30–59, 60–89, and 90+ days past due) + +- Debt ratio + +- Monthly income + +- Number of open credit lines and loans + +- Number of dependents\ ** + ** + +The target variable is binary (0 = no distress, 1 = distress), +indicating the likelihood of future financial trouble. + +.. _about-this-implementation-1: + +**About this implementation** +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This implementation will focus on demonstrating Legate Boost’s flexible +model ensembling capabilities, specifically: + +- Tree-based gradient boosting models, ideal for structured/tabular + data. + +- Neural network-based classifiers, allowing hybrid or deep learning + approaches. + +By leveraging Legate Boost, we can ensemble these two models and +efficiently train and evaluate both model types on GPUs or CPUs, +showcasing scalable performance for large tabular datasets in financial +risk prediction. + +The pipeline begins with importing required libraries and its functions +and also loading the dataset using fetch_openml. Depending on hardware +availability, the data is initially handled either with cuDF (for GPU +execution) or pandas (for CPU execution). The dataset is then wrapped +into a LogicalTable, the distributed data representation used by Legate +DataFrame. LogicalTables internally break data into logical columns, +enabling Legate’s runtime to partition, distribute, and schedule +computations across multiple GPUs and nodes. + + + +import cudf + +import pandas + +import cupy as cp + +import pyarrow as pa + +import legate_dataframe + +import legateboost as lb + +import cupynumeric as cpn + +from legate.timing import time + +from sklearn.datasets import fetch_openml + +from sklearn.metrics import accuracy_score + +from sklearn.model_selection import train_test_split + +from legate_dataframe.lib.replace import replace_nulls + +from legate_dataframe.lib.core.table import LogicalTable + +from legate_dataframe.lib.core.column import LogicalColumn + +#import data + +data = fetch_openml(data_id= 46929, as_frame=True) + +xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas # based on +CPU or GPU + +df = xd.DataFrame(data.data, columns=data.feature_names) + +df['Target'] = data.target + +#covert to logicalTable + +if cp.cuda.runtime.getDeviceCount() > 0: + +ldf = LogicalTable.from_cudf(df) + +else: + +df = pa.Table.from_pandas(df) + +ldf = LogicalTable.from_arrow(df) + + + +Let’s see how data preprocessing is performed directly on the +LogicalTable. Missing values in key columns (MonthlyIncome and +NumberOfDependents) are filled using median imputation through the +replace_nulls operation. These operations are executed in parallel +across distributed partitions of the LogicalTable, avoiding centralized +bottlenecks. Because LogicalTables are immutable, a new LogicalTable +with updated LogicalColumn’s is created after preprocessing. The cleaned +data is then converted into a cuPyNumeric array, Legate’s +GPU-accelerated array type that leverages logical partitioning for +distributed computation. This enables the subsequent machine learning +tasks to execute efficiently across multiple GPUs or nodes. + +#replace nulls + +median_salary = df["MonthlyIncome"].median() + +median_dependents = df["NumberOfDependents"].median() + +mni = +LogicalColumn(replace_nulls(LogicalColumn(ldf["MonthlyIncome"]),median_salary)) + +mnd = +LogicalColumn(replace_nulls(LogicalColumn(ldf["NumberOfDependents"]),median_dependents)) + +#create a new logical Table with updated columns + +features = ldf.get_column_names() + +nldf = LogicalTable( [ ldf[0], ldf[1], ldf[2], ldf[3], mmi, ldf[5], +ldf[6], ldf[7], ldf[8], mnd, ldf[10]], features) + +#coverting LogicalTable to cupyNumeric array + +data_arr = nldf.to_array() + + + +As we have a data_arr backed by cuPyNumeric, we first split the dataset +into training and testing subsets, which are then passed to Legate Boost +for efficient training across available hardware resources. The model is +built using Legate Boost’s ensemble framework (LBClassifier), which +allows combining multiple types of base learners into a single unified +model. + +In this example, the ensemble consists of a Decision Tree +(lb.models.Tree) with max_depth=8, enabling the capture of complex +non-linear decision boundaries by splitting the feature space +hierarchically up to 8 levels deep, and a Neural Network (lb.models.NN) +with two hidden layers of 10 neurons each (hidden_layer_sizes=(10,10)), +trained for max_iter=10 epochs with verbose=True to monitor progress. By +combining a tree-based model with a neural network, Legate Boost +leverages the interpretability and rule-based decision-making of trees +together with the ability of neural networks to model intricate, +high-dimensional relationships. This ensemble design results in a more +accurate and robust classifier than either model could achieve +individually. + +#preparing data for training and testing + +x = data_arr[:, :-1] + +y = data_arr[:, -1] + +num_samples = x.shape[0] + +split_ratio = 0.8 + +split_index = int(num_samples \* split_ratio) + +x_train = x[:split_index] + +y_train = y[:split_index] + +x_test = x[split_index:] + +y_test = y[split_index:] + +start=time() + +#create model and trian it + +model = +lb.LBClassifier(base_models=(lb.models.Tree(max_depth=8),lb.models.NN(max_iter=10,hidden_layer_sizes=(10,10),verbose=True))) + +model.fit(x_train,y_train) + +end=time() + +The trained ensemble model is used to generate predictions on the test +set, and its accuracy is evaluated using accuracy_score. Finally, the +model is saved with Joblib for future inference without retraining. + +#predict + +predictions = model.predict(x_test) + +#evalution + +acc = accuracy_score(y_test, predictions) + +print("Accuracy:", acc) + +print(f"\\n The training time for creditscore exp is: {(end - +start)/1000:.6f}ms") + +#model save + +dump(model, "legate_boost_model.joblib") + +#save the test sets to CSV for easy inference performance calculation + +x_test_cpu = x_test.get() if hasattr(x_test, "get") else +np.array(x_test) + +y_test_cpu = y_test.get() if hasattr(y_test, "get") else +np.array(y_test) + +pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) + +pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", +index=False) + + + +This workflow illustrates how Legate DataFrame provides a scalable +preprocessing layer, cupynumeric arrays enable distributed GPU +computation, and Legate Boost delivers a flexible ensemble learning +framework capable of leveraging multi-node, multi-GPU infrastructure +efficiently. + +.. _running-on-cpu-and-gpu-1: + +**Running on CPU and GPU** +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +**CPU** - To run with CPU, use the following command. +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ++-----------------------------------------------------------------------+ +| **legate --cpus 1 --gpus 0 ./creditscore.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **Accuracy: 0.9343** | +| | +| **The training time for creditscore exp is: 45337.714000 ms** | ++=======================================================================+ + +**GPU** - To run with GPU, use the following command. +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ++-----------------------------------------------------------------------+ +| **legate --gpus 2 ./creditscore.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **Accuracy: 0.9353** | +| | +| **The training time for creditscore exp is: 2688.233000 ms** | ++=======================================================================+ + +**To Do: Multi Node and Multi GPU** + +**Inference performance** +------------------------- + +Let’s explore how cuPyNumeric can be leveraged to measure inference +performance statistics seamlessly across both CPU and GPU all without +modifying the code. In this example, we evaluate a pre-trained machine +learning model by calculating key metrics such as mean, median, minimum, +maximum, variance, and standard deviation of inference times. The model +is loaded using joblib, and predictions are executed multiple times on +the test dataset. By utilizing cuPyNumeric arrays, the timing results +are efficiently processed while ensuring compatibility with both CPU and +GPU environments. This approach provides a simple yet powerful way to +compare inference performance across hardware, offering clear insights +into the speedup and variability achieved with GPU acceleration. + +import cupynumeric as cp + +from joblib import load + +from legate.timing import time + +import pandas as pd + +import legate.core as lg + +timings = [] + +#load pre-trained model + +model = load("legate_boost_model.joblib") + +X = pd.read_csv("x_test.csv") + +rt = lg.get_legate_runtime() + +for \_ in range(10): + +rt.issue_execution_fence() + +start = time() + +model.predict(X) + +rt.issue_execution_fence() + +end = time() + +timings.append(end - start) + +#first iteration is not considered here because, it includes env setup +time for gpu usage + +timings = timings[1:] + +timings_gpu = cp.array(timings) + +mean_time = cp.mean(timings_gpu) + +median_time = cp.median(timings_gpu) + +min_time = cp.min(timings_gpu) + +max_time = cp.max(timings_gpu) + +var_time = cp.var(timings_gpu) + +std = cp.sqrt(var_time) + +print(f"Mean: {float(mean_time)/1000:.2f} ms") + +print(f"Median: {float(median_time)/1000:.2f} ms") + +print(f"Min: {float(min_time)/1000:.2f} ms") + +print(f"Max: {float(max_time)/1000:.2f} ms") + +print(f"Variance: {float(var_time)/1000:.2f} ms") + +print(f"standard deviation: {float(std)/1000:.2f} ms") + + + +.. _running-on-cpu-and-gpu-2: + +**Running on CPU and GPU** +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. _cpu---to-run-with-cpu-use-the-following-command.-1: + +**CPU** - To run with CPU, use the following command. +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ++-----------------------------------------------------------------------+ +| **legate --cpus 1 --gpus 0 ./inference.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **Mean: 265.66 ms** | +| | +| **Median: 262.97 ms** | +| | +| **Min: 249.78 ms** | +| | +| **Max: 284.44 ms** | +| | +| **Variance: 117319.15 ms** | +| | +| **standard deviation: 10.83 ms** | ++=======================================================================+ + +.. _gpu---to-run-with-gpu-use-the-following-command.-1: + +**GPU** - To run with GPU, use the following command. +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ++-----------------------------------------------------------------------+ +| **legate --gpus 1 ./inference.py** | ++=======================================================================+ + +Output: + ++-----------------------------------------------------------------------+ +| **Mean: 122.35 ms** | +| | +| **Median: 122.11 ms** | +| | +| **Min: 121.28 ms** | +| | +| **Max: 125.97 ms** | +| | +| **Variance: 1793.76 ms** | +| | +| **standard deviation: 1.34 ms** | ++=======================================================================+ + +These results clearly show the performance benefits of running inference +on a GPU compared to a CPU using cuPyNumeric arrays. On the CPU, the +model achieved a mean inference time of approximately **265.66 ms**, +with relatively low variability (standard deviation ~\ **10.83 ms**). In +contrast, the GPU significantly reduced the mean inference time to +around **122.35 ms**, representing more than a **2x speedup**, with even +lower variability (standard deviation ~\ **1.34 ms**). This highlights +how cuPyNumeric enables the same code to seamlessly scale across CPU and +GPU, allowing both accurate performance benchmarking and efficient model +deployment across heterogeneous hardware. + +**To Do: Multi Node and Multi GPU** From a25732170b17fe14f8aa718214d4e04b626f337c Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 17:18:57 -0400 Subject: [PATCH 04/72] Update Legate_Boost2.rst --- docs/source/Legate_Boost2.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/source/Legate_Boost2.rst b/docs/source/Legate_Boost2.rst index 618ac7dc..a30036e5 100644 --- a/docs/source/Legate_Boost2.rst +++ b/docs/source/Legate_Boost2.rst @@ -1,3 +1,5 @@ +.. _legate-boost: + This article assumes familiarity with the basic usage of gradient boosting libraries such as XGBoost or LightGBM, as well as cuPyNumeric for GPU-accelerated array computations. From 657f77429b5a6d69f841583dddbb77fbc2c1de47 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 17:43:52 -0400 Subject: [PATCH 05/72] Update Legate_Boost2.rst --- docs/source/Legate_Boost2.rst | 766 ++++++++++++++-------------------- 1 file changed, 314 insertions(+), 452 deletions(-) diff --git a/docs/source/Legate_Boost2.rst b/docs/source/Legate_Boost2.rst index a30036e5..d3dd9c45 100644 --- a/docs/source/Legate_Boost2.rst +++ b/docs/source/Legate_Boost2.rst @@ -1,11 +1,16 @@ .. _legate-boost: + +============= +Legate Boost +============= + This article assumes familiarity with the basic usage of gradient boosting libraries such as XGBoost or LightGBM, as well as cuPyNumeric for GPU-accelerated array computations. -**What is legate boost?** -------------------------- +What is legate boost? +===================== In scenarios where high-performance training is needed across large datasets or distributed hardware, Legate Boost offers a scalable @@ -26,62 +31,55 @@ architectural simplicity, extensibility, and performance, delivering state-of-the-art results on tabular data while leveraging the full computational power of modern heterogeneous hardware. -Please refer to `Distributed computing -cuPyNumeric `__ -and `Legate -boost `__ for more +Please refer to `Distributed Computing with cuPyNumeric`_ +and `Legate boost`_ for more information and detailed instructions on installation. -**Quick installation and setup** --------------------------------- - -+--------------------------------------------------------------------------------------------+ -| # create a new env and install legate boost and dependencies | -| | -| $ conda create -n legate-boost -c legate -c conda-forge -c nvidia legate-boost | -| | -| # activate env | -| | -| $ conda activate legate-boost | -| | -| # install wrappers | -| | -| | $ conda install -c legate/label/gex-experimental realm-gex-wrapper legate-mpi-wrapper | -| | # install cmake | -| | -| $ conda install -c conda-forge cmake>=3.26.4 | -| | -| # build wrappers | -| | -| $ | -| /global/homes/**n/ngraddon**/.conda/envs/legate-boost-new/mpi-wrapper/build-mpi-wrapper.sh | -| | -| $ | -| /global/homes/**n/ngraddon**/.conda/envs/legate-boost-new/gex-wrapper/build-gex-wrapper.sh | -| | -| # reactivate env | -| | -| $ conda activate legate-boost | -| | -| # install legate-dataframe | -| | -| $ conda install -c legate -c rapidsai -c conda-forge legate-dataframe | -+============================================================================================+ - -**Usage** ---------- +.. _Distributed Computing with cuPyNumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cuPyNumeric.ipynb + +.. _Legate boost: https://github.com/rapidsai/legate-boost/tree/main + +Quick installation and setup +---------------------------- + +.. code-block:: sh + + # create a new env and install legate boost and dependencies + conda create -n legate-boost -c legate -c conda-forge -c nvidia legate-boost + + # activate env + conda activate legate-boost + + # install wrappers + conda install -c legate/label/gex-experimental realm-gex-wrapper legate-mpi-wrapper + + # install cmake + conda install -c conda-forge cmake>=3.26.4 + + # build wrappers + /global/homes/n/ngraddon/.conda/envs/legate-boost-new/mpi-wrapper/build-mpi-wrapper.sh + /global/homes/n/ngraddon/.conda/envs/legate-boost-new/gex-wrapper/build-gex-wrapper.sh + + # reactivate env + conda activate legate-boost + + # install legate-dataframe + conda install -c legate -c rapidsai -c conda-forge legate-dataframe + + +Usage +----- Legate Boost offers two main estimator types: - LBRegressor for regression tasks - - LBClassifier for classification tasks -These estimators follow a similar interface to those in **XGboost**, +These estimators follow a similar interface to those in XGboost, making them easy to integrate into existing machine learning pipelines. -**Regression with LBRegressor** -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Regression with LBRegressor +--------------------------- The LBRegressor estimator is used to predict continuous values such as house prices, temperature, or sales forecasting. The following code @@ -89,42 +87,33 @@ demonstrates how to create an instance of the LBRegressor model, use the fit() function to train it on a dataset, and then apply the predict() function to generate predictions on new data. Here’s how to set it up: -import legateboost as lb - -from sklearn.datasets import make_regression - -#creating synthetic dataset - -x,y = make_regression(n_samples=100, n_features=4, noise=8, -random_state=42) - -#splitting the data into training and testing sets +.. code-block:: python -X_train, X_test, y_train, y_test = train_test_split( + import legateboost as lb + from sklearn.datasets import make_regression + from sklearn.model_selection import train_test_split -data.data, data.target, test_size=0.2, random_state=42) + # creating synthetic dataset + X, y = make_regression(n_samples=100, n_features=4, noise=8, random_state=42) -#regression model with 100 estimators + # splitting the data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) -regresion_model = lb.LBRegressor(n_estimators=100) + # regression model with 100 estimators + regression_model = lb.LBRegressor(n_estimators=100) -#fit the model + # fit the model + regression_model.fit(X_train, y_train) -regression_model.fit(X_train, y_train) - -#predict using the trained model - -y_pred = regression_model.predict(X_test) - - + # predict + y_pred = regression_model.predict(X_test) In this example: +~~~~~~~~~~~~~~~~ - LBRegressor is initialized with 100 boosting estimators. - - The fit() method trains the model using the input features (X_train) and target values (y_train). - - After training, the predict() method is used to make predictions on the test set (X_test). @@ -132,11 +121,12 @@ This represents a typical workflow for applying a regression model using Legate Boost. The LBRegressor estimator offers several configurable options, such as base_model and learning_rate, to help optimize model performance. For a comprehensive list of features and parameters, refer -to the official -`documentation `__. +to the `official documentation`_. + +.. _official documentation: https://rapidsai.github.io/legate-boost/api/estimators.html -**Classification with LBClassifier** -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Classification with LBClassifier +--------------------------------- The LBClassifier is designed for predicting categorical outcomes and supports both binary and multi-class classification tasks. It is ideal @@ -146,36 +136,26 @@ classification, and sentiment analysis. The example below demonstrates how to implement a classification model using the LBClassifier estimator from Legate Boost: -import legateboost as lb - -from sklearn.datasets import make_regression - -from sklearn.model_selection import train_test_split - -#creating synthetic dataset +.. code-block:: python -x,y = make_regression(n_samples=100, n_features=4, n_informative=3, -n_redundant=0, n_classes=2, random_state=42) + import legateboost as lb + from sklearn.datasets import make_classification + from sklearn.model_selection import train_test_split -#splitting the data into training and testing sets + # creating synthetic dataset + X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42) -X_train, X_test, y_train, y_test = train_test_split( + # splitting the data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) -data.data, data.target, test_size=0.2, random_state=42) + # classification model with 50 estimators + classification_model = lb.LBClassifier(n_estimators=50) -# Create a classification model with 50 boosting estimators + classification_model.fit(X_train, y_train) + y_pred = classification_model.predict(X_test) -classification_model = lb.LBClassifier(n_estimators=50) - -# Train the model on labeled data - -classification_model.fit(X_train, y_train) - -# Predict class labels on new data - -y_pred = classification_model.predict(X_test) - -In this example: +In this example: +~~~~~~~~~~~~~~~~ - LBClassifier(n_estimators=50) sets up a classifier with 50 boosting rounds. @@ -187,20 +167,19 @@ y_pred = classification_model.predict(X_test) Just like the regressor, the LBClassifier follows a clean and intuitive workflow. It provides additional options and advanced configurations to optimize model performance. For more detailed information, refer to the -Legate Boost estimators -`documentation `__. +Legate Boost `estimators`_ documentation. -**Example 1** -------------- +.. _estimators: https://rapidsai.github.io/legate-boost/api/estimators.html#legateboost.LBClassifier -Here is an example of using Legate Boost to build a regression model on -the California housing dataset. +Example 1 +========= -It showcases key features like scalable training across GPUs/nodes, +Here is an example of using Legate Boost to build a regression model on +the California housing dataset. It showcases key features like scalable training across GPUs/nodes, customizable base models, and adjustable learning rates. -**About dataset** -~~~~~~~~~~~~~~~~~~ +About dataset +------------- The California housing dataset is a classic benchmark dataset containing information collected from California districts in the 1990 census. Each @@ -208,21 +187,17 @@ record describes a block group (a neighborhood-level area), including predictors such as: - Median income of residents - - Average house age - - Average number of rooms and bedrooms - - Population and household count - - Latitude and longitude The target variable is the **median house value** in that block group. This dataset is often used to illustrate regression techniques and assess predictive performance on real-world tabular data. -**About this implementation** -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +About this implementation +------------------------- The following code creates a Legate Boost regression model using LBRegressor, which trains a gradient boosting model optimized for @@ -235,68 +210,40 @@ each tree contributes to the final prediction, balancing speed and stability. The verbose=True flag enables logging during training, allowing to monitor progress and internal operations. -**Code module** -~~~~~~~~~~~~~~~~ - -import cupynumeric as cn - -import legateboost as lb - -from legate.timing import time - -from sklearn.metrics import mean_squared_error - -from sklearn.model_selection import train_test_split - -from sklearn.datasets import fetch_california_housing - -#load and split data - -data = fetch_california_housing() - -X_train, X_test, y_train, y_test = train_test_split( - -data.data, data.target, test_size=0.2, random_state=42) - -#create Legate Boost regressor - -model = lb.LBRegressor(n_estimators=100, -base_models=(lb.models.Tree(max_depth=5),), - -objective = **"squared_error"**, learning_rate = 0.1, verbose=\ **True** -) +Code module +----------- -#training +.. code-block:: python -start = time() + import cupynumeric as cn + import legateboost as lb + from legate.timing import time + from sklearn.metrics import mean_squared_error + from sklearn.model_selection import train_test_split + from sklearn.datasets import fetch_california_housing -model.fit(X_train, y_train) + data = fetch_california_housing() + X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42) -end = time() + model = lb.LBRegressor( + n_estimators=100, + base_models=(lb.models.Tree(max_depth=5),), + objective="squared_error", + learning_rate=0.1, + verbose=True + ) -#prediction + start = time() + model.fit(X_train, y_train) + end = time() -istart = time() + y_pred = model.predict(X_test) -y_pred = model.predict(X_test) + mse = mean_squared_error(y_test, y_pred) + print(f"Test MSE: {mse:.4f}") + print(f"Training time: {(end - start)/1000:.6f} ms") -iend = time() - -#evaluate - -mse = mean_squared_error(y_test, y_pred) - -print(f"Test MSE: {mse:.4f}") - -print(f"\\n The training time for housing exp is: {(end - -start)/1000:.6f}ms") - -print(f"\\n The inference time for housing exp is {(iend - -istart)/1000:.6f}") - - -- This simple example demonstrates how to train a regression model on the California Housing dataset using Legate Boost. Although the code looks @@ -317,37 +264,44 @@ histograms from multiple GPUs), it is handled by legate-mpi-wrapper and realm-gex-wrapper, so we never have to write MPI or manage explicit GPU memory transfers. -**Running on CPU and GPU** -~~~~~~~~~~~~~~~~~~~~~~~~~~ +Running on CPU and GPU +---------------------- + +CPU execution +~~~~~~~~~~~~~ + +To run with CPU, use the following command. + +.. code-block:: sh + + legate --cpus 1 --gpus 0 ./housing.py -**CPU** - To run with CPU, use the following command. +This produces the following output: -+-----------------------------------------------------------------------+ -| **legate --cpus 1 --gpus 0 ./housing.py** | -+=======================================================================+ +.. code-block:: text -Output: + The training time for housing exp is: 7846.303000 milliseconds -+-----------------------------------------------------------------------+ -| **The training time for housing exp is: 7846.303000 milliseconds** | -+=======================================================================+ -**GPU** - To run with GPU, use the following command. +GPU execution +~~~~~~~~~~~~~ -+-----------------------------------------------------------------------+ -| **legate --gpus 2 ./housing.py** | -+=======================================================================+ +To run with GPU, use the following command. -Output: +.. code-block:: sh -+-----------------------------------------------------------------------+ -| **The training time for housing exp is : 846.949000 milliseconds** | -+=======================================================================+ + legate --gpus 2 ./housing.py + +This produces the following output: + +.. code-block:: text + + The training time for housing exp is: 846.949000 milliseconds **To Do: Multi Node and Multi GPU** -**Example 2** -------------- +Example 2 +========= This example demonstrates how Legate Boost can be applied to the *“Give Me Some Credit”* dataset (OpenML data_id: 46929) to build a @@ -357,8 +311,8 @@ Legate Boost to enable distributed training across multi-GPU and multi-node environments, showcasing scalable machine learning on the Credit Score dataset. -**About the dataset** -~~~~~~~~~~~~~~~~~~~~~ +About the dataset +----------------- The Give Me Some Credit dataset is a financial risk prediction dataset originally introduced in a Kaggle competition. It includes anonymized @@ -369,34 +323,25 @@ within the next two years. Each record represents an individual and includes features such as: - Revolving utilization of unsecured credit lines - - Age - - Number of late payments (30–59, 60–89, and 90+ days past due) - - Debt ratio - - Monthly income - - Number of open credit lines and loans - -- Number of dependents\ ** - ** +- Number of dependents The target variable is binary (0 = no distress, 1 = distress), indicating the likelihood of future financial trouble. -.. _about-this-implementation-1: -**About this implementation** -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +About this implementation +------------------------- This implementation will focus on demonstrating Legate Boost’s flexible model ensembling capabilities, specifically: - Tree-based gradient boosting models, ideal for structured/tabular data. - - Neural network-based classifiers, allowing hybrid or deep learning approaches. @@ -414,60 +359,35 @@ DataFrame. LogicalTables internally break data into logical columns, enabling Legate’s runtime to partition, distribute, and schedule computations across multiple GPUs and nodes. - - -import cudf - -import pandas - -import cupy as cp - -import pyarrow as pa - -import legate_dataframe - -import legateboost as lb - -import cupynumeric as cpn - -from legate.timing import time - -from sklearn.datasets import fetch_openml - -from sklearn.metrics import accuracy_score - -from sklearn.model_selection import train_test_split - -from legate_dataframe.lib.replace import replace_nulls - -from legate_dataframe.lib.core.table import LogicalTable - -from legate_dataframe.lib.core.column import LogicalColumn - -#import data - -data = fetch_openml(data_id= 46929, as_frame=True) - -xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas # based on -CPU or GPU - -df = xd.DataFrame(data.data, columns=data.feature_names) - -df['Target'] = data.target - -#covert to logicalTable - -if cp.cuda.runtime.getDeviceCount() > 0: - -ldf = LogicalTable.from_cudf(df) - -else: - -df = pa.Table.from_pandas(df) - -ldf = LogicalTable.from_arrow(df) - - +.. code-block:: python + + import cudf + import pandas + import cupy as cp + import pyarrow as pa + import legate_dataframe + import legateboost as lb + import cupynumeric as cpn + from legate.timing import time + from sklearn.datasets import fetch_openml + from sklearn.metrics import accuracy_score + from legate_dataframe.lib.replace import replace_nulls + from legate_dataframe.lib.core.table import LogicalTable + from legate_dataframe.lib.core.column import LogicalColumn + + # load dataset + data = fetch_openml(data_id=46929, as_frame=True) + + xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas + df = xd.DataFrame(data.data, columns=data.feature_names) + df['Target'] = data.target + + # convert to LogicalTable + if cp.cuda.runtime.getDeviceCount() > 0: + ldf = LogicalTable.from_cudf(df) + else: + df = pa.Table.from_pandas(df) + ldf = LogicalTable.from_arrow(df) Let’s see how data preprocessing is performed directly on the LogicalTable. Missing values in key columns (MonthlyIncome and @@ -481,30 +401,27 @@ GPU-accelerated array type that leverages logical partitioning for distributed computation. This enables the subsequent machine learning tasks to execute efficiently across multiple GPUs or nodes. -#replace nulls - -median_salary = df["MonthlyIncome"].median() +.. code-block:: python -median_dependents = df["NumberOfDependents"].median() + # median imputation + median_salary = df["MonthlyIncome"].median() + median_dependents = df["NumberOfDependents"].median() -mni = -LogicalColumn(replace_nulls(LogicalColumn(ldf["MonthlyIncome"]),median_salary)) + mni = LogicalColumn( + replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary) + ) + mnd = LogicalColumn( + replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) + ) -mnd = -LogicalColumn(replace_nulls(LogicalColumn(ldf["NumberOfDependents"]),median_dependents)) + # rebuild logical table + features = ldf.get_column_names() + nldf = LogicalTable( + [ldf[0], ldf[1], ldf[2], ldf[3], mni, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], + features ) -#create a new logical Table with updated columns - -features = ldf.get_column_names() - -nldf = LogicalTable( [ ldf[0], ldf[1], ldf[2], ldf[3], mmi, ldf[5], -ldf[6], ldf[7], ldf[8], mnd, ldf[10]], features) - -#coverting LogicalTable to cupyNumeric array - -data_arr = nldf.to_array() - - + # convert to cuPyNumeric + data_arr = nldf.to_array() As we have a data_arr backed by cuPyNumeric, we first split the dataset into training and testing subsets, which are then passed to Legate Boost @@ -526,72 +443,55 @@ high-dimensional relationships. This ensemble design results in a more accurate and robust classifier than either model could achieve individually. -#preparing data for training and testing - -x = data_arr[:, :-1] - -y = data_arr[:, -1] - -num_samples = x.shape[0] - -split_ratio = 0.8 +.. code-block:: python -split_index = int(num_samples \* split_ratio) + #preparing data for training and testing + x = data_arr[:, :-1] + y = data_arr[:, -1] -x_train = x[:split_index] + split_index = int(x.shape[0] * 0.8) + x_train, y_train = x[:split_index], y[:split_index] + x_test, y_test = x[split_index:], y[split_index:] -y_train = y[:split_index] + start = time() -x_test = x[split_index:] + # ensemble model + model = lb.LBClassifier( + base_models=( + lb.models.Tree(max_depth=8), + lb.models.NN(max_iter=10, hidden_layer_sizes=(10, 10), verbose=True), + ) + ) + model.fit(x_train, y_train) -y_test = y[split_index:] + end = time() -start=time() - -#create model and trian it - -model = -lb.LBClassifier(base_models=(lb.models.Tree(max_depth=8),lb.models.NN(max_iter=10,hidden_layer_sizes=(10,10),verbose=True))) - -model.fit(x_train,y_train) - -end=time() - -The trained ensemble model is used to generate predictions on the test +The trained ensemble model is used to generate predictions on the test set, and its accuracy is evaluated using accuracy_score. Finally, the model is saved with Joblib for future inference without retraining. -#predict - -predictions = model.predict(x_test) - -#evalution - -acc = accuracy_score(y_test, predictions) - -print("Accuracy:", acc) - -print(f"\\n The training time for creditscore exp is: {(end - -start)/1000:.6f}ms") +.. code-block:: python -#model save + # predict + predictions = model.predict(x_test) -dump(model, "legate_boost_model.joblib") + # evaluate + from sklearn.metrics import accuracy_score + acc = accuracy_score(y_test, predictions) + print("Accuracy:", acc) + print(f"Training time: {(end - start)/1000:.6f} ms") -#save the test sets to CSV for easy inference performance calculation + # save model + from joblib import dump + dump(model, "legate_boost_model.joblib") -x_test_cpu = x_test.get() if hasattr(x_test, "get") else -np.array(x_test) + # save test data for inference + import numpy as np, pandas as pd + x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) + y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) -y_test_cpu = y_test.get() if hasattr(y_test, "get") else -np.array(y_test) - -pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) - -pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", -index=False) - - + pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) + pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) This workflow illustrates how Legate DataFrame provides a scalable preprocessing layer, cupynumeric arrays enable distributed GPU @@ -599,44 +499,43 @@ computation, and Legate Boost delivers a flexible ensemble learning framework capable of leveraging multi-node, multi-GPU infrastructure efficiently. -.. _running-on-cpu-and-gpu-1: +Running on CPU and GPU +---------------------- -**Running on CPU and GPU** -~~~~~~~~~~~~~~~~~~~~~~~~~~ +CPU execution +~~~~~~~~~~~~~ -**CPU** - To run with CPU, use the following command. -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +To run with CPU, use the following command. -+-----------------------------------------------------------------------+ -| **legate --cpus 1 --gpus 0 ./creditscore.py** | -+=======================================================================+ -Output: +.. code-block:: sh -+-----------------------------------------------------------------------+ -| **Accuracy: 0.9343** | -| | -| **The training time for creditscore exp is: 45337.714000 ms** | -+=======================================================================+ + legate --cpus 1 --gpus 0 ./creditscore.py -**GPU** - To run with GPU, use the following command. -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +This produces the following output: -+-----------------------------------------------------------------------+ -| **legate --gpus 2 ./creditscore.py** | -+=======================================================================+ +:: -Output: + Accuracy: 0.9343 + The training time for credit score exp is : 45337.714000 ms -+-----------------------------------------------------------------------+ -| **Accuracy: 0.9353** | -| | -| **The training time for creditscore exp is: 2688.233000 ms** | -+=======================================================================+ +GPU execution +~~~~~~~~~~~~~ -**To Do: Multi Node and Multi GPU** +To run with GPU, use the following command. + +.. code-block:: sh -**Inference performance** + legate --gpus 2 ./creditscore.py + +This produces the following output: + +:: + + Accuracy: 0.9353 + The training time for credit score exp is : 2688.233000 ms + +Inference performance ------------------------- Let’s explore how cuPyNumeric can be leveraged to measure inference @@ -651,127 +550,90 @@ GPU environments. This approach provides a simple yet powerful way to compare inference performance across hardware, offering clear insights into the speedup and variability achieved with GPU acceleration. -import cupynumeric as cp - -from joblib import load - -from legate.timing import time - -import pandas as pd - -import legate.core as lg - -timings = [] - -#load pre-trained model - -model = load("legate_boost_model.joblib") - -X = pd.read_csv("x_test.csv") - -rt = lg.get_legate_runtime() - -for \_ in range(10): - -rt.issue_execution_fence() - -start = time() - -model.predict(X) - -rt.issue_execution_fence() - -end = time() - -timings.append(end - start) - -#first iteration is not considered here because, it includes env setup -time for gpu usage - -timings = timings[1:] - -timings_gpu = cp.array(timings) - -mean_time = cp.mean(timings_gpu) +.. code-block:: python -median_time = cp.median(timings_gpu) + import cupynumeric as cp + from joblib import load + from legate.timing import time + import pandas as pd + import legate.core as lg -min_time = cp.min(timings_gpu) + timings = [] -max_time = cp.max(timings_gpu) + # load model and test data + model = load("legate_boost_model.joblib") + X = pd.read_csv("x_test.csv") -var_time = cp.var(timings_gpu) + rt = lg.get_legate_runtime() -std = cp.sqrt(var_time) + for _ in range(10): + rt.issue_execution_fence() + start = time() + model.predict(X) + rt.issue_execution_fence() + end = time() + timings.append(end - start) -print(f"Mean: {float(mean_time)/1000:.2f} ms") + timings = timings[1:] # ignore first run + timings_gpu = cp.array(timings) -print(f"Median: {float(median_time)/1000:.2f} ms") + mean_time = cp.mean(timings_gpu) + median_time = cp.median(timings_gpu) + min_time = cp.min(timings_gpu) + max_time = cp.max(timings_gpu) + var_time = cp.var(timings_gpu) + std = cp.sqrt(var_time) -print(f"Min: {float(min_time)/1000:.2f} ms") + print(f"Mean: {float(mean_time)/1000:.2f} ms") + print(f"Median: {float(median_time)/1000:.2f} ms") + print(f"Min: {float(min_time)/1000:.2f} ms") + print(f"Max: {float(max_time)/1000:.2f} ms") + print(f"Variance: {float(var_time)/1000:.2f} ms") + print(f"Standard deviation: {float(std)/1000:.2f} ms") -print(f"Max: {float(max_time)/1000:.2f} ms") +Running on CPU and GPU +---------------------- -print(f"Variance: {float(var_time)/1000:.2f} ms") +CPU execution +~~~~~~~~~~~~~ -print(f"standard deviation: {float(std)/1000:.2f} ms") +To run with CPU, use the following command. - +.. code-block:: sh -.. _running-on-cpu-and-gpu-2: + legate --cpus 1 --gpus 0 ./inference.py -**Running on CPU and GPU** -~~~~~~~~~~~~~~~~~~~~~~~~~~ +This produces the following output: -.. _cpu---to-run-with-cpu-use-the-following-command.-1: +.. code-block:: text -**CPU** - To run with CPU, use the following command. -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + Mean: 265.66 ms + Median: 262.97 ms + Min: 249.78 ms + Max: 284.44 ms + Variance: 117319.15 ms + Standard deviation: 10.83 ms -+-----------------------------------------------------------------------+ -| **legate --cpus 1 --gpus 0 ./inference.py** | -+=======================================================================+ -Output: +GPU execution +~~~~~~~~~~~~~ -+-----------------------------------------------------------------------+ -| **Mean: 265.66 ms** | -| | -| **Median: 262.97 ms** | -| | -| **Min: 249.78 ms** | -| | -| **Max: 284.44 ms** | -| | -| **Variance: 117319.15 ms** | -| | -| **standard deviation: 10.83 ms** | -+=======================================================================+ +To run with GPU, use the following command. -.. _gpu---to-run-with-gpu-use-the-following-command.-1: +.. code-block:: sh -**GPU** - To run with GPU, use the following command. -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + legate --gpus 1 ./inference.py -+-----------------------------------------------------------------------+ -| **legate --gpus 1 ./inference.py** | -+=======================================================================+ +This produces the following output: -Output: +.. code-block:: text -+-----------------------------------------------------------------------+ -| **Mean: 122.35 ms** | -| | -| **Median: 122.11 ms** | -| | -| **Min: 121.28 ms** | -| | -| **Max: 125.97 ms** | -| | -| **Variance: 1793.76 ms** | -| | -| **standard deviation: 1.34 ms** | -+=======================================================================+ + Mean: 122.35 ms + Median: 122.11 ms + Min: 121.28 ms + Max: 125.97 ms + Variance: 1793.76 ms + Standard deviation: 1.34 ms These results clearly show the performance benefits of running inference on a GPU compared to a CPU using cuPyNumeric arrays. On the CPU, the From 1cea7b532670497bc5ce27fed48f33673884a24c Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 17:49:11 -0400 Subject: [PATCH 06/72] Update Legate_Boost2.rst --- docs/source/Legate_Boost2.rst | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/docs/source/Legate_Boost2.rst b/docs/source/Legate_Boost2.rst index d3dd9c45..e5212d44 100644 --- a/docs/source/Legate_Boost2.rst +++ b/docs/source/Legate_Boost2.rst @@ -68,7 +68,7 @@ Quick installation and setup Usage ------ +===== Legate Boost offers two main estimator types: @@ -210,7 +210,6 @@ each tree contributes to the final prediction, balancing speed and stability. The verbose=True flag enables logging during training, allowing to monitor progress and internal operations. - Code module ----------- @@ -333,7 +332,6 @@ Each record represents an individual and includes features such as: The target variable is binary (0 = no distress, 1 = distress), indicating the likelihood of future financial trouble. - About this implementation ------------------------- @@ -507,14 +505,13 @@ CPU execution To run with CPU, use the following command. - .. code-block:: sh legate --cpus 1 --gpus 0 ./creditscore.py This produces the following output: -:: +.. code-block:: text Accuracy: 0.9343 The training time for credit score exp is : 45337.714000 ms @@ -530,13 +527,13 @@ To run with GPU, use the following command. This produces the following output: -:: +.. code-block:: text Accuracy: 0.9353 The training time for credit score exp is : 2688.233000 ms Inference performance -------------------------- +===================== Let’s explore how cuPyNumeric can be leveraged to measure inference performance statistics seamlessly across both CPU and GPU all without From a79992d8f6fa0b61372c53c8d6841f86802971bd Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 17:50:50 -0400 Subject: [PATCH 07/72] Delete docs/source/Legate_Boost.rst --- docs/source/Legate_Boost.rst | 649 ----------------------------------- 1 file changed, 649 deletions(-) delete mode 100644 docs/source/Legate_Boost.rst diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst deleted file mode 100644 index 30844230..00000000 --- a/docs/source/Legate_Boost.rst +++ /dev/null @@ -1,649 +0,0 @@ -.. _legate-boost: - -============= -Legate Boost -============= - - -This article assumes familiarity with the basic usage of gradient -boosting libraries such as XGBoost or LightGBM, as well as cuPyNumeric -for GPU-accelerated array computations. - -What is legate boost? -====================== - -In scenarios where high-performance training is needed across large -datasets or distributed hardware, Legate Boost offers a scalable -alternative. Legate Boost is an advanced gradient boosting library built -on the Legate and Legion parallel programming frameworks. Unlike -traditional boosting libraries such as XGBoost or LightGBM, Legate Boost -provides a unified infrastructure that seamlessly scales across CPUs and -GPUs, supporting both single-node and distributed training while -integrating naturally with cuPyNumeric workflows for efficient -end-to-end data processing. It enables users to define not only -conventional boosted decision trees but also hybrid ensembles combining -trees, kernel ridge regression, linear models, or neural networks, all -written in Python with minimal code changes. - -These models are automatically parallelized and executed efficiently -without manual data movement or partitioning. Legate Boost emphasizes -architectural simplicity, extensibility, and performance, delivering -state-of-the-art results on tabular data while leveraging the full -computational power of modern heterogeneous hardware. - -Please refer to `Distributed Computing with cuPyNumeric`_ -and `Legate boost`_ for more -information and detailed instructions on installation. - -.. _Distributed Computing with cuPyNumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cuPyNumeric.ipynb - -.. _Legate boost: https://github.com/rapidsai/legate-boost/tree/main - - -Quick installation and setup ----------------------------- - -.. code-block:: sh - - # create a new env and install legate boost and dependencies - conda create -n legate-boost -c legate -c conda-forge -c nvidia legate-boost - - # activate env - conda activate legate-boost - - # install wrappers - conda install -c legate/label/gex-experimental realm-gex-wrapper legate-mpi-wrapper - - # install cmake - conda install -c conda-forge cmake>=3.26.4 - - # build wrappers - /global/homes/n/ngraddon/.conda/envs/legate-boost-new/mpi-wrapper/build-mpi-wrapper.sh - /global/homes/n/ngraddon/.conda/envs/legate-boost-new/gex-wrapper/build-gex-wrapper.sh - - # reactivate env - conda activate legate-boost - - # install legate-dataframe - conda install -c legate -c rapidsai -c conda-forge legate-dataframe - - -Usage -===== - -Legate Boost offers two main estimator types: - -- LBRegressor for regression tasks -- LBClassifier for classification tasks - -These estimators follow a similar interface to those in **XGboost**, -making them easy to integrate into existing machine learning pipelines. - -Regression with LBRegressor ---------------------------- - -The LBRegressor estimator is used to predict continuous values such as -house prices, temperature, or sales forecasting. The following code -demonstrates how to create an instance of the LBRegressor model, use the -fit() function to train it on a dataset, and then apply the predict() -function to generate predictions on new data. Here’s how to set it up: - - -.. code-block:: python - - import legateboost as lb - from sklearn.datasets import make_regression - from sklearn.model_selection import train_test_split - - # creating synthetic dataset - X, y = make_regression(n_samples=100, n_features=4, noise=8, random_state=42) - - # splitting the data - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - - # regression model with 100 estimators - regression_model = lb.LBRegressor(n_estimators=100) - - # fit the model - regression_model.fit(X_train, y_train) - - # predict - y_pred = regression_model.predict(X_test) - - - -In this example: -~~~~~~~~~~~~~~~~ - -- LBRegressor is initialized with 100 boosting estimators. -- The fit() method trains the model using the input features (X_train) - and target values (y_train). -- After training, the predict() method is used to make predictions on - the test set (X_test). - -This represents a typical workflow for applying a regression model using -Legate Boost. The LBRegressor estimator offers several configurable -options, such as base_model and learning_rate, to help optimize model -performance. For a comprehensive list of features and parameters, refer -to the `official documentation`_. - -.. _official documentation: https://rapidsai.github.io/legate-boost/api/estimators.html - -Classification with LBClassifier ---------------------------------- - -The LBClassifier is designed for predicting categorical outcomes and -supports both binary and multi-class classification tasks. It is ideal -for a wide range of applications, including spam detection, image -classification, and sentiment analysis. - -The example below demonstrates how to implement a classification model -using the LBClassifier estimator from Legate Boost: - -.. code-block:: python - - import legateboost as lb - from sklearn.datasets import make_classification - from sklearn.model_selection import train_test_split - - # creating synthetic dataset - X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42) - - # splitting the data - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - - # classification model with 50 estimators - classification_model = lb.LBClassifier(n_estimators=50) - - classification_model.fit(X_train, y_train) - y_pred = classification_model.predict(X_test) - -In this example: -~~~~~~~~~~~~~~~~ - -- LBClassifier(n_estimators=50) sets up a classifier with 50 boosting - rounds. - -- fit(X_train, y_train) learns the patterns from your training dataset. - -- predict(X_test) outputs predicted class labels for the test dataset. - -Just like the regressor, the LBClassifier follows a clean and intuitive -workflow. It provides additional options and advanced configurations to -optimize model performance. For more detailed information, refer to the -Legate Boost `estimators`_ documentation. - -.. _estimators: https://rapidsai.github.io/legate-boost/api/estimators.html#legateboost.LBClassifier - -Example 1 -========= - -Here is an example of using Legate Boost to build a regression model on -the California housing dataset. - -It showcases key features like scalable training across GPUs/nodes, -customizable base models, and adjustable learning rates. - -About dataset -------------- - -The California housing dataset is a classic benchmark dataset containing -information collected from California districts in the 1990 census. Each -record describes a block group (a neighborhood-level area), including -predictors such as: - -- Median income of residents - -- Average house age - -- Average number of rooms and bedrooms - -- Population and household count - -- Latitude and longitude - -The target variable is the **median house value** in that block group. -This dataset is often used to illustrate regression techniques and -assess predictive performance on real-world tabular data. - -About this implementation -------------------------- - -The following code creates a Legate Boost regression model using -LBRegressor, which trains a gradient boosting model optimized for -multi-GPU and multi-node environments. The model is configured to use -100 boosting rounds (n_estimators=100), with each round adding a -decision tree (lb.models.Tree) limited to a maximum depth of 5. The loss -function is set to "squared_error", suitable for regression tasks as it -minimizes mean squared error. A learning_rate of 0.1 controls how much -each tree contributes to the final prediction, balancing speed and -stability. The verbose=True flag enables logging during training, -allowing to monitor progress and internal operations. - -Code module ------------ - -.. code-block:: python - - import cupynumeric as cn - import legateboost as lb - from legate.timing import time - from sklearn.metrics import mean_squared_error - from sklearn.model_selection import train_test_split - from sklearn.datasets import fetch_california_housing - - data = fetch_california_housing() - X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42) - - model = lb.LBRegressor( - n_estimators=100, - base_models=(lb.models.Tree(max_depth=5),), - objective="squared_error", - learning_rate=0.1, - verbose=True - ) - - start = time() - model.fit(X_train, y_train) - end = time() - - y_pred = model.predict(X_test) - - mse = mean_squared_error(y_test, y_pred) - print(f"Test MSE: {mse:.4f}") - print(f"Training time: {(end - start)/1000:.6f} ms") - - -This simple example demonstrates how to train a regression model on the -California Housing dataset using Legate Boost. Although the code looks -similar to standard XGBoost, Legate Boost automatically enables -multi-GPU and multi-node computation. Legate Boost achieves multi-GPU -and multi-node scaling through its integration with cupynumeric and the -Legion runtime. Unlike traditional GPU libraries that allocate data to a -single device, cupynumeric creates Logical Arrays and abstract -representations of the data that are not bound to one GPU. The Legate -automatically partitions these logical arrays into physical chunks and -maps them across all available GPUs and nodes. - -During training, operations such as histogram building, gradient -computation, and tree construction are expressed as parallel tasks. -Legate schedules these tasks close to where the data resides, minimizing -communication overhead. When synchronization is needed (e.g., to combine -histograms from multiple GPUs), it is handled by legate-mpi-wrapper and -realm-gex-wrapper, so we never have to write MPI or manage explicit GPU -memory transfers. - -Running on CPU and GPU ----------------------- - -CPU - To run with CPU, use the following command. - - -.. code-block:: sh - - legate --cpus 1 --gpus 0 ./housing.py - -This produces the following output: - -.. code-block:: text - - The training time for housing exp is: 7846.303000 milliseconds - - -GPU - To run with GPU, use the following command. - -.. code-block:: sh - - legate --gpus 2 ./housing.py - -This produces the following output: - -.. code-block:: text - - The training time for housing exp is: 846.949000 milliseconds - -**To Do: Multi Node and Multi GPU** - -Example 2 -========= - -This example demonstrates how Legate Boost can be applied to the *“Give -Me Some Credit”* dataset (OpenML data_id: 46929) to build a -classification model using ensemble learning by combining different -model types. It also highlights the integration of Legate DataFrame with -Legate Boost to enable distributed training across multi-GPU and -multi-node environments, showcasing scalable machine learning on the -Credit Score dataset. - -About the dataset ------------------ - -The Give Me Some Credit dataset is a financial risk prediction dataset -originally introduced in a Kaggle competition. It includes anonymized -credit and demographic data for individuals, with the goal of predicting -whether a person is likely to experience serious financial distress -within the next two years. - -Each record represents an individual and includes features such as: - -- Revolving utilization of unsecured credit lines -- Age -- Number of late payments (30–59, 60–89, and 90+ days past due) -- Debt ratio -- Monthly income -- Number of open credit lines and loans -- Number of dependents - -The target variable is binary (0 = no distress, 1 = distress), -indicating the likelihood of future financial trouble. - - -About this implementation -------------------------- - -This implementation will focus on demonstrating Legate Boost’s flexible -model ensembling capabilities, specifically: - -- Tree-based gradient boosting models, ideal for structured/tabular - data. -- Neural network-based classifiers, allowing hybrid or deep learning - approaches. - -By leveraging Legate Boost, we can ensemble these two models and -efficiently train and evaluate both model types on GPUs or CPUs, -showcasing scalable performance for large tabular datasets in financial -risk prediction. - -The pipeline begins with importing required libraries and its functions -and also loading the dataset using fetch_openml. Depending on hardware -availability, the data is initially handled either with cuDF (for GPU -execution) or pandas (for CPU execution). The dataset is then wrapped -into a LogicalTable, the distributed data representation used by Legate -DataFrame. LogicalTables internally break data into logical columns, -enabling Legate’s runtime to partition, distribute, and schedule -computations across multiple GPUs and nodes. - - -.. code-block:: python - - import cudf - import pandas - import cupy as cp - import pyarrow as pa - import legate_dataframe - import legateboost as lb - import cupynumeric as cpn - from legate.timing import time - from sklearn.datasets import fetch_openml - from sklearn.metrics import accuracy_score - from legate_dataframe.lib.replace import replace_nulls - from legate_dataframe.lib.core.table import LogicalTable - from legate_dataframe.lib.core.column import LogicalColumn - - # load dataset - data = fetch_openml(data_id=46929, as_frame=True) - - xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas - df = xd.DataFrame(data.data, columns=data.feature_names) - df['Target'] = data.target - - # convert to LogicalTable - if cp.cuda.runtime.getDeviceCount() > 0: - ldf = LogicalTable.from_cudf(df) - else: - df = pa.Table.from_pandas(df) - ldf = LogicalTable.from_arrow(df) - -Let’s see how data preprocessing is performed directly on the -LogicalTable. Missing values in key columns (MonthlyIncome and -NumberOfDependents) are filled using median imputation through the -replace_nulls operation. These operations are executed in parallel -across distributed partitions of the LogicalTable, avoiding centralized -bottlenecks. Because LogicalTables are immutable, a new LogicalTable -with updated LogicalColumn’s is created after preprocessing. The cleaned -data is then converted into a cuPyNumeric array, Legate’s -GPU-accelerated array type that leverages logical partitioning for -distributed computation. This enables the subsequent machine learning -tasks to execute efficiently across multiple GPUs or nodes. - -.. code-block:: python - - # median imputation - median_salary = df["MonthlyIncome"].median() - median_dependents = df["NumberOfDependents"].median() - - mni = LogicalColumn( - replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary) - ) - mnd = LogicalColumn( - replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) - ) - - # rebuild logical table - features = ldf.get_column_names() - nldf = LogicalTable( - [ldf[0], ldf[1], ldf[2], ldf[3], mni, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], - features ) - - # convert to cuPyNumeric - data_arr = nldf.to_array() - -As we have a data_arr backed by cuPyNumeric, we first split the dataset -into training and testing subsets, which are then passed to Legate Boost -for efficient training across available hardware resources. The model is -built using Legate Boost’s ensemble framework (LBClassifier), which -allows combining multiple types of base learners into a single unified -model. - -In this example, the ensemble consists of a Decision Tree -(lb.models.Tree) with max_depth=8, enabling the capture of complex -non-linear decision boundaries by splitting the feature space -hierarchically up to 8 levels deep, and a Neural Network (lb.models.NN) -with two hidden layers of 10 neurons each (hidden_layer_sizes=(10,10)), -trained for max_iter=10 epochs with verbose=True to monitor progress. By -combining a tree-based model with a neural network, Legate Boost -leverages the interpretability and rule-based decision-making of trees -together with the ability of neural networks to model intricate, -high-dimensional relationships. This ensemble design results in a more -accurate and robust classifier than either model could achieve -individually. - -.. code-block:: python - - #preparing data for training and testing - x = data_arr[:, :-1] - y = data_arr[:, -1] - - split_index = int(x.shape[0] * 0.8) - x_train, y_train = x[:split_index], y[:split_index] - x_test, y_test = x[split_index:], y[split_index:] - - start = time() - - # ensemble model - model = lb.LBClassifier( - base_models=( - lb.models.Tree(max_depth=8), - lb.models.NN(max_iter=10, hidden_layer_sizes=(10, 10), verbose=True), - ) - ) - model.fit(x_train, y_train) - - end = time() - -The trained ensemble model is used to generate predictions on the test -set, and its accuracy is evaluated using accuracy_score. Finally, the -model is saved with Joblib for future inference without retraining. - -.. code-block:: python - - # predict - predictions = model.predict(x_test) - - # evaluate - from sklearn.metrics import accuracy_score - acc = accuracy_score(y_test, predictions) - print("Accuracy:", acc) - print(f"Training time: {(end - start)/1000:.6f} ms") - - # save model - from joblib import dump - dump(model, "legate_boost_model.joblib") - - # save test data for inference - import numpy as np, pandas as pd - x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) - y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) - - pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) - pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) - - -This workflow illustrates how Legate DataFrame provides a scalable -preprocessing layer, cupynumeric arrays enable distributed GPU -computation, and Legate Boost delivers a flexible ensemble learning -framework capable of leveraging multi-node, multi-GPU infrastructure -efficiently. - - -Running on CPU and GPU ----------------------- - -CPU - To run with CPU, use the following command. -^^^ - -.. code-block:: sh - - legate --cpus 1 --gpus 0 ./creditscore.py - -Output: - -:: - - Accuracy: 0.9343 - The training time for credit score exp is : 45337.714000 ms - -GPU - To run with GPU, use the following command. -^^^^^^^ - -.. code-block:: sh - - legate --gpus 2 ./creditscore.py - -Output: - -:: - - Accuracy: 0.9353 - The training time for credit score exp is : 2688.233000 ms - -**To Do: Multi Node and Multi GPU** - -Inference performance -===================== - -Let’s explore how cuPyNumeric can be leveraged to measure inference -performance statistics seamlessly across both CPU and GPU all without -modifying the code. In this example, we evaluate a pre-trained machine -learning model by calculating key metrics such as mean, median, minimum, -maximum, variance, and standard deviation of inference times. The model -is loaded using joblib, and predictions are executed multiple times on -the test dataset. By utilizing cuPyNumeric arrays, the timing results -are efficiently processed while ensuring compatibility with both CPU and -GPU environments. This approach provides a simple yet powerful way to -compare inference performance across hardware, offering clear insights -into the speedup and variability achieved with GPU acceleration. - -.. code-block:: python - - import cupynumeric as cp - from joblib import load - from legate.timing import time - import pandas as pd - import legate.core as lg - - timings = [] - - # load model and test data - model = load("legate_boost_model.joblib") - X = pd.read_csv("x_test.csv") - - rt = lg.get_legate_runtime() - - for _ in range(10): - rt.issue_execution_fence() - start = time() - model.predict(X) - rt.issue_execution_fence() - end = time() - timings.append(end - start) - - timings = timings[1:] # ignore first run - timings_gpu = cp.array(timings) - - mean_time = cp.mean(timings_gpu) - median_time = cp.median(timings_gpu) - min_time = cp.min(timings_gpu) - max_time = cp.max(timings_gpu) - var_time = cp.var(timings_gpu) - std = cp.sqrt(var_time) - - print(f"Mean: {float(mean_time)/1000:.2f} ms") - print(f"Median: {float(median_time)/1000:.2f} ms") - print(f"Min: {float(min_time)/1000:.2f} ms") - print(f"Max: {float(max_time)/1000:.2f} ms") - print(f"Variance: {float(var_time)/1000:.2f} ms") - print(f"Standard deviation: {float(std)/1000:.2f} ms") - - -Running on CPU and GPU ----------------------- - -CPU - To run with CPU, use the following command. - -.. code-block:: sh - - legate --cpus 1 --gpus 0 ./inference.py - -Output: - -.. code-block:: text - - Mean: 265.66 ms - Median: 262.97 ms - Min: 249.78 ms - Max: 284.44 ms - Variance: 117319.15 ms - Standard deviation: 10.83 ms - - -GPU - To run with GPU, use the following command. - - -.. code-block:: sh - - legate --gpus 1 ./inference.py - -Output: - -.. code-block:: text - - Mean: 122.35 ms - Median: 122.11 ms - Min: 121.28 ms - Max: 125.97 ms - Variance: 1793.76 ms - Standard deviation: 1.34 ms - -These results clearly show the performance benefits of running inference -on a GPU compared to a CPU using cuPyNumeric arrays. On the CPU, the -model achieved a mean inference time of approximately **265.66 ms**, -with relatively low variability (standard deviation ~\ **10.83 ms**). In -contrast, the GPU significantly reduced the mean inference time to -around **122.35 ms**, representing more than a **2x speedup**, with even -lower variability (standard deviation ~\ **1.34 ms**). This highlights -how cuPyNumeric enables the same code to seamlessly scale across CPU and -GPU, allowing both accurate performance benchmarking and efficient model -deployment across heterogeneous hardware. - -**To Do: Multi Node and Multi GPU** From 6a37e04a559c6012ae97308301f9c0d23e9ee57b Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 17:51:45 -0400 Subject: [PATCH 08/72] Rename Legate_Boost2.rst to Legate_Boost.rst --- docs/source/{Legate_Boost2.rst => Legate_Boost.rst} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/source/{Legate_Boost2.rst => Legate_Boost.rst} (100%) diff --git a/docs/source/Legate_Boost2.rst b/docs/source/Legate_Boost.rst similarity index 100% rename from docs/source/Legate_Boost2.rst rename to docs/source/Legate_Boost.rst From ced8886760d77d49fd44ae383aa38011fd3ee11a Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 17:53:34 -0400 Subject: [PATCH 09/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index e5212d44..86ef30f2 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -57,8 +57,8 @@ Quick installation and setup conda install -c conda-forge cmake>=3.26.4 # build wrappers - /global/homes/n/ngraddon/.conda/envs/legate-boost-new/mpi-wrapper/build-mpi-wrapper.sh - /global/homes/n/ngraddon/.conda/envs/legate-boost-new/gex-wrapper/build-gex-wrapper.sh + ~/.conda/envs/legate-boost-new/mpi-wrapper/build-mpi-wrapper.sh + ~/.conda/envs/legate-boost-new/gex-wrapper/build-gex-wrapper.sh # reactivate env conda activate legate-boost From 61905e07b61e06594ccb792fad3b38506f5dece5 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 18:15:13 -0400 Subject: [PATCH 10/72] Add files via upload --- docs/source/creditscore.py | 84 ++++++++++++++++++++++++++++++++++++++ docs/source/housing.py | 51 +++++++++++++++++++++++ 2 files changed, 135 insertions(+) create mode 100644 docs/source/creditscore.py create mode 100644 docs/source/housing.py diff --git a/docs/source/creditscore.py b/docs/source/creditscore.py new file mode 100644 index 00000000..12670d18 --- /dev/null +++ b/docs/source/creditscore.py @@ -0,0 +1,84 @@ +import cudf +import pandas +import cupy as cp +import pyarrow as pa +import legate_dataframe +import legateboost as lb +import cupynumeric as cpn +from legate.timing import time +from sklearn.datasets import fetch_openml +from sklearn.metrics import accuracy_score +from legate_dataframe.lib.replace import replace_nulls +from legate_dataframe.lib.core.table import LogicalTable +from legate_dataframe.lib.core.column import LogicalColumn + +rt = lg.get_legate_runtime() + +# import data +data = fetch_openml(data_id= 46929, as_frame=True) +xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas +df = xd.DataFrame(data.data, columns=data.feature_names) +df['Target'] = data.target + +# covert to logicalTable +if cp.cuda.runtime.getDeviceCount() > 0: + ldf = LogicalTable.from_cudf(df) +else: + df = pa.Table.from_pandas(df) + ldf = LogicalTable.from_arrow(df) + +#replace nulls +median_salary = df["MonthlyIncome"].median() +median_dependents = df["NumberOfDependents"].median() +mmi = LogicalColumn(replace_nulls(LogicalColumn(ldf["MonthlyIncome"]),median_salary)) +mnd = LogicalColumn(replace_nulls(LogicalColumn(ldf["NumberOfDependents"]),median_dependents)) + +#create a new logical Table with updated columns +features = ldf.get_column_names() + +nldf = LogicalTable( [ ldf[0], ldf[1], ldf[2], ldf[3], mmi, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], features) + +# covert to cupynumeric array +data_arr = nldf.to_array() +print(type(data_arr)) +#print(f"\n The dataprocessing time for creditscore exp is: {(e - st)/1000:.6f}ms") + +x = data_arr[:, :-1] # all columns except last +y = data_arr[:, -1] + +#splitting the data into training and testing +num_samples = x.shape[0] +split_ratio = 0.8 +split_index = int(num_samples * split_ratio) + +x_train = x[:split_index] +y_train = y[:split_index] +x_test = x[split_index:] +y_test = y[split_index:] + +rt.issue_execution_fence() +start=time() +# Create model and trian it +model = lb.LBClassifier(base_models=(lb.models.Tree(max_depth=2),lb.models.NN(max_iter=2,hidden_layer_sizes=(10,),verbose=True))).fit(x_train,y_train) +rt.issue_execution_fence() +end=time() + +#predict +predictions = model.predict(x_test) +print(type(predictions)) + +#evalution +acc = accuracy_score(y_test, predictions) +print("Accuracy:", acc) + +print(f"\n The training time for creditscore exp is: {(end - start)/1000:.6f}ms") + +#model save +dump(model, "legate_boost_model.joblib") + +x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) +y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) + +# Save as CSV for easy inspection +pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) +pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) diff --git a/docs/source/housing.py b/docs/source/housing.py new file mode 100644 index 00000000..f7d616e7 --- /dev/null +++ b/docs/source/housing.py @@ -0,0 +1,51 @@ + +from sklearn.datasets import fetch_california_housing +from sklearn.model_selection import train_test_split +from sklearn.metrics import mean_squared_error +import cupynumeric as cn +import legateboost as lb +from legate.timing import time +from joblib import dump +import numpy as np +import pandas as pd + +# Load data +data = fetch_california_housing() +X_train, X_test, y_train, y_test = train_test_split( + data.data, data.target, test_size=0.2, random_state=42 +) + +# Create and fit Legate Boost regressor +model = lb.LBRegressor( + n_estimators=100, + base_models=(lb.models.Tree(max_depth=5),), + objective="squared_error", + learning_rate=0.1, + verbose=True, +) + +start = time() +model.fit(X_train, y_train) +end = time() + +# Predict +istart = time() +y_pred = model.predict(X_test) +iend = time() + +# Evaluate +mse = mean_squared_error(y_test, y_pred) +print(f"Test MSE: {mse:.4f}") +print(f"\nThe training time for housing exp is: {(end - start)/1000:.6f} ms") +print(f"\nThe inference time for housing exp is {(iend - istart)/1000:.6f} ms") + +# Save model +dump(model, "legate_boost_housing.joblib") + +# Convert test data to CPU if needed +x_test_cpu = X_test.get() if hasattr(X_test, "get") else np.array(X_test) +y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) + +# Save as CSV for easy inspection +pd.DataFrame(x_test_cpu).to_csv("x_test_housing.csv", index=False) +pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test_housing.csv", index=False) From ae7aa38fe72231036fb3c641b401082195972c26 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 18:18:31 -0400 Subject: [PATCH 11/72] Add files via upload --- docs/source/creditscore.py | 93 ++++++++++++++++++++++++++------------ 1 file changed, 65 insertions(+), 28 deletions(-) diff --git a/docs/source/creditscore.py b/docs/source/creditscore.py index 12670d18..3d4e75c2 100644 --- a/docs/source/creditscore.py +++ b/docs/source/creditscore.py @@ -14,39 +14,60 @@ rt = lg.get_legate_runtime() -# import data -data = fetch_openml(data_id= 46929, as_frame=True) +# ---------------------------- +# Import data +# ---------------------------- +data = fetch_openml(data_id=46929, as_frame=True) xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas df = xd.DataFrame(data.data, columns=data.feature_names) df['Target'] = data.target -# covert to logicalTable +# ---------------------------- +# Convert to LogicalTable +# ---------------------------- if cp.cuda.runtime.getDeviceCount() > 0: - ldf = LogicalTable.from_cudf(df) + ldf = LogicalTable.from_cudf(df) else: - df = pa.Table.from_pandas(df) - ldf = LogicalTable.from_arrow(df) + df = pa.Table.from_pandas(df) + ldf = LogicalTable.from_arrow(df) -#replace nulls +# ---------------------------- +# Replace nulls +# ---------------------------- median_salary = df["MonthlyIncome"].median() -median_dependents = df["NumberOfDependents"].median() -mmi = LogicalColumn(replace_nulls(LogicalColumn(ldf["MonthlyIncome"]),median_salary)) -mnd = LogicalColumn(replace_nulls(LogicalColumn(ldf["NumberOfDependents"]),median_dependents)) - -#create a new logical Table with updated columns +median_dependents = df["NumberOfDependents"].median() + +mmi = LogicalColumn( + replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary) +) +mnd = LogicalColumn( + replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) +) + +# ---------------------------- +# Create a new LogicalTable with updated columns +# ---------------------------- features = ldf.get_column_names() - -nldf = LogicalTable( [ ldf[0], ldf[1], ldf[2], ldf[3], mmi, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], features) - -# covert to cupynumeric array +nldf = LogicalTable( + [ + ldf[0], ldf[1], ldf[2], ldf[3], mmi, + ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10] + ], + features +) + +# ---------------------------- +# Convert to cuPyNumeric array +# ---------------------------- data_arr = nldf.to_array() print(type(data_arr)) -#print(f"\n The dataprocessing time for creditscore exp is: {(e - st)/1000:.6f}ms") x = data_arr[:, :-1] # all columns except last y = data_arr[:, -1] -#splitting the data into training and testing +# ---------------------------- +# Splitting the data into training and testing +# ---------------------------- num_samples = x.shape[0] split_ratio = 0.8 split_index = int(num_samples * split_ratio) @@ -56,29 +77,45 @@ x_test = x[split_index:] y_test = y[split_index:] +# ---------------------------- +# Training +# ---------------------------- rt.issue_execution_fence() -start=time() -# Create model and trian it -model = lb.LBClassifier(base_models=(lb.models.Tree(max_depth=2),lb.models.NN(max_iter=2,hidden_layer_sizes=(10,),verbose=True))).fit(x_train,y_train) +start = time() + +model = lb.LBClassifier( + base_models=( + lb.models.Tree(max_depth=2), + lb.models.NN(max_iter=2, hidden_layer_sizes=(10,), verbose=True) + ) +).fit(x_train, y_train) + rt.issue_execution_fence() -end=time() +end = time() -#predict +# ---------------------------- +# Prediction +# ---------------------------- predictions = model.predict(x_test) print(type(predictions)) -#evalution +# ---------------------------- +# Evaluation +# ---------------------------- acc = accuracy_score(y_test, predictions) print("Accuracy:", acc) +print(f"\nThe training time for creditscore exp is: {(end - start)/1000:.6f} ms") -print(f"\n The training time for creditscore exp is: {(end - start)/1000:.6f}ms") - -#model save +# ---------------------------- +# Save model +# ---------------------------- dump(model, "legate_boost_model.joblib") +# ---------------------------- +# Save test data +# ---------------------------- x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) -# Save as CSV for easy inspection pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) From 082c070d534c1206c65bed8e9ca6f8813aa5f22b Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 18:20:38 -0400 Subject: [PATCH 12/72] Update creditscore.py --- docs/source/creditscore.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/creditscore.py b/docs/source/creditscore.py index 3d4e75c2..445a8c1b 100644 --- a/docs/source/creditscore.py +++ b/docs/source/creditscore.py @@ -85,8 +85,8 @@ model = lb.LBClassifier( base_models=( - lb.models.Tree(max_depth=2), - lb.models.NN(max_iter=2, hidden_layer_sizes=(10,), verbose=True) + lb.models.Tree(max_depth=8), + lb.models.NN(max_iter=10, hidden_layer_sizes=(10,10), verbose=True) ) ).fit(x_train, y_train) From 33876e7f0dad99abcaecf22306e3bfd0ccac59eb Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 21 Aug 2025 18:23:25 -0400 Subject: [PATCH 13/72] Update housing.py --- docs/source/housing.py | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/docs/source/housing.py b/docs/source/housing.py index f7d616e7..b495d6a7 100644 --- a/docs/source/housing.py +++ b/docs/source/housing.py @@ -9,13 +9,17 @@ import numpy as np import pandas as pd -# Load data +# --------------------------- +# Import data +# --------------------------- data = fetch_california_housing() X_train, X_test, y_train, y_test = train_test_split( data.data, data.target, test_size=0.2, random_state=42 ) +# --------------------------- # Create and fit Legate Boost regressor +# --------------------------- model = lb.LBRegressor( n_estimators=100, base_models=(lb.models.Tree(max_depth=5),), @@ -28,24 +32,31 @@ model.fit(X_train, y_train) end = time() -# Predict +# --------------------------- +# Prediction +# --------------------------- istart = time() y_pred = model.predict(X_test) iend = time() -# Evaluate +# --------------------------- +# Evaluation +# --------------------------- mse = mean_squared_error(y_test, y_pred) print(f"Test MSE: {mse:.4f}") print(f"\nThe training time for housing exp is: {(end - start)/1000:.6f} ms") print(f"\nThe inference time for housing exp is {(iend - istart)/1000:.6f} ms") +# --------------------------- # Save model +# --------------------------- dump(model, "legate_boost_housing.joblib") -# Convert test data to CPU if needed +# ---------------------------- +# Save test data +# ---------------------------- x_test_cpu = X_test.get() if hasattr(X_test, "get") else np.array(X_test) y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) -# Save as CSV for easy inspection pd.DataFrame(x_test_cpu).to_csv("x_test_housing.csv", index=False) pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test_housing.csv", index=False) From 7f9e4cff06ee317e79c0841a3a6694479a659c4d Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 25 Aug 2025 16:56:12 -0400 Subject: [PATCH 14/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index 86ef30f2..85624a2b 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -57,8 +57,8 @@ Quick installation and setup conda install -c conda-forge cmake>=3.26.4 # build wrappers - ~/.conda/envs/legate-boost-new/mpi-wrapper/build-mpi-wrapper.sh - ~/.conda/envs/legate-boost-new/gex-wrapper/build-gex-wrapper.sh + ~/.conda/envs/legate-boost/mpi-wrapper/build-mpi-wrapper.sh + ~/.conda/envs/legate-boost/gex-wrapper/build-gex-wrapper.sh # reactivate env conda activate legate-boost From 0477f49971c2395664ea61ec0fb5c7174458fc4c Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 25 Aug 2025 17:39:46 -0400 Subject: [PATCH 15/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index 85624a2b..cd68ebbf 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -5,9 +5,10 @@ Legate Boost ============= -This article assumes familiarity with the basic usage of gradient -boosting libraries such as XGBoost or LightGBM, as well as cuPyNumeric -for GPU-accelerated array computations. +This article assumes familiarity with the basic usage of gradient boosting +libraries such as XGBoost or LightGBM, as well as cuPyNumeric for GPU-accelerated +array computations. In this tutorial, these libraries are used for efficient model +training, large-scale data handling, and accelerating computations across CPUs and GPUs. What is legate boost? ===================== From efce19766e7e9c6d122312883237c2d0a7ac1400 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 25 Aug 2025 17:57:42 -0400 Subject: [PATCH 16/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 149 +++++++++++++++++------------------ 1 file changed, 73 insertions(+), 76 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index cd68ebbf..6b4607f5 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -6,7 +6,7 @@ Legate Boost ============= This article assumes familiarity with the basic usage of gradient boosting -libraries such as XGBoost or LightGBM, as well as cuPyNumeric for GPU-accelerated +libraries such as ``XGBoost`` or ``LightGBM``, as well as ``cupynumeric`` for GPU-accelerated array computations. In this tutorial, these libraries are used for efficient model training, large-scale data handling, and accelerating computations across CPUs and GPUs. @@ -14,29 +14,29 @@ What is legate boost? ===================== In scenarios where high-performance training is needed across large -datasets or distributed hardware, Legate Boost offers a scalable -alternative. Legate Boost is an advanced gradient boosting library built -on the Legate and Legion parallel programming frameworks. Unlike -traditional boosting libraries such as XGBoost or LightGBM, Legate Boost -provides a unified infrastructure that seamlessly scales across CPUs and -GPUs, supporting both single-node and distributed training while -integrating naturally with cuPyNumeric workflows for efficient +datasets or distributed hardware, ``Legate Boost`` offers a scalable +alternative. ``Legate Boost`` is an advanced gradient boosting library built +on the ``Legate`` and Legion parallel programming frameworks. Unlike +traditional boosting libraries such as ``XGBoost`` or ``LightGBM``, ``Legate Boost`` +provides a unified infrastructure that seamlessly scales across ``CPU's`` and +``GPU's``, supporting both single-node and distributed training while +integrating naturally with ``cupynumeric`` workflows for efficient end-to-end data processing. It enables users to define not only conventional boosted decision trees but also hybrid ensembles combining trees, kernel ridge regression, linear models, or neural networks, all written in Python with minimal code changes. These models are automatically parallelized and executed efficiently -without manual data movement or partitioning. Legate Boost emphasizes +without manual data movement or partitioning. ``Legate Boost`` emphasizes architectural simplicity, extensibility, and performance, delivering state-of-the-art results on tabular data while leveraging the full computational power of modern heterogeneous hardware. -Please refer to `Distributed Computing with cuPyNumeric`_ +Please refer to `Distributed Computing with cupynumeric`_ and `Legate boost`_ for more information and detailed instructions on installation. -.. _Distributed Computing with cuPyNumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cuPyNumeric.ipynb +.. _Distributed Computing with cupynumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cupynumeric.ipynb .. _Legate boost: https://github.com/rapidsai/legate-boost/tree/main @@ -71,21 +71,21 @@ Quick installation and setup Usage ===== -Legate Boost offers two main estimator types: +``Legate Boost`` offers two main estimator types: -- LBRegressor for regression tasks -- LBClassifier for classification tasks +- ``LBRegressor`` for regression tasks +- ``LBClassifier`` for classification tasks -These estimators follow a similar interface to those in XGboost, +These estimators follow a similar interface to those in ``XGboost``, making them easy to integrate into existing machine learning pipelines. Regression with LBRegressor --------------------------- -The LBRegressor estimator is used to predict continuous values such as +The ``LBRegressor`` estimator is used to predict continuous values such as house prices, temperature, or sales forecasting. The following code -demonstrates how to create an instance of the LBRegressor model, use the -fit() function to train it on a dataset, and then apply the predict() +demonstrates how to create an instance of the ``LBRegressor`` model, use the +``fit()`` function to train it on a dataset, and then apply the ``predict()`` function to generate predictions on new data. Here’s how to set it up: .. code-block:: python @@ -112,14 +112,14 @@ function to generate predictions on new data. Here’s how to set it up: In this example: ~~~~~~~~~~~~~~~~ -- LBRegressor is initialized with 100 boosting estimators. -- The fit() method trains the model using the input features (X_train) +- ``LBRegressor`` is initialized with 100 boosting estimators. +- The ``fit()`` method trains the model using the input features (X_train) and target values (y_train). -- After training, the predict() method is used to make predictions on +- After training, the ``predict()`` method is used to make predictions on the test set (X_test). This represents a typical workflow for applying a regression model using -Legate Boost. The LBRegressor estimator offers several configurable +``Legate Boost``. The ``LBRegressor`` estimator offers several configurable options, such as base_model and learning_rate, to help optimize model performance. For a comprehensive list of features and parameters, refer to the `official documentation`_. @@ -129,13 +129,13 @@ to the `official documentation`_. Classification with LBClassifier --------------------------------- -The LBClassifier is designed for predicting categorical outcomes and +The ``LBClassifier`` is designed for predicting categorical outcomes and supports both binary and multi-class classification tasks. It is ideal for a wide range of applications, including spam detection, image classification, and sentiment analysis. The example below demonstrates how to implement a classification model -using the LBClassifier estimator from Legate Boost: +using the ``LBClassifier`` estimator from ``Legate Boost``: .. code-block:: python @@ -158,14 +158,14 @@ using the LBClassifier estimator from Legate Boost: In this example: ~~~~~~~~~~~~~~~~ -- LBClassifier(n_estimators=50) sets up a classifier with 50 boosting +- ``LBClassifier`` (n_estimators=50) sets up a classifier with 50 boosting rounds. -- fit(X_train, y_train) learns the patterns from your training dataset. +- ``fit(X_train, y_train)`` learns the patterns from your training dataset. -- predict(X_test) outputs predicted class labels for the test dataset. +- ``predict(X_test)`` outputs predicted class labels for the test dataset. -Just like the regressor, the LBClassifier follows a clean and intuitive +Just like the regressor, the ``LBClassifier`` follows a clean and intuitive workflow. It provides additional options and advanced configurations to optimize model performance. For more detailed information, refer to the Legate Boost `estimators`_ documentation. @@ -175,7 +175,7 @@ Legate Boost `estimators`_ documentation. Example 1 ========= -Here is an example of using Legate Boost to build a regression model on +Here is an example of using ``Legate Boost`` to build a regression model on the California housing dataset. It showcases key features like scalable training across GPUs/nodes, customizable base models, and adjustable learning rates. @@ -193,19 +193,19 @@ predictors such as: - Population and household count - Latitude and longitude -The target variable is the **median house value** in that block group. +The target variable is the median house value in that block group. This dataset is often used to illustrate regression techniques and assess predictive performance on real-world tabular data. About this implementation ------------------------- -The following code creates a Legate Boost regression model using -LBRegressor, which trains a gradient boosting model optimized for +The following code creates a ``Legate Boost`` regression model using +``LBRegressor``, which trains a gradient boosting model optimized for multi-GPU and multi-node environments. The model is configured to use 100 boosting rounds (n_estimators=100), with each round adding a decision tree (lb.models.Tree) limited to a maximum depth of 5. The loss -function is set to "squared_error", suitable for regression tasks as it +function is set to squared_error, suitable for regression tasks as it minimizes mean squared error. A learning_rate of 0.1 controls how much each tree contributes to the final prediction, balancing speed and stability. The verbose=True flag enables logging during training, @@ -246,22 +246,22 @@ Code module This simple example demonstrates how to train a regression model on the -California Housing dataset using Legate Boost. Although the code looks -similar to standard XGBoost, Legate Boost automatically enables -multi-GPU and multi-node computation. Legate Boost achieves multi-GPU +california housing dataset using ``Legate Boost``. Although the code looks +similar to standard ``XGBoost``, Legate Boost automatically enables +multi-GPU and multi-node computation. ``Legate Boost`` achieves multi-GPU and multi-node scaling through its integration with cupynumeric and the Legion runtime. Unlike traditional GPU libraries that allocate data to a -single device, cupynumeric creates Logical Arrays and abstract -representations of the data that are not bound to one GPU. The Legate -automatically partitions these logical arrays into physical chunks and +single device, ``cupynumeric`` creates ``logical arrays`` and abstract +representations of the data that are not bound to one GPU. The ``Legate`` +automatically partitions these ``logical arrays`` into physical chunks and maps them across all available GPUs and nodes. During training, operations such as histogram building, gradient computation, and tree construction are expressed as parallel tasks. -Legate schedules these tasks close to where the data resides, minimizing +``Legate`` schedules these tasks close to where the data resides, minimizing communication overhead. When synchronization is needed (e.g., to combine -histograms from multiple GPUs), it is handled by legate-mpi-wrapper and -realm-gex-wrapper, so we never have to write MPI or manage explicit GPU +histograms from multiple GPUs), it is handled by ``legate-mpi-wrapper`` and +``realm-gex-wrapper``, so we never have to write MPI or manage explicit GPU memory transfers. Running on CPU and GPU @@ -298,23 +298,22 @@ This produces the following output: The training time for housing exp is: 846.949000 milliseconds -**To Do: Multi Node and Multi GPU** Example 2 ========= -This example demonstrates how Legate Boost can be applied to the *“Give -Me Some Credit”* dataset (OpenML data_id: 46929) to build a +This example demonstrates how Legate Boost can be applied to the ``Give +Me Some Credit`` dataset (OpenML data_id: 46929) to build a classification model using ensemble learning by combining different -model types. It also highlights the integration of Legate DataFrame with -Legate Boost to enable distributed training across multi-GPU and +model types. It also highlights the integration of ``Legate Dataframe`` with +``Legate Boost`` to enable distributed training across multi-GPU and multi-node environments, showcasing scalable machine learning on the -Credit Score dataset. +credit score dataset. About the dataset ----------------- -The Give Me Some Credit dataset is a financial risk prediction dataset +The ``Give Me Some Credit`` dataset is a financial risk prediction dataset originally introduced in a Kaggle competition. It includes anonymized credit and demographic data for individuals, with the goal of predicting whether a person is likely to experience serious financial distress @@ -336,7 +335,7 @@ indicating the likelihood of future financial trouble. About this implementation ------------------------- -This implementation will focus on demonstrating Legate Boost’s flexible +This implementation will focus on demonstrating ``Legate Boost’s`` flexible model ensembling capabilities, specifically: - Tree-based gradient boosting models, ideal for structured/tabular @@ -344,7 +343,7 @@ model ensembling capabilities, specifically: - Neural network-based classifiers, allowing hybrid or deep learning approaches. -By leveraging Legate Boost, we can ensemble these two models and +By leveraging ``Legate Boost``, we can ensemble these two models and efficiently train and evaluate both model types on GPUs or CPUs, showcasing scalable performance for large tabular datasets in financial risk prediction. @@ -353,8 +352,8 @@ The pipeline begins with importing required libraries and its functions and also loading the dataset using fetch_openml. Depending on hardware availability, the data is initially handled either with cuDF (for GPU execution) or pandas (for CPU execution). The dataset is then wrapped -into a LogicalTable, the distributed data representation used by Legate -DataFrame. LogicalTables internally break data into logical columns, +into a ``LogicalTable``, the distributed data representation used by ``Legate +Dataframe``. ``LogicalTables`` internally break data into ``logical columns``, enabling Legate’s runtime to partition, distribute, and schedule computations across multiple GPUs and nodes. @@ -389,13 +388,13 @@ computations across multiple GPUs and nodes. ldf = LogicalTable.from_arrow(df) Let’s see how data preprocessing is performed directly on the -LogicalTable. Missing values in key columns (MonthlyIncome and +``LogicalTable``. Missing values in key columns (MonthlyIncome and NumberOfDependents) are filled using median imputation through the replace_nulls operation. These operations are executed in parallel -across distributed partitions of the LogicalTable, avoiding centralized -bottlenecks. Because LogicalTables are immutable, a new LogicalTable +across distributed partitions of the ``LogicalTable``, avoiding centralized +bottlenecks. Because ``LogicalTable's`` are immutable, a new ``LogicalTable`` with updated LogicalColumn’s is created after preprocessing. The cleaned -data is then converted into a cuPyNumeric array, Legate’s +data is then converted into a cupynumeric array, Legate’s GPU-accelerated array type that leverages logical partitioning for distributed computation. This enables the subsequent machine learning tasks to execute efficiently across multiple GPUs or nodes. @@ -419,13 +418,13 @@ tasks to execute efficiently across multiple GPUs or nodes. [ldf[0], ldf[1], ldf[2], ldf[3], mni, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], features ) - # convert to cuPyNumeric + # convert to cupynumeric data_arr = nldf.to_array() -As we have a data_arr backed by cuPyNumeric, we first split the dataset -into training and testing subsets, which are then passed to Legate Boost +As we have a data_arr backed by ``cupynumeric``, we first split the dataset +into training and testing subsets, which are then passed to ``Legate Boost`` for efficient training across available hardware resources. The model is -built using Legate Boost’s ensemble framework (LBClassifier), which +built using ``Legate Boost’s`` ensemble framework (LBClassifier), which allows combining multiple types of base learners into a single unified model. @@ -435,7 +434,7 @@ non-linear decision boundaries by splitting the feature space hierarchically up to 8 levels deep, and a Neural Network (lb.models.NN) with two hidden layers of 10 neurons each (hidden_layer_sizes=(10,10)), trained for max_iter=10 epochs with verbose=True to monitor progress. By -combining a tree-based model with a neural network, Legate Boost +combining a tree-based model with a neural network, ``Legate Boost`` leverages the interpretability and rule-based decision-making of trees together with the ability of neural networks to model intricate, high-dimensional relationships. This ensemble design results in a more @@ -466,7 +465,7 @@ individually. end = time() The trained ensemble model is used to generate predictions on the test -set, and its accuracy is evaluated using accuracy_score. Finally, the +set, and its accuracy is evaluated using ``accuracy_score``. Finally, the model is saved with Joblib for future inference without retraining. .. code-block:: python @@ -492,9 +491,9 @@ model is saved with Joblib for future inference without retraining. pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) -This workflow illustrates how Legate DataFrame provides a scalable -preprocessing layer, cupynumeric arrays enable distributed GPU -computation, and Legate Boost delivers a flexible ensemble learning +This workflow illustrates how ``Legate Dataframe`` provides a scalable +preprocessing layer, ``cupynumeric`` arrays enable distributed GPU +computation, and ``Legate Boost`` delivers a flexible ensemble learning framework capable of leveraging multi-node, multi-GPU infrastructure efficiently. @@ -536,13 +535,13 @@ This produces the following output: Inference performance ===================== -Let’s explore how cuPyNumeric can be leveraged to measure inference +Let’s explore how ``cupynumeric`` can be leveraged to measure inference performance statistics seamlessly across both CPU and GPU all without modifying the code. In this example, we evaluate a pre-trained machine learning model by calculating key metrics such as mean, median, minimum, maximum, variance, and standard deviation of inference times. The model is loaded using joblib, and predictions are executed multiple times on -the test dataset. By utilizing cuPyNumeric arrays, the timing results +the test dataset. By utilizing ``cupynumeric`` arrays, the timing results are efficiently processed while ensuring compatibility with both CPU and GPU environments. This approach provides a simple yet powerful way to compare inference performance across hardware, offering clear insights @@ -634,14 +633,12 @@ This produces the following output: Standard deviation: 1.34 ms These results clearly show the performance benefits of running inference -on a GPU compared to a CPU using cuPyNumeric arrays. On the CPU, the -model achieved a mean inference time of approximately **265.66 ms**, -with relatively low variability (standard deviation ~\ **10.83 ms**). In +on a GPU compared to a CPU using ``cupynumeric`` arrays. On the CPU, the +model achieved a mean inference time of approximately 265.66 ms, +with relatively low variability (standard deviation ~\ 10.83 ms). In contrast, the GPU significantly reduced the mean inference time to -around **122.35 ms**, representing more than a **2x speedup**, with even -lower variability (standard deviation ~\ **1.34 ms**). This highlights -how cuPyNumeric enables the same code to seamlessly scale across CPU and +around 122.35 ms, representing more than a 2x speedup, with even +lower variability (standard deviation ~\ 1.34 ms). This highlights +how ``cupynumeric`` enables the same code to seamlessly scale across CPU and GPU, allowing both accurate performance benchmarking and efficient model deployment across heterogeneous hardware. - -**To Do: Multi Node and Multi GPU** From cdbff4b51da55c768d598c70234f6073ff47bad1 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 25 Aug 2025 18:15:19 -0400 Subject: [PATCH 17/72] Update housing.py --- docs/source/housing.py | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/docs/source/housing.py b/docs/source/housing.py index b495d6a7..2f63884d 100644 --- a/docs/source/housing.py +++ b/docs/source/housing.py @@ -46,17 +46,3 @@ print(f"Test MSE: {mse:.4f}") print(f"\nThe training time for housing exp is: {(end - start)/1000:.6f} ms") print(f"\nThe inference time for housing exp is {(iend - istart)/1000:.6f} ms") - -# --------------------------- -# Save model -# --------------------------- -dump(model, "legate_boost_housing.joblib") - -# ---------------------------- -# Save test data -# ---------------------------- -x_test_cpu = X_test.get() if hasattr(X_test, "get") else np.array(X_test) -y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) - -pd.DataFrame(x_test_cpu).to_csv("x_test_housing.csv", index=False) -pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test_housing.csv", index=False) From c11d94114130da2023895e9374a5c7f1254300e7 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 25 Aug 2025 18:18:51 -0400 Subject: [PATCH 18/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index 6b4607f5..15181635 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -245,6 +245,9 @@ Code module print(f"Training time: {(end - start)/1000:.6f} ms") +.. literalinclude:: housing.py + :language: python + This simple example demonstrates how to train a regression model on the california housing dataset using ``Legate Boost``. Although the code looks similar to standard ``XGBoost``, Legate Boost automatically enables @@ -387,6 +390,9 @@ computations across multiple GPUs and nodes. df = pa.Table.from_pandas(df) ldf = LogicalTable.from_arrow(df) +.. literalinclude:: creditscore.py + :language: python + Let’s see how data preprocessing is performed directly on the ``LogicalTable``. Missing values in key columns (MonthlyIncome and NumberOfDependents) are filled using median imputation through the From a832d9521a3ae9d113136f523a681b1282984ac2 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 25 Aug 2025 19:47:22 -0400 Subject: [PATCH 19/72] Update creditscore.py --- docs/source/creditscore.py | 54 ++++++++++++++------------------------ 1 file changed, 20 insertions(+), 34 deletions(-) diff --git a/docs/source/creditscore.py b/docs/source/creditscore.py index 445a8c1b..3441535c 100644 --- a/docs/source/creditscore.py +++ b/docs/source/creditscore.py @@ -1,3 +1,4 @@ +# [import-libraries] import cudf import pandas import cupy as cp @@ -14,26 +15,22 @@ rt = lg.get_legate_runtime() -# ---------------------------- -# Import data -# ---------------------------- +# [import-data] data = fetch_openml(data_id=46929, as_frame=True) xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas df = xd.DataFrame(data.data, columns=data.feature_names) df['Target'] = data.target -# ---------------------------- -# Convert to LogicalTable -# ---------------------------- +# [convert-to-LogicalTable] if cp.cuda.runtime.getDeviceCount() > 0: ldf = LogicalTable.from_cudf(df) else: df = pa.Table.from_pandas(df) ldf = LogicalTable.from_arrow(df) + +# [covert-to-LogicalTable-end] -# ---------------------------- -# Replace nulls -# ---------------------------- +# [Replace nulls] median_salary = df["MonthlyIncome"].median() median_dependents = df["NumberOfDependents"].median() @@ -44,9 +41,8 @@ replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) ) -# ---------------------------- -# Create a new LogicalTable with updated columns -# ---------------------------- +# [Create-new-LogicalTable-with-updated-columns] + features = ldf.get_column_names() nldf = LogicalTable( [ @@ -56,18 +52,17 @@ features ) -# ---------------------------- -# Convert to cuPyNumeric array -# ---------------------------- +# [Convert-to-cupynumeric-array] + data_arr = nldf.to_array() -print(type(data_arr)) +# [convert-to-cupynumeric-array-end] + +# [preparing-data-for-training-and-testing] x = data_arr[:, :-1] # all columns except last y = data_arr[:, -1] -# ---------------------------- -# Splitting the data into training and testing -# ---------------------------- +# [Splitting the data into training and testing] num_samples = x.shape[0] split_ratio = 0.8 split_index = int(num_samples * split_ratio) @@ -77,9 +72,7 @@ x_test = x[split_index:] y_test = y[split_index:] -# ---------------------------- -# Training -# ---------------------------- +# [training] rt.issue_execution_fence() start = time() @@ -92,28 +85,21 @@ rt.issue_execution_fence() end = time() +# [training-end] -# ---------------------------- -# Prediction -# ---------------------------- +# [Prediction] predictions = model.predict(x_test) print(type(predictions)) -# ---------------------------- -# Evaluation -# ---------------------------- +# [Evaluation] acc = accuracy_score(y_test, predictions) print("Accuracy:", acc) print(f"\nThe training time for creditscore exp is: {(end - start)/1000:.6f} ms") -# ---------------------------- -# Save model -# ---------------------------- +# [Save model] dump(model, "legate_boost_model.joblib") -# ---------------------------- -# Save test data -# ---------------------------- +# [ Save test data [ x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) From a1d6397df64053f7930c574e6224babc152ac06a Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 25 Aug 2025 19:53:38 -0400 Subject: [PATCH 20/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 180 ++++++++++++++++++++--------------- 1 file changed, 104 insertions(+), 76 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index 15181635..5ddb6cb8 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -362,36 +362,40 @@ computations across multiple GPUs and nodes. .. code-block:: python - import cudf - import pandas - import cupy as cp - import pyarrow as pa - import legate_dataframe - import legateboost as lb - import cupynumeric as cpn - from legate.timing import time - from sklearn.datasets import fetch_openml - from sklearn.metrics import accuracy_score - from legate_dataframe.lib.replace import replace_nulls - from legate_dataframe.lib.core.table import LogicalTable - from legate_dataframe.lib.core.column import LogicalColumn - - # load dataset - data = fetch_openml(data_id=46929, as_frame=True) - - xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas - df = xd.DataFrame(data.data, columns=data.feature_names) - df['Target'] = data.target - - # convert to LogicalTable - if cp.cuda.runtime.getDeviceCount() > 0: - ldf = LogicalTable.from_cudf(df) - else: - df = pa.Table.from_pandas(df) - ldf = LogicalTable.from_arrow(df) + # [import-libraries] + import cudf + import pandas + import cupy as cp + import pyarrow as pa + import legate_dataframe + import legateboost as lb + import cupynumeric as cpn + from legate.timing import time + from sklearn.datasets import fetch_openml + from sklearn.metrics import accuracy_score + from legate_dataframe.lib.replace import replace_nulls + from legate_dataframe.lib.core.table import LogicalTable + from legate_dataframe.lib.core.column import LogicalColumn + + rt = lg.get_legate_runtime() + + # [import-data] + data = fetch_openml(data_id=46929, as_frame=True) + xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas + df = xd.DataFrame(data.data, columns=data.feature_names) + df['Target'] = data.target + + # [convert-to-LogicalTable] + if cp.cuda.runtime.getDeviceCount() > 0: + ldf = LogicalTable.from_cudf(df) + else: + df = pa.Table.from_pandas(df) + ldf = LogicalTable.from_arrow(df) .. literalinclude:: creditscore.py :language: python + :start-after: [import-libraries] + :end-before: [covert-to-LogicalTable-end] Let’s see how data preprocessing is performed directly on the ``LogicalTable``. Missing values in key columns (MonthlyIncome and @@ -407,25 +411,35 @@ tasks to execute efficiently across multiple GPUs or nodes. .. code-block:: python - # median imputation - median_salary = df["MonthlyIncome"].median() - median_dependents = df["NumberOfDependents"].median() + # [Replace nulls] + median_salary = df["MonthlyIncome"].median() + median_dependents = df["NumberOfDependents"].median() + + mmi = LogicalColumn( + replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary) + ) + mnd = LogicalColumn( + replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) + ) + + # [Create-new-LogicalTable-with-updated-columns] + + features = ldf.get_column_names() + nldf = LogicalTable( + [ + ldf[0], ldf[1], ldf[2], ldf[3], mmi, + ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10] + ], + features + ) + + # [Convert-to-cupynumeric-array] + data_arr = nldf.to_array() - mni = LogicalColumn( - replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary) - ) - mnd = LogicalColumn( - replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) - ) - - # rebuild logical table - features = ldf.get_column_names() - nldf = LogicalTable( - [ldf[0], ldf[1], ldf[2], ldf[3], mni, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], - features ) - - # convert to cupynumeric - data_arr = nldf.to_array() +.. literalinclude:: creditscore.py + :language: python + :start-after: [Replace nulls] + :end-before: [convert-to-cupynumeric-array-end] As we have a data_arr backed by ``cupynumeric``, we first split the dataset into training and testing subsets, which are then passed to ``Legate Boost`` @@ -449,26 +463,38 @@ individually. .. code-block:: python - #preparing data for training and testing - x = data_arr[:, :-1] - y = data_arr[:, -1] - - split_index = int(x.shape[0] * 0.8) - x_train, y_train = x[:split_index], y[:split_index] - x_test, y_test = x[split_index:], y[split_index:] - - start = time() - - # ensemble model - model = lb.LBClassifier( - base_models=( - lb.models.Tree(max_depth=8), - lb.models.NN(max_iter=10, hidden_layer_sizes=(10, 10), verbose=True), - ) - ) - model.fit(x_train, y_train) + # [preparing-data-for-training-and-testing] + x = data_arr[:, :-1] # all columns except last + y = data_arr[:, -1] + + # [Splitting the data into training and testing] + num_samples = x.shape[0] + split_ratio = 0.8 + split_index = int(num_samples * split_ratio) + + x_train = x[:split_index] + y_train = y[:split_index] + x_test = x[split_index:] + y_test = y[split_index:] + + # [training] + rt.issue_execution_fence() + start = time() + + model = lb.LBClassifier( + base_models=( + lb.models.Tree(max_depth=8), + lb.models.NN(max_iter=10, hidden_layer_sizes=(10,10), verbose=True) + ) + ).fit(x_train, y_train) + + rt.issue_execution_fence() + end = time() - end = time() +.. literalinclude:: creditscore.py + :language: python + :start-after: # [preparing-data-for-training-and-testing] + :end-before: # [training-end] The trained ensemble model is used to generate predictions on the test set, and its accuracy is evaluated using ``accuracy_score``. Finally, the @@ -476,27 +502,29 @@ model is saved with Joblib for future inference without retraining. .. code-block:: python - # predict + # [Prediction] predictions = model.predict(x_test) - - # evaluate - from sklearn.metrics import accuracy_score + print(type(predictions)) + + # [Evaluation] acc = accuracy_score(y_test, predictions) print("Accuracy:", acc) - print(f"Training time: {(end - start)/1000:.6f} ms") - - # save model - from joblib import dump + print(f"\nThe training time for creditscore exp is: {(end - start)/1000:.6f} ms") + + # [Save model] dump(model, "legate_boost_model.joblib") - - # save test data for inference - import numpy as np, pandas as pd + + # [ Save test data [ x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) - + pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) +.. literalinclude:: creditscore.py + :language: python + :start-after: # [Prediction] + This workflow illustrates how ``Legate Dataframe`` provides a scalable preprocessing layer, ``cupynumeric`` arrays enable distributed GPU computation, and ``Legate Boost`` delivers a flexible ensemble learning From c84835e38c50a5e8511f55ff904d9e1fc2b8991a Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 25 Aug 2025 19:54:47 -0400 Subject: [PATCH 21/72] Update creditscore.py --- docs/source/creditscore.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/source/creditscore.py b/docs/source/creditscore.py index 3441535c..7c034205 100644 --- a/docs/source/creditscore.py +++ b/docs/source/creditscore.py @@ -1,4 +1,4 @@ -# [import-libraries] +# [import libraries] import cudf import pandas import cupy as cp @@ -15,20 +15,20 @@ rt = lg.get_legate_runtime() -# [import-data] +# [import data] data = fetch_openml(data_id=46929, as_frame=True) xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas df = xd.DataFrame(data.data, columns=data.feature_names) df['Target'] = data.target -# [convert-to-LogicalTable] +# [convert to LogicalTable] if cp.cuda.runtime.getDeviceCount() > 0: ldf = LogicalTable.from_cudf(df) else: df = pa.Table.from_pandas(df) ldf = LogicalTable.from_arrow(df) -# [covert-to-LogicalTable-end] +# [covert to LogicalTable end] # [Replace nulls] median_salary = df["MonthlyIncome"].median() @@ -41,7 +41,7 @@ replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) ) -# [Create-new-LogicalTable-with-updated-columns] +# [Create new LogicalTable with updated columns] features = ldf.get_column_names() nldf = LogicalTable( @@ -52,13 +52,13 @@ features ) -# [Convert-to-cupynumeric-array] +# [Convert to cupynumeric array] data_arr = nldf.to_array() -# [convert-to-cupynumeric-array-end] +# [convert to cupynumeric array end] -# [preparing-data-for-training-and-testing] +# [preparing data for training and testing] x = data_arr[:, :-1] # all columns except last y = data_arr[:, -1] @@ -85,7 +85,7 @@ rt.issue_execution_fence() end = time() -# [training-end] +# [training end] # [Prediction] predictions = model.predict(x_test) From be448326d2ff2850e78aba32f8d838474871bcc5 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 25 Aug 2025 19:56:02 -0400 Subject: [PATCH 22/72] Update housing.py --- docs/source/housing.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/docs/source/housing.py b/docs/source/housing.py index 2f63884d..0467f45f 100644 --- a/docs/source/housing.py +++ b/docs/source/housing.py @@ -1,4 +1,4 @@ - +# [Import libraries] from sklearn.datasets import fetch_california_housing from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error @@ -9,17 +9,13 @@ import numpy as np import pandas as pd -# --------------------------- -# Import data -# --------------------------- +# [Import data] data = fetch_california_housing() X_train, X_test, y_train, y_test = train_test_split( data.data, data.target, test_size=0.2, random_state=42 ) -# --------------------------- -# Create and fit Legate Boost regressor -# --------------------------- +# [Create and fit Legate Boost regressor] model = lb.LBRegressor( n_estimators=100, base_models=(lb.models.Tree(max_depth=5),), @@ -32,16 +28,12 @@ model.fit(X_train, y_train) end = time() -# --------------------------- -# Prediction -# --------------------------- +# [Prediction] istart = time() y_pred = model.predict(X_test) iend = time() -# --------------------------- -# Evaluation -# --------------------------- +# [Evaluation] mse = mean_squared_error(y_test, y_pred) print(f"Test MSE: {mse:.4f}") print(f"\nThe training time for housing exp is: {(end - start)/1000:.6f} ms") From 014ec22fb8768d481e69c3a92a0c2950158eaa0c Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 25 Aug 2025 20:26:00 -0400 Subject: [PATCH 23/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index 5ddb6cb8..41629e1f 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -394,8 +394,8 @@ computations across multiple GPUs and nodes. .. literalinclude:: creditscore.py :language: python - :start-after: [import-libraries] - :end-before: [covert-to-LogicalTable-end] + :start-after: [import libraries] + :end-before: [covert to LogicalTable end] Let’s see how data preprocessing is performed directly on the ``LogicalTable``. Missing values in key columns (MonthlyIncome and @@ -439,7 +439,7 @@ tasks to execute efficiently across multiple GPUs or nodes. .. literalinclude:: creditscore.py :language: python :start-after: [Replace nulls] - :end-before: [convert-to-cupynumeric-array-end] + :end-before: [convert to cupynumeric array end] As we have a data_arr backed by ``cupynumeric``, we first split the dataset into training and testing subsets, which are then passed to ``Legate Boost`` @@ -493,8 +493,8 @@ individually. .. literalinclude:: creditscore.py :language: python - :start-after: # [preparing-data-for-training-and-testing] - :end-before: # [training-end] + :start-after: # [preparing data for training and testing] + :end-before: # [training end] The trained ensemble model is used to generate predictions on the test set, and its accuracy is evaluated using ``accuracy_score``. Finally, the From f81209f7ae4e0d2b18de4f70fe6a25065ac1af72 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Wed, 27 Aug 2025 14:50:26 -0400 Subject: [PATCH 24/72] Add files via upload --- docs/source/LBClassifier.py | 16 ++++++++++++++++ docs/source/LBRegressor.py | 20 ++++++++++++++++++++ 2 files changed, 36 insertions(+) create mode 100644 docs/source/LBClassifier.py create mode 100644 docs/source/LBRegressor.py diff --git a/docs/source/LBClassifier.py b/docs/source/LBClassifier.py new file mode 100644 index 00000000..091515a9 --- /dev/null +++ b/docs/source/LBClassifier.py @@ -0,0 +1,16 @@ +import legateboost as lb +from sklearn.datasets import make_classification +from sklearn.model_selection import train_test_split + +# creating synthetic dataset +X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42) + +# splitting the data +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +# classification model with 50 estimators +classification_model = lb.LBClassifier(n_estimators=50) + +classification_model.fit(X_train, y_train) +y_pred = classification_model.predict(X_test) +print(y_pred) diff --git a/docs/source/LBRegressor.py b/docs/source/LBRegressor.py new file mode 100644 index 00000000..f3c6df60 --- /dev/null +++ b/docs/source/LBRegressor.py @@ -0,0 +1,20 @@ +import legateboost as lb +from sklearn.datasets import make_regression +from sklearn.model_selection import train_test_split + +# creating synthetic dataset +X, y = make_regression(n_samples=100, n_features=4, noise=8, random_state=42) + +# splitting the data +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +# regression model with 100 estimators +regression_model = lb.LBRegressor(n_estimators=100) + +# fit the model +regression_model.fit(X_train, y_train) + +# predict +y_pred = regression_model.predict(X_test) + +print(y_pred) From 60aa0073e46eb481e15420efa090f7f0e76d17d0 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Wed, 27 Aug 2025 14:56:23 -0400 Subject: [PATCH 25/72] Add files via upload --- docs/source/inference.py | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 docs/source/inference.py diff --git a/docs/source/inference.py b/docs/source/inference.py new file mode 100644 index 00000000..8e841732 --- /dev/null +++ b/docs/source/inference.py @@ -0,0 +1,35 @@ +import cupynumeric as cp +from joblib import dump, load +from legate.timing import time +import pandas as pd +import legate.core as lg + +rt = lg.get_legate_runtime() + +timings = [] +model = load("legate_boost_housing.joblib") +X = pd.read_csv("x_test_housing.csv") + +for _ in range(10): + rt.issue_execution_fence() + start = time() + model.predict(X) + rt.issue_execution_fence() + end = time() + timings.append(end - start) + +timings = timings[1:] +timings_gpu = cp.array(timings) +mean_time = cp.mean(timings_gpu) +median_time = cp.median(timings_gpu) +min_time = cp.min(timings_gpu) +max_time = cp.max(timings_gpu) +var_time = cp.var(timings_gpu) +std = cp.sqrt(var_time) + +print(f"Mean: {float(mean_time)/1000:.2f} ms") +print(f"Median: {float(median_time)/1000:.2f} ms") +print(f"Min: {float(min_time)/1000:.2f} ms") +print(f"Max: {float(max_time)/1000:.2f} ms") +print(f"Variance: {float(var_time)/1000:.2f} ms") +print(f"standard deviation: {float(std)/1000:.2f} ms") From adf8d560ca8e290c9d985438fe5539ab4760fcc8 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Wed, 27 Aug 2025 14:56:56 -0400 Subject: [PATCH 26/72] Update inference.py --- docs/source/inference.py | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/inference.py b/docs/source/inference.py index 8e841732..33f12d4f 100644 --- a/docs/source/inference.py +++ b/docs/source/inference.py @@ -20,6 +20,7 @@ timings = timings[1:] timings_gpu = cp.array(timings) + mean_time = cp.mean(timings_gpu) median_time = cp.median(timings_gpu) min_time = cp.min(timings_gpu) From 12de89b6b39e02c4382684c021bb14a00f2b42a8 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 28 Aug 2025 10:37:14 -0400 Subject: [PATCH 27/72] Update LBClassifier.py --- docs/source/LBClassifier.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/source/LBClassifier.py b/docs/source/LBClassifier.py index 091515a9..d0cd81fc 100644 --- a/docs/source/LBClassifier.py +++ b/docs/source/LBClassifier.py @@ -11,6 +11,9 @@ # classification model with 50 estimators classification_model = lb.LBClassifier(n_estimators=50) +# train the model classification_model.fit(X_train, y_train) + +# predictions y_pred = classification_model.predict(X_test) print(y_pred) From aba1e425ab24e375c6445a5a5b14baa8487214db Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 28 Aug 2025 10:43:44 -0400 Subject: [PATCH 28/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 17 ++++------------- 1 file changed, 4 insertions(+), 13 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index 41629e1f..73518357 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -51,23 +51,14 @@ Quick installation and setup # activate env conda activate legate-boost - # install wrappers - conda install -c legate/label/gex-experimental realm-gex-wrapper legate-mpi-wrapper - - # install cmake - conda install -c conda-forge cmake>=3.26.4 - - # build wrappers - ~/.conda/envs/legate-boost/mpi-wrapper/build-mpi-wrapper.sh - ~/.conda/envs/legate-boost/gex-wrapper/build-gex-wrapper.sh - - # reactivate env - conda activate legate-boost - # install legate-dataframe conda install -c legate -c rapidsai -c conda-forge legate-dataframe +for multi-node setup plese refer to `GASnet-based installation`_ + +.. _GASnet-based installation: https://docs.nvidia.com/legate/latest/gasnet.html + Usage ===== From 7a2ac5d5fa2cf68989b8dd6af1b0b4c9f9e66a31 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 28 Aug 2025 10:46:54 -0400 Subject: [PATCH 29/72] Update creditscore.py --- docs/source/creditscore.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/creditscore.py b/docs/source/creditscore.py index 7c034205..e69421ad 100644 --- a/docs/source/creditscore.py +++ b/docs/source/creditscore.py @@ -78,7 +78,7 @@ model = lb.LBClassifier( base_models=( - lb.models.Tree(max_depth=8), + lb.models.Tree(max_depth=5), lb.models.NN(max_iter=10, hidden_layer_sizes=(10,10), verbose=True) ) ).fit(x_train, y_train) From 9d4cd73be3a5b8e117b3e42704839a7072030921 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 28 Aug 2025 10:55:20 -0400 Subject: [PATCH 30/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index 73518357..391a90ca 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -219,7 +219,7 @@ Code module model = lb.LBRegressor( n_estimators=100, - base_models=(lb.models.Tree(max_depth=5),), + base_models=(lb.models.Tree(max_depth=8),), objective="squared_error", learning_rate=0.1, verbose=True From ee0586c303f804ebe5114c68af5c5c6a5ba2f79a Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 28 Aug 2025 10:57:19 -0400 Subject: [PATCH 31/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index 391a90ca..4b21a711 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -274,7 +274,7 @@ This produces the following output: .. code-block:: text - The training time for housing exp is: 7846.303000 milliseconds + The training time for housing exp is: 1742.303000 milliseconds GPU execution @@ -290,7 +290,7 @@ This produces the following output: .. code-block:: text - The training time for housing exp is: 846.949000 milliseconds + The training time for housing exp is: 831.949000 milliseconds Example 2 From 2df0df4d28966a1553521b2c3bd94441e2260fab Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 28 Aug 2025 11:21:54 -0400 Subject: [PATCH 32/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index 4b21a711..dc51f0a6 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -100,6 +100,9 @@ function to generate predictions on new data. Here’s how to set it up: # predict y_pred = regression_model.predict(X_test) +.. literalinclude:: LBRegressor.py + :language: python + In this example: ~~~~~~~~~~~~~~~~ @@ -146,6 +149,9 @@ using the ``LBClassifier`` estimator from ``Legate Boost``: classification_model.fit(X_train, y_train) y_pred = classification_model.predict(X_test) +.. literalinclude:: LBClassifier.py + :language: python + In this example: ~~~~~~~~~~~~~~~~ @@ -484,8 +490,8 @@ individually. .. literalinclude:: creditscore.py :language: python - :start-after: # [preparing data for training and testing] - :end-before: # [training end] + :start-after: [preparing data for training and testing] + :end-before: [training end] The trained ensemble model is used to generate predictions on the test set, and its accuracy is evaluated using ``accuracy_score``. Finally, the @@ -514,7 +520,7 @@ model is saved with Joblib for future inference without retraining. .. literalinclude:: creditscore.py :language: python - :start-after: # [Prediction] + :start-after: [Prediction] This workflow illustrates how ``Legate Dataframe`` provides a scalable preprocessing layer, ``cupynumeric`` arrays enable distributed GPU @@ -613,6 +619,10 @@ into the speedup and variability achieved with GPU acceleration. print(f"Variance: {float(var_time)/1000:.2f} ms") print(f"Standard deviation: {float(std)/1000:.2f} ms") +.. literalinclude:: inference.py + :language: python + + Running on CPU and GPU ---------------------- From fbc339b51ef8c543a67bb44c05a435ba9c84eea5 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 28 Aug 2025 11:41:31 -0400 Subject: [PATCH 33/72] Update Legate_Boost.rst --- docs/source/Legate_Boost.rst | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index dc51f0a6..e3ab5810 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -6,7 +6,7 @@ Legate Boost ============= This article assumes familiarity with the basic usage of gradient boosting -libraries such as ``XGBoost`` or ``LightGBM``, as well as ``cupynumeric`` for GPU-accelerated +libraries such as XGBoost or LightGBM, as well as ``cupynumeric`` for GPU-accelerated array computations. In this tutorial, these libraries are used for efficient model training, large-scale data handling, and accelerating computations across CPUs and GPUs. @@ -17,9 +17,9 @@ In scenarios where high-performance training is needed across large datasets or distributed hardware, ``Legate Boost`` offers a scalable alternative. ``Legate Boost`` is an advanced gradient boosting library built on the ``Legate`` and Legion parallel programming frameworks. Unlike -traditional boosting libraries such as ``XGBoost`` or ``LightGBM``, ``Legate Boost`` -provides a unified infrastructure that seamlessly scales across ``CPU's`` and -``GPU's``, supporting both single-node and distributed training while +traditional boosting libraries such as XGBoost or LightGBM, ``Legate Boost`` +provides a unified infrastructure that seamlessly scales across CPU's and +GPU's, supporting both single-node and distributed training while integrating naturally with ``cupynumeric`` workflows for efficient end-to-end data processing. It enables users to define not only conventional boosted decision trees but also hybrid ensembles combining @@ -67,7 +67,7 @@ Usage - ``LBRegressor`` for regression tasks - ``LBClassifier`` for classification tasks -These estimators follow a similar interface to those in ``XGboost``, +These estimators follow a similar interface to those in XGboost, making them easy to integrate into existing machine learning pipelines. Regression with LBRegressor @@ -247,7 +247,7 @@ Code module This simple example demonstrates how to train a regression model on the california housing dataset using ``Legate Boost``. Although the code looks -similar to standard ``XGBoost``, Legate Boost automatically enables +similar to standard XGBoost, Legate Boost automatically enables multi-GPU and multi-node computation. ``Legate Boost`` achieves multi-GPU and multi-node scaling through its integration with cupynumeric and the Legion runtime. Unlike traditional GPU libraries that allocate data to a @@ -446,9 +446,9 @@ allows combining multiple types of base learners into a single unified model. In this example, the ensemble consists of a Decision Tree -(lb.models.Tree) with max_depth=8, enabling the capture of complex +(lb.models.Tree) with max_depth=5, enabling the capture of complex non-linear decision boundaries by splitting the feature space -hierarchically up to 8 levels deep, and a Neural Network (lb.models.NN) +hierarchically up to 5 levels deep, and a Neural Network (lb.models.NN) with two hidden layers of 10 neurons each (hidden_layer_sizes=(10,10)), trained for max_iter=10 epochs with verbose=True to monitor progress. By combining a tree-based model with a neural network, ``Legate Boost`` @@ -494,7 +494,7 @@ individually. :end-before: [training end] The trained ensemble model is used to generate predictions on the test -set, and its accuracy is evaluated using ``accuracy_score``. Finally, the +set, and its accuracy is evaluated using ``accuracy_score()``. Finally, the model is saved with Joblib for future inference without retraining. .. code-block:: python From 44e09fdc957eb81a88b3b89b8287f408d0795d52 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 28 Aug 2025 11:43:53 -0400 Subject: [PATCH 34/72] Update legate_boost.rst --- docs/source/Legate_Boost.rst | 52 ++++++++++++++++++------------------ 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/docs/source/Legate_Boost.rst b/docs/source/Legate_Boost.rst index e3ab5810..70d83a0f 100644 --- a/docs/source/Legate_Boost.rst +++ b/docs/source/Legate_Boost.rst @@ -2,7 +2,7 @@ ============= -Legate Boost +legate-boost ============= This article assumes familiarity with the basic usage of gradient boosting @@ -10,14 +10,14 @@ libraries such as XGBoost or LightGBM, as well as ``cupynumeric`` for GPU-accele array computations. In this tutorial, these libraries are used for efficient model training, large-scale data handling, and accelerating computations across CPUs and GPUs. -What is legate boost? +What is legate-boost? ===================== In scenarios where high-performance training is needed across large -datasets or distributed hardware, ``Legate Boost`` offers a scalable -alternative. ``Legate Boost`` is an advanced gradient boosting library built +datasets or distributed hardware, ``legate-boost`` offers a scalable +alternative. ``legate-boost`` is an advanced gradient boosting library built on the ``Legate`` and Legion parallel programming frameworks. Unlike -traditional boosting libraries such as XGBoost or LightGBM, ``Legate Boost`` +traditional boosting libraries such as XGBoost or LightGBM, ``legate-boost`` provides a unified infrastructure that seamlessly scales across CPU's and GPU's, supporting both single-node and distributed training while integrating naturally with ``cupynumeric`` workflows for efficient @@ -27,25 +27,25 @@ trees, kernel ridge regression, linear models, or neural networks, all written in Python with minimal code changes. These models are automatically parallelized and executed efficiently -without manual data movement or partitioning. ``Legate Boost`` emphasizes +without manual data movement or partitioning. ``legate-boost`` emphasizes architectural simplicity, extensibility, and performance, delivering state-of-the-art results on tabular data while leveraging the full computational power of modern heterogeneous hardware. Please refer to `Distributed Computing with cupynumeric`_ -and `Legate boost`_ for more +and `legate-boost`_ for more information and detailed instructions on installation. .. _Distributed Computing with cupynumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cupynumeric.ipynb -.. _Legate boost: https://github.com/rapidsai/legate-boost/tree/main +.. _legate-boost: https://github.com/rapidsai/legate-boost/tree/main Quick installation and setup ---------------------------- .. code-block:: sh - # create a new env and install legate boost and dependencies + # create a new env and install legate-boost and dependencies conda create -n legate-boost -c legate -c conda-forge -c nvidia legate-boost # activate env @@ -62,7 +62,7 @@ for multi-node setup plese refer to `GASnet-based installation`_ Usage ===== -``Legate Boost`` offers two main estimator types: +``legate-boost`` offers two main estimator types: - ``LBRegressor`` for regression tasks - ``LBClassifier`` for classification tasks @@ -113,7 +113,7 @@ In this example: the test set (X_test). This represents a typical workflow for applying a regression model using -``Legate Boost``. The ``LBRegressor`` estimator offers several configurable +``legate-boost``. The ``LBRegressor`` estimator offers several configurable options, such as base_model and learning_rate, to help optimize model performance. For a comprehensive list of features and parameters, refer to the `official documentation`_. @@ -129,7 +129,7 @@ for a wide range of applications, including spam detection, image classification, and sentiment analysis. The example below demonstrates how to implement a classification model -using the ``LBClassifier`` estimator from ``Legate Boost``: +using the ``LBClassifier`` estimator from ``legate-boost``: .. code-block:: python @@ -165,14 +165,14 @@ In this example: Just like the regressor, the ``LBClassifier`` follows a clean and intuitive workflow. It provides additional options and advanced configurations to optimize model performance. For more detailed information, refer to the -Legate Boost `estimators`_ documentation. +legate-boost `estimators`_ documentation. .. _estimators: https://rapidsai.github.io/legate-boost/api/estimators.html#legateboost.LBClassifier Example 1 ========= -Here is an example of using ``Legate Boost`` to build a regression model on +Here is an example of using ``legate-boost`` to build a regression model on the California housing dataset. It showcases key features like scalable training across GPUs/nodes, customizable base models, and adjustable learning rates. @@ -197,7 +197,7 @@ assess predictive performance on real-world tabular data. About this implementation ------------------------- -The following code creates a ``Legate Boost`` regression model using +The following code creates a ``legate-boost`` regression model using ``LBRegressor``, which trains a gradient boosting model optimized for multi-GPU and multi-node environments. The model is configured to use 100 boosting rounds (n_estimators=100), with each round adding a @@ -246,9 +246,9 @@ Code module :language: python This simple example demonstrates how to train a regression model on the -california housing dataset using ``Legate Boost``. Although the code looks -similar to standard XGBoost, Legate Boost automatically enables -multi-GPU and multi-node computation. ``Legate Boost`` achieves multi-GPU +california housing dataset using ``legate-boost``. Although the code looks +similar to standard XGBoost, legate-boost automatically enables +multi-GPU and multi-node computation. ``legate-boost`` achieves multi-GPU and multi-node scaling through its integration with cupynumeric and the Legion runtime. Unlike traditional GPU libraries that allocate data to a single device, ``cupynumeric`` creates ``logical arrays`` and abstract @@ -302,11 +302,11 @@ This produces the following output: Example 2 ========= -This example demonstrates how Legate Boost can be applied to the ``Give +This example demonstrates how legate-boost can be applied to the ``Give Me Some Credit`` dataset (OpenML data_id: 46929) to build a classification model using ensemble learning by combining different model types. It also highlights the integration of ``Legate Dataframe`` with -``Legate Boost`` to enable distributed training across multi-GPU and +``legate-boost`` to enable distributed training across multi-GPU and multi-node environments, showcasing scalable machine learning on the credit score dataset. @@ -335,7 +335,7 @@ indicating the likelihood of future financial trouble. About this implementation ------------------------- -This implementation will focus on demonstrating ``Legate Boost’s`` flexible +This implementation will focus on demonstrating ``legate-boost’s`` flexible model ensembling capabilities, specifically: - Tree-based gradient boosting models, ideal for structured/tabular @@ -343,7 +343,7 @@ model ensembling capabilities, specifically: - Neural network-based classifiers, allowing hybrid or deep learning approaches. -By leveraging ``Legate Boost``, we can ensemble these two models and +By leveraging ``legate-boost``, we can ensemble these two models and efficiently train and evaluate both model types on GPUs or CPUs, showcasing scalable performance for large tabular datasets in financial risk prediction. @@ -439,9 +439,9 @@ tasks to execute efficiently across multiple GPUs or nodes. :end-before: [convert to cupynumeric array end] As we have a data_arr backed by ``cupynumeric``, we first split the dataset -into training and testing subsets, which are then passed to ``Legate Boost`` +into training and testing subsets, which are then passed to ``legate-boost`` for efficient training across available hardware resources. The model is -built using ``Legate Boost’s`` ensemble framework (LBClassifier), which +built using ``legate-boost’s`` ensemble framework (LBClassifier), which allows combining multiple types of base learners into a single unified model. @@ -451,7 +451,7 @@ non-linear decision boundaries by splitting the feature space hierarchically up to 5 levels deep, and a Neural Network (lb.models.NN) with two hidden layers of 10 neurons each (hidden_layer_sizes=(10,10)), trained for max_iter=10 epochs with verbose=True to monitor progress. By -combining a tree-based model with a neural network, ``Legate Boost`` +combining a tree-based model with a neural network, ``legate-boost`` leverages the interpretability and rule-based decision-making of trees together with the ability of neural networks to model intricate, high-dimensional relationships. This ensemble design results in a more @@ -524,7 +524,7 @@ model is saved with Joblib for future inference without retraining. This workflow illustrates how ``Legate Dataframe`` provides a scalable preprocessing layer, ``cupynumeric`` arrays enable distributed GPU -computation, and ``Legate Boost`` delivers a flexible ensemble learning +computation, and ``legate-boost`` delivers a flexible ensemble learning framework capable of leveraging multi-node, multi-GPU infrastructure efficiently. From 7d4d8f86f652010c9633c2a934ddbd9ec2aaecae Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 28 Aug 2025 11:54:17 -0400 Subject: [PATCH 35/72] Rename Legate_Boost.rst to legate-boost.rst --- docs/source/{Legate_Boost.rst => legate-boost.rst} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/source/{Legate_Boost.rst => legate-boost.rst} (100%) diff --git a/docs/source/Legate_Boost.rst b/docs/source/legate-boost.rst similarity index 100% rename from docs/source/Legate_Boost.rst rename to docs/source/legate-boost.rst From 2d38285068feb4d535e3fa23731c84ff7a59047b Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 28 Aug 2025 12:07:46 -0400 Subject: [PATCH 36/72] Update legate-boost.rst --- docs/source/legate-boost.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index 70d83a0f..8ee042a5 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -33,12 +33,12 @@ state-of-the-art results on tabular data while leveraging the full computational power of modern heterogeneous hardware. Please refer to `Distributed Computing with cupynumeric`_ -and `legate-boost`_ for more +and `legate boost`_ for more information and detailed instructions on installation. .. _Distributed Computing with cupynumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cupynumeric.ipynb -.. _legate-boost: https://github.com/rapidsai/legate-boost/tree/main +.. _legate boost: https://github.com/rapidsai/legate-boost/tree/main Quick installation and setup ---------------------------- From 31776d87610787a96158ee0da3c2a9600d38eb05 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 12:47:54 -0400 Subject: [PATCH 37/72] Update legate-boost.rst --- docs/source/legate-boost.rst | 20 ++------------------ 1 file changed, 2 insertions(+), 18 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index 8ee042a5..fd357af1 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -34,29 +34,13 @@ computational power of modern heterogeneous hardware. Please refer to `Distributed Computing with cupynumeric`_ and `legate boost`_ for more -information and detailed instructions on installation. +information and detailed instructions on installation. +for multi-node setup plese refer to `GASnet-based installation`_ .. _Distributed Computing with cupynumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cupynumeric.ipynb .. _legate boost: https://github.com/rapidsai/legate-boost/tree/main -Quick installation and setup ----------------------------- - -.. code-block:: sh - - # create a new env and install legate-boost and dependencies - conda create -n legate-boost -c legate -c conda-forge -c nvidia legate-boost - - # activate env - conda activate legate-boost - - # install legate-dataframe - conda install -c legate -c rapidsai -c conda-forge legate-dataframe - - -for multi-node setup plese refer to `GASnet-based installation`_ - .. _GASnet-based installation: https://docs.nvidia.com/legate/latest/gasnet.html Usage From f26e5bc7a495577d30546c88f586350a272dd244 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 12:52:51 -0400 Subject: [PATCH 38/72] removed installation steps and liked to repo --- docs/source/legate-boost.rst | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index fd357af1..2a604da2 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -34,15 +34,12 @@ computational power of modern heterogeneous hardware. Please refer to `Distributed Computing with cupynumeric`_ and `legate boost`_ for more -information and detailed instructions on installation. -for multi-node setup plese refer to `GASnet-based installation`_ +information and detailed instructions on installation and setup. -.. _Distributed Computing with cupynumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cupynumeric.ipynb +.. _Distributed Computing with cupynumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cuPyNumeric.ipynb .. _legate boost: https://github.com/rapidsai/legate-boost/tree/main -.. _GASnet-based installation: https://docs.nvidia.com/legate/latest/gasnet.html - Usage ===== From 12c83c0657c4dcfed61ef28581add0be55fdd1f2 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 12:54:52 -0400 Subject: [PATCH 39/72] Updated installation links --- docs/source/legate-boost.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index 2a604da2..c5d2a822 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -38,7 +38,7 @@ information and detailed instructions on installation and setup. .. _Distributed Computing with cupynumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cuPyNumeric.ipynb -.. _legate boost: https://github.com/rapidsai/legate-boost/tree/main +.. _legate boost: https://github.com/rapidsai/legate-boost?tab=readme-ov-file#installation. Usage ===== From 08f3833b6614964941bb0c44fe354e8f954ffabe Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 13:30:54 -0400 Subject: [PATCH 40/72] Rename docs/source/LBClassifier.py to docs/source/examples/LBClassifier.py --- docs/source/{ => examples}/LBClassifier.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/source/{ => examples}/LBClassifier.py (100%) diff --git a/docs/source/LBClassifier.py b/docs/source/examples/LBClassifier.py similarity index 100% rename from docs/source/LBClassifier.py rename to docs/source/examples/LBClassifier.py From c8501f456268d2ef480cf352414c7268fcbab242 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 13:31:15 -0400 Subject: [PATCH 41/72] Rename docs/source/LBRegressor.py to docs/source/examples/LBRegressor.py --- docs/source/{ => examples}/LBRegressor.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/source/{ => examples}/LBRegressor.py (100%) diff --git a/docs/source/LBRegressor.py b/docs/source/examples/LBRegressor.py similarity index 100% rename from docs/source/LBRegressor.py rename to docs/source/examples/LBRegressor.py From 25dc85de4bfe9bfa89f0801f84ad64950a1e29d4 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 13:31:30 -0400 Subject: [PATCH 42/72] Rename docs/source/creditscore.py to docs/source/examples/creditscore.py --- docs/source/{ => examples}/creditscore.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/source/{ => examples}/creditscore.py (100%) diff --git a/docs/source/creditscore.py b/docs/source/examples/creditscore.py similarity index 100% rename from docs/source/creditscore.py rename to docs/source/examples/creditscore.py From 8780e711b059d760c7307fb41da7325b9c819134 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 13:31:46 -0400 Subject: [PATCH 43/72] Rename docs/source/housing.py to docs/source/examples/housing.py --- docs/source/{ => examples}/housing.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/source/{ => examples}/housing.py (100%) diff --git a/docs/source/housing.py b/docs/source/examples/housing.py similarity index 100% rename from docs/source/housing.py rename to docs/source/examples/housing.py From 65c6d6f5eff1dd051391220677c161c179bf5cb9 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 13:32:00 -0400 Subject: [PATCH 44/72] Rename docs/source/inference.py to docs/source/examples/inference.py --- docs/source/{ => examples}/inference.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/source/{ => examples}/inference.py (100%) diff --git a/docs/source/inference.py b/docs/source/examples/inference.py similarity index 100% rename from docs/source/inference.py rename to docs/source/examples/inference.py From a7b6a252a15627ad4746e8c9b685015ccdb77cbb Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 13:34:37 -0400 Subject: [PATCH 45/72] Update legate-boost.rst --- docs/source/legate-boost.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index c5d2a822..8c8d4a2d 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -81,7 +81,7 @@ function to generate predictions on new data. Here’s how to set it up: # predict y_pred = regression_model.predict(X_test) -.. literalinclude:: LBRegressor.py +.. literalinclude:: examples/LBRegressor.py :language: python In this example: @@ -130,7 +130,7 @@ using the ``LBClassifier`` estimator from ``legate-boost``: classification_model.fit(X_train, y_train) y_pred = classification_model.predict(X_test) -.. literalinclude:: LBClassifier.py +.. literalinclude:: examples/LBClassifier.py :language: python In this example: @@ -223,7 +223,7 @@ Code module print(f"Training time: {(end - start)/1000:.6f} ms") -.. literalinclude:: housing.py +.. literalinclude:: examples/housing.py :language: python This simple example demonstrates how to train a regression model on the @@ -370,7 +370,7 @@ computations across multiple GPUs and nodes. df = pa.Table.from_pandas(df) ldf = LogicalTable.from_arrow(df) -.. literalinclude:: creditscore.py +.. literalinclude:: examples/creditscore.py :language: python :start-after: [import libraries] :end-before: [covert to LogicalTable end] @@ -414,7 +414,7 @@ tasks to execute efficiently across multiple GPUs or nodes. # [Convert-to-cupynumeric-array] data_arr = nldf.to_array() -.. literalinclude:: creditscore.py +.. literalinclude:: examples/creditscore.py :language: python :start-after: [Replace nulls] :end-before: [convert to cupynumeric array end] @@ -469,7 +469,7 @@ individually. rt.issue_execution_fence() end = time() -.. literalinclude:: creditscore.py +.. literalinclude:: examples/creditscore.py :language: python :start-after: [preparing data for training and testing] :end-before: [training end] @@ -499,7 +499,7 @@ model is saved with Joblib for future inference without retraining. pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) -.. literalinclude:: creditscore.py +.. literalinclude:: examples/creditscore.py :language: python :start-after: [Prediction] @@ -600,7 +600,7 @@ into the speedup and variability achieved with GPU acceleration. print(f"Variance: {float(var_time)/1000:.2f} ms") print(f"Standard deviation: {float(std)/1000:.2f} ms") -.. literalinclude:: inference.py +.. literalinclude:: examples/inference.py :language: python From 942a3ab645ecc0123ec719e909c7c390c5316478 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 15:09:42 -0400 Subject: [PATCH 46/72] Update creditscore.py --- docs/source/examples/creditscore.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/docs/source/examples/creditscore.py b/docs/source/examples/creditscore.py index e69421ad..d5e01a7a 100644 --- a/docs/source/examples/creditscore.py +++ b/docs/source/examples/creditscore.py @@ -2,10 +2,11 @@ import cudf import pandas import cupy as cp +import numpy as np +import legate as lg import pyarrow as pa -import legate_dataframe +from joblib import dump import legateboost as lb -import cupynumeric as cpn from legate.timing import time from sklearn.datasets import fetch_openml from sklearn.metrics import accuracy_score @@ -103,5 +104,5 @@ x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) -pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) -pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) +pandas.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) +pandas.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) From 8a1407451fdd64107e98cd9424b6ed8419513270 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 15:10:40 -0400 Subject: [PATCH 47/72] Update housing.py --- docs/source/examples/housing.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/docs/source/examples/housing.py b/docs/source/examples/housing.py index 0467f45f..098d7925 100644 --- a/docs/source/examples/housing.py +++ b/docs/source/examples/housing.py @@ -5,9 +5,6 @@ import cupynumeric as cn import legateboost as lb from legate.timing import time -from joblib import dump -import numpy as np -import pandas as pd # [Import data] data = fetch_california_housing() From 3c41b879cc4a5d113b42cfdd62807cefda6ec5be Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 15:11:21 -0400 Subject: [PATCH 48/72] Update housing.py --- docs/source/examples/housing.py | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/examples/housing.py b/docs/source/examples/housing.py index 098d7925..e6c40759 100644 --- a/docs/source/examples/housing.py +++ b/docs/source/examples/housing.py @@ -2,7 +2,6 @@ from sklearn.datasets import fetch_california_housing from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error -import cupynumeric as cn import legateboost as lb from legate.timing import time From 0bbbda54ed021b9847efeac94b71ed1535090dd4 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 15:11:45 -0400 Subject: [PATCH 49/72] Update inference.py --- docs/source/examples/inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/examples/inference.py b/docs/source/examples/inference.py index 33f12d4f..daa4eff1 100644 --- a/docs/source/examples/inference.py +++ b/docs/source/examples/inference.py @@ -1,5 +1,5 @@ import cupynumeric as cp -from joblib import dump, load +from joblib import load from legate.timing import time import pandas as pd import legate.core as lg From 81b587e15f2fb815f30e9122d24351e092c5a970 Mon Sep 17 00:00:00 2001 From: NihaalChowdary Date: Fri, 29 Aug 2025 15:59:44 -0400 Subject: [PATCH 50/72] Apply pre-commit formatting and linting fixes --- docs/source/examples/LBClassifier.py | 7 ++++-- docs/source/examples/LBRegressor.py | 7 ++++-- docs/source/examples/creditscore.py | 34 ++++++++++++---------------- docs/source/examples/housing.py | 5 ++-- docs/source/examples/inference.py | 23 ++++++++++--------- docs/source/legate-boost.rst | 34 ++++++++++++++-------------- legateboost/callbacks.py | 4 ++-- legateboost/encoder.py | 22 +++++++++--------- legateboost/legateboost.py | 33 ++++++++++++++------------- legateboost/metrics.py | 8 +++---- legateboost/models/base_model.py | 9 ++++---- legateboost/models/krr.py | 6 ++--- legateboost/models/linear.py | 10 ++++---- legateboost/objectives.py | 24 +++++++++++--------- legateboost/utils.py | 7 +++--- 15 files changed, 121 insertions(+), 112 deletions(-) diff --git a/docs/source/examples/LBClassifier.py b/docs/source/examples/LBClassifier.py index d0cd81fc..ab13f9ba 100644 --- a/docs/source/examples/LBClassifier.py +++ b/docs/source/examples/LBClassifier.py @@ -1,12 +1,15 @@ -import legateboost as lb from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split +import legateboost as lb + # creating synthetic dataset X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42) # splitting the data -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) +X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 +) # classification model with 50 estimators classification_model = lb.LBClassifier(n_estimators=50) diff --git a/docs/source/examples/LBRegressor.py b/docs/source/examples/LBRegressor.py index f3c6df60..6a35f0a6 100644 --- a/docs/source/examples/LBRegressor.py +++ b/docs/source/examples/LBRegressor.py @@ -1,12 +1,15 @@ -import legateboost as lb from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split +import legateboost as lb + # creating synthetic dataset X, y = make_regression(n_samples=100, n_features=4, noise=8, random_state=42) # splitting the data -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) +X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 +) # regression model with 100 estimators regression_model = lb.LBRegressor(n_estimators=100) diff --git a/docs/source/examples/creditscore.py b/docs/source/examples/creditscore.py index d5e01a7a..47686492 100644 --- a/docs/source/examples/creditscore.py +++ b/docs/source/examples/creditscore.py @@ -1,18 +1,19 @@ # [import libraries] import cudf -import pandas import cupy as cp import numpy as np -import legate as lg +import pandas import pyarrow as pa from joblib import dump -import legateboost as lb -from legate.timing import time +from legate_dataframe.lib.core.column import LogicalColumn +from legate_dataframe.lib.core.table import LogicalTable +from legate_dataframe.lib.replace import replace_nulls from sklearn.datasets import fetch_openml from sklearn.metrics import accuracy_score -from legate_dataframe.lib.replace import replace_nulls -from legate_dataframe.lib.core.table import LogicalTable -from legate_dataframe.lib.core.column import LogicalColumn + +import legate.core as lg +import legateboost as lb +from legate.timing import time rt = lg.get_legate_runtime() @@ -20,7 +21,7 @@ data = fetch_openml(data_id=46929, as_frame=True) xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas df = xd.DataFrame(data.data, columns=data.feature_names) -df['Target'] = data.target +df["Target"] = data.target # [convert to LogicalTable] if cp.cuda.runtime.getDeviceCount() > 0: @@ -28,16 +29,14 @@ else: df = pa.Table.from_pandas(df) ldf = LogicalTable.from_arrow(df) - + # [covert to LogicalTable end] # [Replace nulls] median_salary = df["MonthlyIncome"].median() median_dependents = df["NumberOfDependents"].median() -mmi = LogicalColumn( - replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary) -) +mmi = LogicalColumn(replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary)) mnd = LogicalColumn( replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) ) @@ -46,11 +45,8 @@ features = ldf.get_column_names() nldf = LogicalTable( - [ - ldf[0], ldf[1], ldf[2], ldf[3], mmi, - ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10] - ], - features + [ldf[0], ldf[1], ldf[2], ldf[3], mmi, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], + features, ) # [Convert to cupynumeric array] @@ -60,7 +56,7 @@ # [convert to cupynumeric array end] # [preparing data for training and testing] -x = data_arr[:, :-1] # all columns except last +x = data_arr[:, :-1] # all columns except last y = data_arr[:, -1] # [Splitting the data into training and testing] @@ -80,7 +76,7 @@ model = lb.LBClassifier( base_models=( lb.models.Tree(max_depth=5), - lb.models.NN(max_iter=10, hidden_layer_sizes=(10,10), verbose=True) + lb.models.NN(max_iter=10, hidden_layer_sizes=(10, 10), verbose=True), ) ).fit(x_train, y_train) diff --git a/docs/source/examples/housing.py b/docs/source/examples/housing.py index e6c40759..169f4bfe 100644 --- a/docs/source/examples/housing.py +++ b/docs/source/examples/housing.py @@ -1,7 +1,8 @@ # [Import libraries] from sklearn.datasets import fetch_california_housing -from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error +from sklearn.model_selection import train_test_split + import legateboost as lb from legate.timing import time @@ -14,7 +15,7 @@ # [Create and fit Legate Boost regressor] model = lb.LBRegressor( n_estimators=100, - base_models=(lb.models.Tree(max_depth=5),), + base_models=(lb.models.Tree(max_depth=8),), objective="squared_error", learning_rate=0.1, verbose=True, diff --git a/docs/source/examples/inference.py b/docs/source/examples/inference.py index daa4eff1..bf217ab0 100644 --- a/docs/source/examples/inference.py +++ b/docs/source/examples/inference.py @@ -1,13 +1,14 @@ -import cupynumeric as cp -from joblib import load -from legate.timing import time import pandas as pd +from joblib import load + +import cupynumeric as cpn import legate.core as lg +from legate.timing import time rt = lg.get_legate_runtime() timings = [] -model = load("legate_boost_housing.joblib") +model = load("legate_boost_housing.joblib") X = pd.read_csv("x_test_housing.csv") for _ in range(10): @@ -19,14 +20,14 @@ timings.append(end - start) timings = timings[1:] -timings_gpu = cp.array(timings) +timings_gpu = cpn.array(timings) -mean_time = cp.mean(timings_gpu) -median_time = cp.median(timings_gpu) -min_time = cp.min(timings_gpu) -max_time = cp.max(timings_gpu) -var_time = cp.var(timings_gpu) -std = cp.sqrt(var_time) +mean_time = cpn.mean(timings_gpu) +median_time = cpn.median(timings_gpu) +min_time = cpn.min(timings_gpu) +max_time = cpn.max(timings_gpu) +var_time = cpn.var(timings_gpu) +std = cpn.sqrt(var_time) print(f"Mean: {float(mean_time)/1000:.2f} ms") print(f"Median: {float(median_time)/1000:.2f} ms") diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index 8c8d4a2d..30ae38de 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -157,7 +157,7 @@ Here is an example of using ``legate-boost`` to build a regression model on the California housing dataset. It showcases key features like scalable training across GPUs/nodes, customizable base models, and adjustable learning rates. -About dataset +About dataset ------------- The California housing dataset is a classic benchmark dataset containing @@ -354,15 +354,15 @@ computations across multiple GPUs and nodes. from legate_dataframe.lib.replace import replace_nulls from legate_dataframe.lib.core.table import LogicalTable from legate_dataframe.lib.core.column import LogicalColumn - + rt = lg.get_legate_runtime() - + # [import-data] data = fetch_openml(data_id=46929, as_frame=True) xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas df = xd.DataFrame(data.data, columns=data.feature_names) df['Target'] = data.target - + # [convert-to-LogicalTable] if cp.cuda.runtime.getDeviceCount() > 0: ldf = LogicalTable.from_cudf(df) @@ -392,16 +392,16 @@ tasks to execute efficiently across multiple GPUs or nodes. # [Replace nulls] median_salary = df["MonthlyIncome"].median() median_dependents = df["NumberOfDependents"].median() - + mmi = LogicalColumn( replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary) ) mnd = LogicalColumn( replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) ) - + # [Create-new-LogicalTable-with-updated-columns] - + features = ldf.get_column_names() nldf = LogicalTable( [ @@ -410,7 +410,7 @@ tasks to execute efficiently across multiple GPUs or nodes. ], features ) - + # [Convert-to-cupynumeric-array] data_arr = nldf.to_array() @@ -444,28 +444,28 @@ individually. # [preparing-data-for-training-and-testing] x = data_arr[:, :-1] # all columns except last y = data_arr[:, -1] - + # [Splitting the data into training and testing] num_samples = x.shape[0] split_ratio = 0.8 split_index = int(num_samples * split_ratio) - + x_train = x[:split_index] y_train = y[:split_index] x_test = x[split_index:] y_test = y[split_index:] - + # [training] rt.issue_execution_fence() start = time() - + model = lb.LBClassifier( base_models=( lb.models.Tree(max_depth=8), lb.models.NN(max_iter=10, hidden_layer_sizes=(10,10), verbose=True) ) ).fit(x_train, y_train) - + rt.issue_execution_fence() end = time() @@ -483,19 +483,19 @@ model is saved with Joblib for future inference without retraining. # [Prediction] predictions = model.predict(x_test) print(type(predictions)) - + # [Evaluation] acc = accuracy_score(y_test, predictions) print("Accuracy:", acc) print(f"\nThe training time for creditscore exp is: {(end - start)/1000:.6f} ms") - + # [Save model] dump(model, "legate_boost_model.joblib") - + # [ Save test data [ x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) - + pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) diff --git a/legateboost/callbacks.py b/legateboost/callbacks.py index e17b26dc..bacf2693 100644 --- a/legateboost/callbacks.py +++ b/legateboost/callbacks.py @@ -57,8 +57,8 @@ def after_iteration( class EarlyStopping(TrainingCallback): - """Callback for early stopping during training. The last evaluation dataset is - used for early stopping. + """Callback for early stopping during training. The last evaluation dataset + is used for early stopping. Args: rounds (int): The number of rounds to wait for improvement before stopping. diff --git a/legateboost/encoder.py b/legateboost/encoder.py index 16737fd7..69008ba4 100644 --- a/legateboost/encoder.py +++ b/legateboost/encoder.py @@ -16,18 +16,18 @@ class TargetEncoder(TransformerMixin, BaseEstimator, PickleCupynumericMixin): - """TargetEncoder is a transformer that encodes categorical features using the - mean of the target variable. When `fit_transform` is called, a cross- + """TargetEncoder is a transformer that encodes categorical features using + the mean of the target variable. When `fit_transform` is called, a cross- validation procedure is used to generate encodings for each training fold, which are then applied to the test fold. `fit().transform()` differs from `fit_transform()` in that the former fits the encoder on all the data and - generates encodings for each feature. This encoder is modelled on the sklearn - TargetEncoder with only minor differences in how the CV folds are generated. - As it is difficult to rearrange and gather data from each fold in distributed - environment, training rows are kept in place and then assigned a cv fold by - generating a random integer in the range [0, n_folds). As per sklearn, when - smooth="auto", an empirical Bayes estimate per [#]_ is used to avoid - overfitting. + generates encodings for each feature. This encoder is modelled on the + sklearn TargetEncoder with only minor differences in how the CV folds are + generated. As it is difficult to rearrange and gather data from each fold + in distributed environment, training rows are kept in place and then + assigned a cv fold by generating a random integer in the range [0, + n_folds). As per sklearn, when smooth="auto", an empirical Bayes estimate + per [#]_ is used to avoid overfitting. .. [#] Micci-Barreca, Daniele. "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems." ACM SIGKDD explorations newsletter 3.1 (2001): 27-32. @@ -264,8 +264,8 @@ def _get_category_means( """Compute some label summary statistics for each category in the input data. - Returns a 3D array of shape (n_categories, n_outputs, 2) containing the - sum, count of the labels for each category. + Returns a 3D array of shape (n_categories, n_outputs, 2) + containing the sum, count of the labels for each category. """ task = get_legate_runtime().create_auto_task( user_context, user_lib.cffi.TARGET_ENCODER_MEAN diff --git a/legateboost/legateboost.py b/legateboost/legateboost.py index 80c8beb2..6f283302 100644 --- a/legateboost/legateboost.py +++ b/legateboost/legateboost.py @@ -313,8 +313,8 @@ def update( eval_result: EvalResult = {}, ) -> Self: """Update a gradient boosting model from the training set (X, y). This - method does not add any new models to the ensemble, only updates existing - models to fit the new data. + method does not add any new models to the ensemble, only updates + existing models to fit the new data. Parameters ---------- @@ -476,8 +476,8 @@ def __iter__(self) -> Any: return iter(self.models_) def __mul__(self, scalar: Any) -> Self: - """Gradient boosted models are linear in the predictions before the non- - linear link function is applied. This means that the model can be + """Gradient boosted models are linear in the predictions before the + non- linear link function is applied. This means that the model can be multiplied by a scalar, which subsequently scales all raw output predictions. This is useful for ensembling models. @@ -550,8 +550,8 @@ def global_attributions( n_samples: int = 5, check_efficiency: bool = False, ) -> Tuple[cn.array, cn.array]: - r"""Compute global feature attributions for the model. Global attributions - show the effect of a feature on a model's loss function. + r"""Compute global feature attributions for the model. Global + attributions show the effect of a feature on a model's loss function. We use a Shapley value approach to compute the attributions: :math:`Sh_i(v)=\frac{1}{|N|!} \sum_{\sigma \in \mathfrak{S}_d} \big[ v([\sigma]_{i-1} \cup\{i\}) - v([\sigma]_{i-1}) \big],` @@ -612,10 +612,11 @@ def local_attributions( n_samples: int = 5, check_efficiency: bool = False, ) -> Tuple[cn.array, cn.array]: - r"""Local feature attributions for model predictions. Shows the effect of a - feature on each output prediction. See the definition of Shapley values in - :func:`~legateboost.BaseModel.global_attributions`, where the :math:`v` - function is here the model prediction instead of the loss function. + r"""Local feature attributions for model predictions. Shows the effect + of a feature on each output prediction. See the definition of Shapley + values in :func:`~legateboost.BaseModel.global_attributions`, where the + :math:`v` function is here the model prediction instead of the loss + function. Parameters ---------- @@ -749,8 +750,8 @@ def partial_fit( eval_set: List[Tuple[cn.ndarray, ...]] = [], eval_result: EvalResult = {}, ) -> LBBase: - """This method is used for incremental (online) training of the model. An - additional `n_estimators` models will be added to the ensemble. + """This method is used for incremental (online) training of the model. + An additional `n_estimators` models will be added to the ensemble. Parameters ---------- @@ -927,8 +928,8 @@ def partial_fit( eval_result: EvalResult = {}, ) -> LBBase: """This method is used for incremental fitting on a batch of samples. - Requires the classes to be provided up front, as they may not be inferred - from the first batch. + Requires the classes to be provided up front, as they may not be + inferred from the first batch. Parameters ---------- @@ -1032,8 +1033,8 @@ def fit( return self def predict_raw(self, X: cn.ndarray) -> cn.ndarray: - """Predict pre-transformed values for samples in X. E.g. before applying a - sigmoid function. + """Predict pre-transformed values for samples in X. E.g. before + applying a sigmoid function. Parameters ---------- diff --git a/legateboost/metrics.py b/legateboost/metrics.py index 6f4065b5..68e6a028 100644 --- a/legateboost/metrics.py +++ b/legateboost/metrics.py @@ -145,8 +145,8 @@ def name(self) -> str: class GammaLLMetric(BaseMetric): - """The mean negative log likelihood of the labels, given parameters predicted - by the model.""" + """The mean negative log likelihood of the labels, given parameters + predicted by the model.""" @override def metric(self, y: cn.ndarray, pred: cn.ndarray, w: cn.ndarray) -> cn.ndarray: @@ -253,8 +253,8 @@ def name(self) -> str: class LogLossMetric(BaseMetric): - """Class for computing the logarithmic loss (logloss) metric between the true - labels and predicted labels. + """Class for computing the logarithmic loss (logloss) metric between the + true labels and predicted labels. For binary classification: diff --git a/legateboost/models/base_model.py b/legateboost/models/base_model.py index a1e88011..39987f88 100644 --- a/legateboost/models/base_model.py +++ b/legateboost/models/base_model.py @@ -11,9 +11,9 @@ class BaseModel(PickleCupynumericMixin, ABC): """Base class for all models in LegateBoost. - Defines the interface for fitting, updating, and predicting a model, as well - as string representation and equality comparison. Implement these methods to - create a custom model. + Defines the interface for fitting, updating, and predicting a model, + as well as string representation and equality comparison. Implement + these methods to create a custom model. """ def set_random_state(self, random_state: np.random.RandomState) -> "BaseModel": @@ -27,7 +27,8 @@ def fit( g: cn.ndarray, h: cn.ndarray, ) -> "BaseModel": - """Fit the model to a second order Taylor expansion of the loss function. + """Fit the model to a second order Taylor expansion of the loss + function. Parameters ---------- diff --git a/legateboost/models/krr.py b/legateboost/models/krr.py index 31af8d5a..ef0de7ef 100644 --- a/legateboost/models/krr.py +++ b/legateboost/models/krr.py @@ -35,9 +35,9 @@ def rbf(x: cn.ndarray, sigma: float) -> cn.ndarray: class KRR(BaseModel): - """Kernel Ridge Regression model using the Nyström approximation. The accuracy - of the approximation is governed by the parameter `n_components` <= `n`. - Effectively, `n_components` rows will be randomly sampled (without + """Kernel Ridge Regression model using the Nyström approximation. The + accuracy of the approximation is governed by the parameter `n_components` + <= `n`. Effectively, `n_components` rows will be randomly sampled (without replacement) from X in each boosting iteration. The kernel is fixed to be the RBF kernel: diff --git a/legateboost/models/linear.py b/legateboost/models/linear.py index ec34594e..f8757772 100644 --- a/legateboost/models/linear.py +++ b/legateboost/models/linear.py @@ -9,11 +9,11 @@ class Linear(BaseModel): - """Generalised linear model. Boosting linear models is equivalent to fitting a - single linear model where each boosting iteration is a newton step. Note that - the l2 penalty is applied to the weights of each model, as opposed to the sum - of all models. This can lead to different results when compared to fitting a - linear model with sklearn. + """Generalised linear model. Boosting linear models is equivalent to + fitting a single linear model where each boosting iteration is a newton + step. Note that the l2 penalty is applied to the weights of each model, as + opposed to the sum of all models. This can lead to different results when + compared to fitting a linear model with sklearn. It is recommended to normalize the data before fitting. This ensures regularisation is evenly applied to all features and prevents numerical issues. diff --git a/legateboost/objectives.py b/legateboost/objectives.py index 52865bb3..eafa0b98 100644 --- a/legateboost/objectives.py +++ b/legateboost/objectives.py @@ -100,12 +100,13 @@ def initialise_prediction( class ClassificationObjective(BaseObjective): - """Extension of BaseObjective for classification problems, use can optionaly - define a method of extracting a class output from probabilities.""" + """Extension of BaseObjective for classification problems, use can + optionaly define a method of extracting a class output from + probabilities.""" def output_class(self, pred: cn.ndarray) -> cn.ndarray: - """Defined how to output class labels from transfored output. This may be - as simple as argmax over probabilities. + """Defined how to output class labels from transfored output. This may + be as simple as argmax over probabilities. Args: pred (cn.ndarray): The transformed predictions. @@ -339,8 +340,8 @@ def initialise_prediction( class GammaObjective(FitInterceptRegMixIn, Forecast): - """Regression with the :math:`\\Gamma` distribution function using the shape - scale parameterization.""" + """Regression with the :math:`\\Gamma` distribution function using the + shape scale parameterization.""" @override def gradient(self, y: cn.ndarray, pred: cn.ndarray) -> GradPair: @@ -419,7 +420,8 @@ def var(self, param: cn.ndarray) -> cn.ndarray: class QuantileObjective(BaseObjective): - """Minimises the quantile loss, otherwise known as check loss or pinball loss. + """Minimises the quantile loss, otherwise known as check loss or pinball + loss. :math:`L(y_i, p_i) = \\frac{1}{k}\\sum_{j=1}^{k} (q_j - \\mathbb{1})(y_i - p_{i, j})` @@ -475,8 +477,8 @@ def initialise_prediction( class LogLossObjective(ClassificationObjective): - """The Log Loss objective function for binary and multi-class classification - problems. + """The Log Loss objective function for binary and multi-class + classification problems. This objective function computes the log loss between the predicted and true labels. @@ -565,8 +567,8 @@ def initialise_prediction( class ExponentialObjective(ClassificationObjective, FitInterceptRegMixIn): - """Exponential loss objective function for binary classification. Equivalent - to the AdaBoost multiclass exponential loss in [1]. + """Exponential loss objective function for binary classification. + Equivalent to the AdaBoost multiclass exponential loss in [1]. Defined as: diff --git a/legateboost/utils.py b/legateboost/utils.py index c38d62d0..fb7c3f95 100644 --- a/legateboost/utils.py +++ b/legateboost/utils.py @@ -156,8 +156,8 @@ def get_store(input: Any) -> LogicalStore: def solve_singular(a: cn.ndarray, b: cn.ndarray) -> cn.ndarray: - """Solve a singular linear system Ax = b for x. The same as np.linalg.solve, - but if A is singular, then we use Algorithm 3.3 from: + """Solve a singular linear system Ax = b for x. The same as + np.linalg.solve, but if A is singular, then we use Algorithm 3.3 from: Nocedal, Jorge, and Stephen J. Wright, eds. Numerical optimization. New York, NY: Springer New York, 1999. @@ -202,7 +202,8 @@ def solve_singular(a: cn.ndarray, b: cn.ndarray) -> cn.ndarray: def sample_average( y: cn.ndarray, sample_weight: Optional[cn.ndarray] = None ) -> cn.ndarray: - """Compute weighted average on the first axis (usually the sample dimension). + """Compute weighted average on the first axis (usually the sample + dimension). Returns 0 if sum weight is zero or if the input is empty. """ From 3debd21b1754cf311aaf8e3928194027d91da46c Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Fri, 29 Aug 2025 16:18:51 -0400 Subject: [PATCH 51/72] Update legate-boost.rst --- docs/source/legate-boost.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index 30ae38de..f53cd2f2 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -182,7 +182,7 @@ The following code creates a ``legate-boost`` regression model using ``LBRegressor``, which trains a gradient boosting model optimized for multi-GPU and multi-node environments. The model is configured to use 100 boosting rounds (n_estimators=100), with each round adding a -decision tree (lb.models.Tree) limited to a maximum depth of 5. The loss +decision tree (lb.models.Tree) limited to a maximum depth of 8. The loss function is set to squared_error, suitable for regression tasks as it minimizes mean squared error. A learning_rate of 0.1 controls how much each tree contributes to the final prediction, balancing speed and @@ -461,7 +461,7 @@ individually. model = lb.LBClassifier( base_models=( - lb.models.Tree(max_depth=8), + lb.models.Tree(max_depth=5), lb.models.NN(max_iter=10, hidden_layer_sizes=(10,10), verbose=True) ) ).fit(x_train, y_train) From ca2615596b70dd76e001e3215e0580491151d4fb Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Sat, 30 Aug 2025 17:31:11 -0400 Subject: [PATCH 52/72] updated data for CI --- docs/source/examples/housing.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/docs/source/examples/housing.py b/docs/source/examples/housing.py index 169f4bfe..1cbed32f 100644 --- a/docs/source/examples/housing.py +++ b/docs/source/examples/housing.py @@ -1,20 +1,28 @@ # [Import libraries] -from sklearn.datasets import fetch_california_housing +from sklearn.datasets import fetch_california_housing, make_regression from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split +import os import legateboost as lb from legate.timing import time # [Import data] -data = fetch_california_housing() +if os.environ.get("CI"): + X, y = make_regression(n_samples=100, n_features=5, n_targets=1, random_state=42) + total_estimators = 10 +else: + data = fetch_california_housing() + X, y = data.data, data.target + total_estimators = 100 + X_train, X_test, y_train, y_test = train_test_split( - data.data, data.target, test_size=0.2, random_state=42 + X, y, test_size=0.2, random_state=42 ) # [Create and fit Legate Boost regressor] model = lb.LBRegressor( - n_estimators=100, + n_estimators= total_estimators, base_models=(lb.models.Tree(max_depth=8),), objective="squared_error", learning_rate=0.1, From 3bbc63a7dfdbdf65f3aa07b0b095ca78824577cb Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Sat, 30 Aug 2025 17:53:22 -0400 Subject: [PATCH 53/72] Updated data for CI --- docs/source/examples/creditscore.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/docs/source/examples/creditscore.py b/docs/source/examples/creditscore.py index 47686492..5155ef0c 100644 --- a/docs/source/examples/creditscore.py +++ b/docs/source/examples/creditscore.py @@ -1,4 +1,5 @@ # [import libraries] +import os import cudf import cupy as cp import numpy as np @@ -8,7 +9,7 @@ from legate_dataframe.lib.core.column import LogicalColumn from legate_dataframe.lib.core.table import LogicalTable from legate_dataframe.lib.replace import replace_nulls -from sklearn.datasets import fetch_openml +from sklearn.datasets import fetch_openml, make_classification from sklearn.metrics import accuracy_score import legate.core as lg @@ -18,10 +19,16 @@ rt = lg.get_legate_runtime() # [import data] -data = fetch_openml(data_id=46929, as_frame=True) xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas -df = xd.DataFrame(data.data, columns=data.feature_names) -df["Target"] = data.target + +if os.environ.get("CI"): + X, y = make_classification(n_samples=100, n_features=10, n_classes=2, random_state=42) + df = xd.DataFrame(X, columns=[f"f{i}" for i in range(X.shape[1])]) + df["Target"] = y +else: + data = fetch_openml(data_id=46929, as_frame=True) + df = xd.DataFrame(data.data, columns=data.feature_names) + df["Target"] = data.target.astype(int) # [convert to LogicalTable] if cp.cuda.runtime.getDeviceCount() > 0: From 0e2ab90674bce99f364d3cb09bc5323e848a9d53 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Sat, 30 Aug 2025 18:06:33 -0400 Subject: [PATCH 54/72] Updated data and model for CI --- docs/source/examples/creditscore.py | 23 +++++++++++------------ 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/docs/source/examples/creditscore.py b/docs/source/examples/creditscore.py index 5155ef0c..a77cb3b6 100644 --- a/docs/source/examples/creditscore.py +++ b/docs/source/examples/creditscore.py @@ -9,7 +9,7 @@ from legate_dataframe.lib.core.column import LogicalColumn from legate_dataframe.lib.core.table import LogicalTable from legate_dataframe.lib.replace import replace_nulls -from sklearn.datasets import fetch_openml, make_classification +from sklearn.datasets import fetch_openml from sklearn.metrics import accuracy_score import legate.core as lg @@ -19,17 +19,14 @@ rt = lg.get_legate_runtime() # [import data] +data = fetch_openml(data_id=46929, as_frame=True) xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas +df = xd.DataFrame(data.data, columns=data.feature_names) +df["Target"] = data.target if os.environ.get("CI"): - X, y = make_classification(n_samples=100, n_features=10, n_classes=2, random_state=42) - df = xd.DataFrame(X, columns=[f"f{i}" for i in range(X.shape[1])]) - df["Target"] = y -else: - data = fetch_openml(data_id=46929, as_frame=True) - df = xd.DataFrame(data.data, columns=data.feature_names) - df["Target"] = data.target.astype(int) - + df = df.sample(n=100, random_state=42).reset_index(drop=True) + # [convert to LogicalTable] if cp.cuda.runtime.getDeviceCount() > 0: ldf = LogicalTable.from_cudf(df) @@ -79,11 +76,13 @@ # [training] rt.issue_execution_fence() start = time() - +nn_iter = 2 if os.environ.get("CI") else 10 +hidden_layers = (2,2) if os.environ.get("CI") else (10,10) + model = lb.LBClassifier( base_models=( lb.models.Tree(max_depth=5), - lb.models.NN(max_iter=10, hidden_layer_sizes=(10, 10), verbose=True), + lb.models.NN(max_iter= nn_iter, hidden_layer_sizes= hidden_layers, verbose=True), ) ).fit(x_train, y_train) @@ -103,7 +102,7 @@ # [Save model] dump(model, "legate_boost_model.joblib") -# [ Save test data [ +# [Save test data] x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) From d516ee611cf3357af20f220d0bec9f4c5e55a8e9 Mon Sep 17 00:00:00 2001 From: NihaalChowdary Date: Sat, 30 Aug 2025 18:44:08 -0400 Subject: [PATCH 55/72] Move tutorial examples to examples directory for CI --- .../examples => examples/tutorial_examples}/LBClassifier.py | 0 .../source/examples => examples/tutorial_examples}/LBRegressor.py | 0 .../source/examples => examples/tutorial_examples}/creditscore.py | 0 {docs/source/examples => examples/tutorial_examples}/housing.py | 0 {docs/source/examples => examples/tutorial_examples}/inference.py | 0 5 files changed, 0 insertions(+), 0 deletions(-) rename {docs/source/examples => examples/tutorial_examples}/LBClassifier.py (100%) rename {docs/source/examples => examples/tutorial_examples}/LBRegressor.py (100%) rename {docs/source/examples => examples/tutorial_examples}/creditscore.py (100%) rename {docs/source/examples => examples/tutorial_examples}/housing.py (100%) rename {docs/source/examples => examples/tutorial_examples}/inference.py (100%) diff --git a/docs/source/examples/LBClassifier.py b/examples/tutorial_examples/LBClassifier.py similarity index 100% rename from docs/source/examples/LBClassifier.py rename to examples/tutorial_examples/LBClassifier.py diff --git a/docs/source/examples/LBRegressor.py b/examples/tutorial_examples/LBRegressor.py similarity index 100% rename from docs/source/examples/LBRegressor.py rename to examples/tutorial_examples/LBRegressor.py diff --git a/docs/source/examples/creditscore.py b/examples/tutorial_examples/creditscore.py similarity index 100% rename from docs/source/examples/creditscore.py rename to examples/tutorial_examples/creditscore.py diff --git a/docs/source/examples/housing.py b/examples/tutorial_examples/housing.py similarity index 100% rename from docs/source/examples/housing.py rename to examples/tutorial_examples/housing.py diff --git a/docs/source/examples/inference.py b/examples/tutorial_examples/inference.py similarity index 100% rename from docs/source/examples/inference.py rename to examples/tutorial_examples/inference.py From 23e6f329d858cd3c8820eb2bdeda8753b2b4cbf8 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Sat, 30 Aug 2025 18:55:40 -0400 Subject: [PATCH 56/72] Updated the code files path --- docs/source/legate-boost.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index f53cd2f2..dfbdf324 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -81,7 +81,7 @@ function to generate predictions on new data. Here’s how to set it up: # predict y_pred = regression_model.predict(X_test) -.. literalinclude:: examples/LBRegressor.py +.. literalinclude:: ../../examples/tutorial_examples/LBRegressor.py :language: python In this example: @@ -130,7 +130,7 @@ using the ``LBClassifier`` estimator from ``legate-boost``: classification_model.fit(X_train, y_train) y_pred = classification_model.predict(X_test) -.. literalinclude:: examples/LBClassifier.py +.. literalinclude:: ../../examples/tutorial_examples/LBClassifier.py :language: python In this example: @@ -223,7 +223,7 @@ Code module print(f"Training time: {(end - start)/1000:.6f} ms") -.. literalinclude:: examples/housing.py +.. literalinclude:: ../../examples/tutorial_examples/housing.py :language: python This simple example demonstrates how to train a regression model on the @@ -370,7 +370,7 @@ computations across multiple GPUs and nodes. df = pa.Table.from_pandas(df) ldf = LogicalTable.from_arrow(df) -.. literalinclude:: examples/creditscore.py +.. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python :start-after: [import libraries] :end-before: [covert to LogicalTable end] @@ -414,7 +414,7 @@ tasks to execute efficiently across multiple GPUs or nodes. # [Convert-to-cupynumeric-array] data_arr = nldf.to_array() -.. literalinclude:: examples/creditscore.py +.. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python :start-after: [Replace nulls] :end-before: [convert to cupynumeric array end] @@ -469,7 +469,7 @@ individually. rt.issue_execution_fence() end = time() -.. literalinclude:: examples/creditscore.py +.. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python :start-after: [preparing data for training and testing] :end-before: [training end] @@ -499,7 +499,7 @@ model is saved with Joblib for future inference without retraining. pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) -.. literalinclude:: examples/creditscore.py +.. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python :start-after: [Prediction] @@ -600,7 +600,7 @@ into the speedup and variability achieved with GPU acceleration. print(f"Variance: {float(var_time)/1000:.2f} ms") print(f"Standard deviation: {float(std)/1000:.2f} ms") -.. literalinclude:: examples/inference.py +.. literalinclude:: ../../examples/tutorial_examples/inference.py :language: python From d58f7a6f17a8a0f87dea452527d203c10e50f795 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Sat, 30 Aug 2025 19:13:03 -0400 Subject: [PATCH 57/72] Update legate-boost.rst --- docs/source/legate-boost.rst | 224 ++++++++++++++++++++--------------- 1 file changed, 129 insertions(+), 95 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index dfbdf324..e7a5ac33 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -62,24 +62,29 @@ function to generate predictions on new data. Here’s how to set it up: .. code-block:: python - import legateboost as lb from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split - + + import legateboost as lb + # creating synthetic dataset X, y = make_regression(n_samples=100, n_features=4, noise=8, random_state=42) - + # splitting the data - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + # regression model with 100 estimators regression_model = lb.LBRegressor(n_estimators=100) - + # fit the model regression_model.fit(X_train, y_train) - + # predict y_pred = regression_model.predict(X_test) + + print(y_pred) .. literalinclude:: ../../examples/tutorial_examples/LBRegressor.py :language: python @@ -114,21 +119,28 @@ using the ``LBClassifier`` estimator from ``legate-boost``: .. code-block:: python - import legateboost as lb from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split - + + import legateboost as lb + # creating synthetic dataset X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42) - + # splitting the data - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + # classification model with 50 estimators classification_model = lb.LBClassifier(n_estimators=50) - + + # train the model classification_model.fit(X_train, y_train) + + # predictions y_pred = classification_model.predict(X_test) + print(y_pred) .. literalinclude:: ../../examples/tutorial_examples/LBClassifier.py :language: python @@ -194,34 +206,51 @@ Code module .. code-block:: python - import cupynumeric as cn - import legateboost as lb - from legate.timing import time + # [Import libraries] + from sklearn.datasets import fetch_california_housing, make_regression from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split - from sklearn.datasets import fetch_california_housing - - data = fetch_california_housing() - X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42) - + + import os + import legateboost as lb + from legate.timing import time + + # [Import data] + if os.environ.get("CI"): + X, y = make_regression(n_samples=100, n_features=5, n_targets=1, random_state=42) + total_estimators = 10 + else: + data = fetch_california_housing() + X, y = data.data, data.target + total_estimators = 100 + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + # [Create and fit Legate Boost regressor] model = lb.LBRegressor( - n_estimators=100, + n_estimators= total_estimators, base_models=(lb.models.Tree(max_depth=8),), objective="squared_error", learning_rate=0.1, - verbose=True + verbose=True, ) - + start = time() model.fit(X_train, y_train) end = time() - + + # [Prediction] + istart = time() y_pred = model.predict(X_test) - + iend = time() + + # [Evaluation] mse = mean_squared_error(y_test, y_pred) print(f"Test MSE: {mse:.4f}") - print(f"Training time: {(end - start)/1000:.6f} ms") - + print(f"\nThe training time for housing exp is: {(end - start)/1000:.6f} ms") + print(f"\nThe inference time for housing exp is {(iend - istart)/1000:.6f} ms") .. literalinclude:: ../../examples/tutorial_examples/housing.py :language: python @@ -340,36 +369,43 @@ computations across multiple GPUs and nodes. .. code-block:: python - # [import-libraries] + # [import libraries] + import os import cudf - import pandas import cupy as cp + import numpy as np + import pandas import pyarrow as pa - import legate_dataframe - import legateboost as lb - import cupynumeric as cpn - from legate.timing import time + from joblib import dump + from legate_dataframe.lib.core.column import LogicalColumn + from legate_dataframe.lib.core.table import LogicalTable + from legate_dataframe.lib.replace import replace_nulls from sklearn.datasets import fetch_openml from sklearn.metrics import accuracy_score - from legate_dataframe.lib.replace import replace_nulls - from legate_dataframe.lib.core.table import LogicalTable - from legate_dataframe.lib.core.column import LogicalColumn - + + import legate.core as lg + import legateboost as lb + from legate.timing import time + rt = lg.get_legate_runtime() - - # [import-data] + + # [import data] data = fetch_openml(data_id=46929, as_frame=True) xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas df = xd.DataFrame(data.data, columns=data.feature_names) - df['Target'] = data.target - - # [convert-to-LogicalTable] + df["Target"] = data.target + + if os.environ.get("CI"): + df = df.sample(n=100, random_state=42).reset_index(drop=True) + + # [convert to LogicalTable] if cp.cuda.runtime.getDeviceCount() > 0: ldf = LogicalTable.from_cudf(df) else: df = pa.Table.from_pandas(df) ldf = LogicalTable.from_arrow(df) + .. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python :start-after: [import libraries] @@ -392,28 +428,25 @@ tasks to execute efficiently across multiple GPUs or nodes. # [Replace nulls] median_salary = df["MonthlyIncome"].median() median_dependents = df["NumberOfDependents"].median() - - mmi = LogicalColumn( - replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary) - ) + + mmi = LogicalColumn(replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary)) mnd = LogicalColumn( replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) ) - - # [Create-new-LogicalTable-with-updated-columns] - + + # [Create new LogicalTable with updated columns] + features = ldf.get_column_names() nldf = LogicalTable( - [ - ldf[0], ldf[1], ldf[2], ldf[3], mmi, - ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10] - ], - features + [ldf[0], ldf[1], ldf[2], ldf[3], mmi, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], + features, ) - - # [Convert-to-cupynumeric-array] + + # [Convert to cupynumeric array] + data_arr = nldf.to_array() + .. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python :start-after: [Replace nulls] @@ -441,31 +474,33 @@ individually. .. code-block:: python - # [preparing-data-for-training-and-testing] - x = data_arr[:, :-1] # all columns except last + # [preparing data for training and testing] + x = data_arr[:, :-1] # all columns except last y = data_arr[:, -1] - + # [Splitting the data into training and testing] num_samples = x.shape[0] split_ratio = 0.8 split_index = int(num_samples * split_ratio) - + x_train = x[:split_index] y_train = y[:split_index] x_test = x[split_index:] y_test = y[split_index:] - + # [training] rt.issue_execution_fence() start = time() - + nn_iter = 2 if os.environ.get("CI") else 10 + hidden_layers = (2,2) if os.environ.get("CI") else (10,10) + model = lb.LBClassifier( base_models=( lb.models.Tree(max_depth=5), - lb.models.NN(max_iter=10, hidden_layer_sizes=(10,10), verbose=True) + lb.models.NN(max_iter= nn_iter, hidden_layer_sizes= hidden_layers, verbose=True), ) ).fit(x_train, y_train) - + rt.issue_execution_fence() end = time() @@ -483,21 +518,21 @@ model is saved with Joblib for future inference without retraining. # [Prediction] predictions = model.predict(x_test) print(type(predictions)) - + # [Evaluation] acc = accuracy_score(y_test, predictions) print("Accuracy:", acc) print(f"\nThe training time for creditscore exp is: {(end - start)/1000:.6f} ms") - + # [Save model] dump(model, "legate_boost_model.joblib") - - # [ Save test data [ + + # [Save test data] x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) - - pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) - pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) + + pandas.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) + pandas.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) .. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python @@ -561,20 +596,19 @@ into the speedup and variability achieved with GPU acceleration. .. code-block:: python - import cupynumeric as cp - from joblib import load - from legate.timing import time import pandas as pd + from joblib import load + + import cupynumeric as cpn import legate.core as lg - - timings = [] - - # load model and test data - model = load("legate_boost_model.joblib") - X = pd.read_csv("x_test.csv") - + from legate.timing import time + rt = lg.get_legate_runtime() - + + timings = [] + model = load("legate_boost_housing.joblib") + X = pd.read_csv("x_test_housing.csv") + for _ in range(10): rt.issue_execution_fence() start = time() @@ -582,23 +616,23 @@ into the speedup and variability achieved with GPU acceleration. rt.issue_execution_fence() end = time() timings.append(end - start) - - timings = timings[1:] # ignore first run - timings_gpu = cp.array(timings) - - mean_time = cp.mean(timings_gpu) - median_time = cp.median(timings_gpu) - min_time = cp.min(timings_gpu) - max_time = cp.max(timings_gpu) - var_time = cp.var(timings_gpu) - std = cp.sqrt(var_time) - + + timings = timings[1:] + timings_gpu = cpn.array(timings) + + mean_time = cpn.mean(timings_gpu) + median_time = cpn.median(timings_gpu) + min_time = cpn.min(timings_gpu) + max_time = cpn.max(timings_gpu) + var_time = cpn.var(timings_gpu) + std = cpn.sqrt(var_time) + print(f"Mean: {float(mean_time)/1000:.2f} ms") print(f"Median: {float(median_time)/1000:.2f} ms") print(f"Min: {float(min_time)/1000:.2f} ms") print(f"Max: {float(max_time)/1000:.2f} ms") print(f"Variance: {float(var_time)/1000:.2f} ms") - print(f"Standard deviation: {float(std)/1000:.2f} ms") + print(f"standard deviation: {float(std)/1000:.2f} ms") .. literalinclude:: ../../examples/tutorial_examples/inference.py :language: python From 2cdf9ee673d07c20eaf518d1b12a9696aa89dbdc Mon Sep 17 00:00:00 2001 From: Rory Mitchell Date: Mon, 1 Sep 2025 00:47:08 -0700 Subject: [PATCH 58/72] Fix pre-commit Signed-off-by: Rory Mitchell --- docs/source/legate-boost.rst | 94 +++++++++++------------ examples/tutorial_examples/creditscore.py | 11 +-- examples/tutorial_examples/housing.py | 7 +- legateboost/callbacks.py | 4 +- legateboost/encoder.py | 22 +++--- legateboost/legateboost.py | 33 ++++---- legateboost/metrics.py | 8 +- legateboost/models/base_model.py | 9 +-- legateboost/models/krr.py | 6 +- legateboost/models/linear.py | 10 +-- legateboost/objectives.py | 24 +++--- legateboost/utils.py | 7 +- 12 files changed, 116 insertions(+), 119 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index e7a5ac33..392fabe1 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -64,26 +64,26 @@ function to generate predictions on new data. Here’s how to set it up: from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split - + import legateboost as lb - + # creating synthetic dataset X, y = make_regression(n_samples=100, n_features=4, noise=8, random_state=42) - + # splitting the data X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) - + # regression model with 100 estimators regression_model = lb.LBRegressor(n_estimators=100) - + # fit the model regression_model.fit(X_train, y_train) - + # predict y_pred = regression_model.predict(X_test) - + print(y_pred) .. literalinclude:: ../../examples/tutorial_examples/LBRegressor.py @@ -121,23 +121,23 @@ using the ``LBClassifier`` estimator from ``legate-boost``: from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split - + import legateboost as lb - + # creating synthetic dataset X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42) - + # splitting the data X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) - + # classification model with 50 estimators classification_model = lb.LBClassifier(n_estimators=50) - + # train the model classification_model.fit(X_train, y_train) - + # predictions y_pred = classification_model.predict(X_test) print(y_pred) @@ -210,11 +210,11 @@ Code module from sklearn.datasets import fetch_california_housing, make_regression from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split - + import os import legateboost as lb from legate.timing import time - + # [Import data] if os.environ.get("CI"): X, y = make_regression(n_samples=100, n_features=5, n_targets=1, random_state=42) @@ -223,11 +223,11 @@ Code module data = fetch_california_housing() X, y = data.data, data.target total_estimators = 100 - + X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) - + # [Create and fit Legate Boost regressor] model = lb.LBRegressor( n_estimators= total_estimators, @@ -236,16 +236,16 @@ Code module learning_rate=0.1, verbose=True, ) - + start = time() model.fit(X_train, y_train) end = time() - + # [Prediction] istart = time() y_pred = model.predict(X_test) iend = time() - + # [Evaluation] mse = mean_squared_error(y_test, y_pred) print(f"Test MSE: {mse:.4f}") @@ -382,22 +382,22 @@ computations across multiple GPUs and nodes. from legate_dataframe.lib.replace import replace_nulls from sklearn.datasets import fetch_openml from sklearn.metrics import accuracy_score - + import legate.core as lg import legateboost as lb from legate.timing import time - + rt = lg.get_legate_runtime() - + # [import data] data = fetch_openml(data_id=46929, as_frame=True) xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas df = xd.DataFrame(data.data, columns=data.feature_names) df["Target"] = data.target - + if os.environ.get("CI"): df = df.sample(n=100, random_state=42).reset_index(drop=True) - + # [convert to LogicalTable] if cp.cuda.runtime.getDeviceCount() > 0: ldf = LogicalTable.from_cudf(df) @@ -428,22 +428,22 @@ tasks to execute efficiently across multiple GPUs or nodes. # [Replace nulls] median_salary = df["MonthlyIncome"].median() median_dependents = df["NumberOfDependents"].median() - + mmi = LogicalColumn(replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary)) mnd = LogicalColumn( replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) ) - + # [Create new LogicalTable with updated columns] - + features = ldf.get_column_names() nldf = LogicalTable( [ldf[0], ldf[1], ldf[2], ldf[3], mmi, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], features, ) - + # [Convert to cupynumeric array] - + data_arr = nldf.to_array() @@ -477,30 +477,30 @@ individually. # [preparing data for training and testing] x = data_arr[:, :-1] # all columns except last y = data_arr[:, -1] - + # [Splitting the data into training and testing] num_samples = x.shape[0] split_ratio = 0.8 split_index = int(num_samples * split_ratio) - + x_train = x[:split_index] y_train = y[:split_index] x_test = x[split_index:] y_test = y[split_index:] - + # [training] rt.issue_execution_fence() start = time() - nn_iter = 2 if os.environ.get("CI") else 10 + nn_iter = 2 if os.environ.get("CI") else 10 hidden_layers = (2,2) if os.environ.get("CI") else (10,10) - + model = lb.LBClassifier( base_models=( lb.models.Tree(max_depth=5), lb.models.NN(max_iter= nn_iter, hidden_layer_sizes= hidden_layers, verbose=True), ) ).fit(x_train, y_train) - + rt.issue_execution_fence() end = time() @@ -518,19 +518,19 @@ model is saved with Joblib for future inference without retraining. # [Prediction] predictions = model.predict(x_test) print(type(predictions)) - + # [Evaluation] acc = accuracy_score(y_test, predictions) print("Accuracy:", acc) print(f"\nThe training time for creditscore exp is: {(end - start)/1000:.6f} ms") - + # [Save model] dump(model, "legate_boost_model.joblib") - + # [Save test data] x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) - + pandas.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) pandas.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) @@ -598,17 +598,17 @@ into the speedup and variability achieved with GPU acceleration. import pandas as pd from joblib import load - + import cupynumeric as cpn import legate.core as lg from legate.timing import time - + rt = lg.get_legate_runtime() - + timings = [] model = load("legate_boost_housing.joblib") X = pd.read_csv("x_test_housing.csv") - + for _ in range(10): rt.issue_execution_fence() start = time() @@ -616,17 +616,17 @@ into the speedup and variability achieved with GPU acceleration. rt.issue_execution_fence() end = time() timings.append(end - start) - + timings = timings[1:] timings_gpu = cpn.array(timings) - + mean_time = cpn.mean(timings_gpu) median_time = cpn.median(timings_gpu) min_time = cpn.min(timings_gpu) max_time = cpn.max(timings_gpu) var_time = cpn.var(timings_gpu) std = cpn.sqrt(var_time) - + print(f"Mean: {float(mean_time)/1000:.2f} ms") print(f"Median: {float(median_time)/1000:.2f} ms") print(f"Min: {float(min_time)/1000:.2f} ms") diff --git a/examples/tutorial_examples/creditscore.py b/examples/tutorial_examples/creditscore.py index a77cb3b6..0eba268f 100644 --- a/examples/tutorial_examples/creditscore.py +++ b/examples/tutorial_examples/creditscore.py @@ -1,5 +1,6 @@ # [import libraries] import os + import cudf import cupy as cp import numpy as np @@ -26,7 +27,7 @@ if os.environ.get("CI"): df = df.sample(n=100, random_state=42).reset_index(drop=True) - + # [convert to LogicalTable] if cp.cuda.runtime.getDeviceCount() > 0: ldf = LogicalTable.from_cudf(df) @@ -76,13 +77,13 @@ # [training] rt.issue_execution_fence() start = time() -nn_iter = 2 if os.environ.get("CI") else 10 -hidden_layers = (2,2) if os.environ.get("CI") else (10,10) - +nn_iter = 2 if os.environ.get("CI") else 10 +hidden_layers = (2, 2) if os.environ.get("CI") else (10, 10) + model = lb.LBClassifier( base_models=( lb.models.Tree(max_depth=5), - lb.models.NN(max_iter= nn_iter, hidden_layer_sizes= hidden_layers, verbose=True), + lb.models.NN(max_iter=nn_iter, hidden_layer_sizes=hidden_layers, verbose=True), ) ).fit(x_train, y_train) diff --git a/examples/tutorial_examples/housing.py b/examples/tutorial_examples/housing.py index 1cbed32f..4054ec0f 100644 --- a/examples/tutorial_examples/housing.py +++ b/examples/tutorial_examples/housing.py @@ -1,9 +1,10 @@ # [Import libraries] +import os + from sklearn.datasets import fetch_california_housing, make_regression from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split -import os import legateboost as lb from legate.timing import time @@ -15,14 +16,14 @@ data = fetch_california_housing() X, y = data.data, data.target total_estimators = 100 - + X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) # [Create and fit Legate Boost regressor] model = lb.LBRegressor( - n_estimators= total_estimators, + n_estimators=total_estimators, base_models=(lb.models.Tree(max_depth=8),), objective="squared_error", learning_rate=0.1, diff --git a/legateboost/callbacks.py b/legateboost/callbacks.py index bacf2693..e17b26dc 100644 --- a/legateboost/callbacks.py +++ b/legateboost/callbacks.py @@ -57,8 +57,8 @@ def after_iteration( class EarlyStopping(TrainingCallback): - """Callback for early stopping during training. The last evaluation dataset - is used for early stopping. + """Callback for early stopping during training. The last evaluation dataset is + used for early stopping. Args: rounds (int): The number of rounds to wait for improvement before stopping. diff --git a/legateboost/encoder.py b/legateboost/encoder.py index 69008ba4..16737fd7 100644 --- a/legateboost/encoder.py +++ b/legateboost/encoder.py @@ -16,18 +16,18 @@ class TargetEncoder(TransformerMixin, BaseEstimator, PickleCupynumericMixin): - """TargetEncoder is a transformer that encodes categorical features using - the mean of the target variable. When `fit_transform` is called, a cross- + """TargetEncoder is a transformer that encodes categorical features using the + mean of the target variable. When `fit_transform` is called, a cross- validation procedure is used to generate encodings for each training fold, which are then applied to the test fold. `fit().transform()` differs from `fit_transform()` in that the former fits the encoder on all the data and - generates encodings for each feature. This encoder is modelled on the - sklearn TargetEncoder with only minor differences in how the CV folds are - generated. As it is difficult to rearrange and gather data from each fold - in distributed environment, training rows are kept in place and then - assigned a cv fold by generating a random integer in the range [0, - n_folds). As per sklearn, when smooth="auto", an empirical Bayes estimate - per [#]_ is used to avoid overfitting. + generates encodings for each feature. This encoder is modelled on the sklearn + TargetEncoder with only minor differences in how the CV folds are generated. + As it is difficult to rearrange and gather data from each fold in distributed + environment, training rows are kept in place and then assigned a cv fold by + generating a random integer in the range [0, n_folds). As per sklearn, when + smooth="auto", an empirical Bayes estimate per [#]_ is used to avoid + overfitting. .. [#] Micci-Barreca, Daniele. "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems." ACM SIGKDD explorations newsletter 3.1 (2001): 27-32. @@ -264,8 +264,8 @@ def _get_category_means( """Compute some label summary statistics for each category in the input data. - Returns a 3D array of shape (n_categories, n_outputs, 2) - containing the sum, count of the labels for each category. + Returns a 3D array of shape (n_categories, n_outputs, 2) containing the + sum, count of the labels for each category. """ task = get_legate_runtime().create_auto_task( user_context, user_lib.cffi.TARGET_ENCODER_MEAN diff --git a/legateboost/legateboost.py b/legateboost/legateboost.py index 6f283302..80c8beb2 100644 --- a/legateboost/legateboost.py +++ b/legateboost/legateboost.py @@ -313,8 +313,8 @@ def update( eval_result: EvalResult = {}, ) -> Self: """Update a gradient boosting model from the training set (X, y). This - method does not add any new models to the ensemble, only updates - existing models to fit the new data. + method does not add any new models to the ensemble, only updates existing + models to fit the new data. Parameters ---------- @@ -476,8 +476,8 @@ def __iter__(self) -> Any: return iter(self.models_) def __mul__(self, scalar: Any) -> Self: - """Gradient boosted models are linear in the predictions before the - non- linear link function is applied. This means that the model can be + """Gradient boosted models are linear in the predictions before the non- + linear link function is applied. This means that the model can be multiplied by a scalar, which subsequently scales all raw output predictions. This is useful for ensembling models. @@ -550,8 +550,8 @@ def global_attributions( n_samples: int = 5, check_efficiency: bool = False, ) -> Tuple[cn.array, cn.array]: - r"""Compute global feature attributions for the model. Global - attributions show the effect of a feature on a model's loss function. + r"""Compute global feature attributions for the model. Global attributions + show the effect of a feature on a model's loss function. We use a Shapley value approach to compute the attributions: :math:`Sh_i(v)=\frac{1}{|N|!} \sum_{\sigma \in \mathfrak{S}_d} \big[ v([\sigma]_{i-1} \cup\{i\}) - v([\sigma]_{i-1}) \big],` @@ -612,11 +612,10 @@ def local_attributions( n_samples: int = 5, check_efficiency: bool = False, ) -> Tuple[cn.array, cn.array]: - r"""Local feature attributions for model predictions. Shows the effect - of a feature on each output prediction. See the definition of Shapley - values in :func:`~legateboost.BaseModel.global_attributions`, where the - :math:`v` function is here the model prediction instead of the loss - function. + r"""Local feature attributions for model predictions. Shows the effect of a + feature on each output prediction. See the definition of Shapley values in + :func:`~legateboost.BaseModel.global_attributions`, where the :math:`v` + function is here the model prediction instead of the loss function. Parameters ---------- @@ -750,8 +749,8 @@ def partial_fit( eval_set: List[Tuple[cn.ndarray, ...]] = [], eval_result: EvalResult = {}, ) -> LBBase: - """This method is used for incremental (online) training of the model. - An additional `n_estimators` models will be added to the ensemble. + """This method is used for incremental (online) training of the model. An + additional `n_estimators` models will be added to the ensemble. Parameters ---------- @@ -928,8 +927,8 @@ def partial_fit( eval_result: EvalResult = {}, ) -> LBBase: """This method is used for incremental fitting on a batch of samples. - Requires the classes to be provided up front, as they may not be - inferred from the first batch. + Requires the classes to be provided up front, as they may not be inferred + from the first batch. Parameters ---------- @@ -1033,8 +1032,8 @@ def fit( return self def predict_raw(self, X: cn.ndarray) -> cn.ndarray: - """Predict pre-transformed values for samples in X. E.g. before - applying a sigmoid function. + """Predict pre-transformed values for samples in X. E.g. before applying a + sigmoid function. Parameters ---------- diff --git a/legateboost/metrics.py b/legateboost/metrics.py index 68e6a028..6f4065b5 100644 --- a/legateboost/metrics.py +++ b/legateboost/metrics.py @@ -145,8 +145,8 @@ def name(self) -> str: class GammaLLMetric(BaseMetric): - """The mean negative log likelihood of the labels, given parameters - predicted by the model.""" + """The mean negative log likelihood of the labels, given parameters predicted + by the model.""" @override def metric(self, y: cn.ndarray, pred: cn.ndarray, w: cn.ndarray) -> cn.ndarray: @@ -253,8 +253,8 @@ def name(self) -> str: class LogLossMetric(BaseMetric): - """Class for computing the logarithmic loss (logloss) metric between the - true labels and predicted labels. + """Class for computing the logarithmic loss (logloss) metric between the true + labels and predicted labels. For binary classification: diff --git a/legateboost/models/base_model.py b/legateboost/models/base_model.py index 39987f88..a1e88011 100644 --- a/legateboost/models/base_model.py +++ b/legateboost/models/base_model.py @@ -11,9 +11,9 @@ class BaseModel(PickleCupynumericMixin, ABC): """Base class for all models in LegateBoost. - Defines the interface for fitting, updating, and predicting a model, - as well as string representation and equality comparison. Implement - these methods to create a custom model. + Defines the interface for fitting, updating, and predicting a model, as well + as string representation and equality comparison. Implement these methods to + create a custom model. """ def set_random_state(self, random_state: np.random.RandomState) -> "BaseModel": @@ -27,8 +27,7 @@ def fit( g: cn.ndarray, h: cn.ndarray, ) -> "BaseModel": - """Fit the model to a second order Taylor expansion of the loss - function. + """Fit the model to a second order Taylor expansion of the loss function. Parameters ---------- diff --git a/legateboost/models/krr.py b/legateboost/models/krr.py index ef0de7ef..31af8d5a 100644 --- a/legateboost/models/krr.py +++ b/legateboost/models/krr.py @@ -35,9 +35,9 @@ def rbf(x: cn.ndarray, sigma: float) -> cn.ndarray: class KRR(BaseModel): - """Kernel Ridge Regression model using the Nyström approximation. The - accuracy of the approximation is governed by the parameter `n_components` - <= `n`. Effectively, `n_components` rows will be randomly sampled (without + """Kernel Ridge Regression model using the Nyström approximation. The accuracy + of the approximation is governed by the parameter `n_components` <= `n`. + Effectively, `n_components` rows will be randomly sampled (without replacement) from X in each boosting iteration. The kernel is fixed to be the RBF kernel: diff --git a/legateboost/models/linear.py b/legateboost/models/linear.py index f8757772..ec34594e 100644 --- a/legateboost/models/linear.py +++ b/legateboost/models/linear.py @@ -9,11 +9,11 @@ class Linear(BaseModel): - """Generalised linear model. Boosting linear models is equivalent to - fitting a single linear model where each boosting iteration is a newton - step. Note that the l2 penalty is applied to the weights of each model, as - opposed to the sum of all models. This can lead to different results when - compared to fitting a linear model with sklearn. + """Generalised linear model. Boosting linear models is equivalent to fitting a + single linear model where each boosting iteration is a newton step. Note that + the l2 penalty is applied to the weights of each model, as opposed to the sum + of all models. This can lead to different results when compared to fitting a + linear model with sklearn. It is recommended to normalize the data before fitting. This ensures regularisation is evenly applied to all features and prevents numerical issues. diff --git a/legateboost/objectives.py b/legateboost/objectives.py index eafa0b98..52865bb3 100644 --- a/legateboost/objectives.py +++ b/legateboost/objectives.py @@ -100,13 +100,12 @@ def initialise_prediction( class ClassificationObjective(BaseObjective): - """Extension of BaseObjective for classification problems, use can - optionaly define a method of extracting a class output from - probabilities.""" + """Extension of BaseObjective for classification problems, use can optionaly + define a method of extracting a class output from probabilities.""" def output_class(self, pred: cn.ndarray) -> cn.ndarray: - """Defined how to output class labels from transfored output. This may - be as simple as argmax over probabilities. + """Defined how to output class labels from transfored output. This may be + as simple as argmax over probabilities. Args: pred (cn.ndarray): The transformed predictions. @@ -340,8 +339,8 @@ def initialise_prediction( class GammaObjective(FitInterceptRegMixIn, Forecast): - """Regression with the :math:`\\Gamma` distribution function using the - shape scale parameterization.""" + """Regression with the :math:`\\Gamma` distribution function using the shape + scale parameterization.""" @override def gradient(self, y: cn.ndarray, pred: cn.ndarray) -> GradPair: @@ -420,8 +419,7 @@ def var(self, param: cn.ndarray) -> cn.ndarray: class QuantileObjective(BaseObjective): - """Minimises the quantile loss, otherwise known as check loss or pinball - loss. + """Minimises the quantile loss, otherwise known as check loss or pinball loss. :math:`L(y_i, p_i) = \\frac{1}{k}\\sum_{j=1}^{k} (q_j - \\mathbb{1})(y_i - p_{i, j})` @@ -477,8 +475,8 @@ def initialise_prediction( class LogLossObjective(ClassificationObjective): - """The Log Loss objective function for binary and multi-class - classification problems. + """The Log Loss objective function for binary and multi-class classification + problems. This objective function computes the log loss between the predicted and true labels. @@ -567,8 +565,8 @@ def initialise_prediction( class ExponentialObjective(ClassificationObjective, FitInterceptRegMixIn): - """Exponential loss objective function for binary classification. - Equivalent to the AdaBoost multiclass exponential loss in [1]. + """Exponential loss objective function for binary classification. Equivalent + to the AdaBoost multiclass exponential loss in [1]. Defined as: diff --git a/legateboost/utils.py b/legateboost/utils.py index fb7c3f95..c38d62d0 100644 --- a/legateboost/utils.py +++ b/legateboost/utils.py @@ -156,8 +156,8 @@ def get_store(input: Any) -> LogicalStore: def solve_singular(a: cn.ndarray, b: cn.ndarray) -> cn.ndarray: - """Solve a singular linear system Ax = b for x. The same as - np.linalg.solve, but if A is singular, then we use Algorithm 3.3 from: + """Solve a singular linear system Ax = b for x. The same as np.linalg.solve, + but if A is singular, then we use Algorithm 3.3 from: Nocedal, Jorge, and Stephen J. Wright, eds. Numerical optimization. New York, NY: Springer New York, 1999. @@ -202,8 +202,7 @@ def solve_singular(a: cn.ndarray, b: cn.ndarray) -> cn.ndarray: def sample_average( y: cn.ndarray, sample_weight: Optional[cn.ndarray] = None ) -> cn.ndarray: - """Compute weighted average on the first axis (usually the sample - dimension). + """Compute weighted average on the first axis (usually the sample dimension). Returns 0 if sum weight is zero or if the input is empty. """ From 0e050882f4c8e61100ef8206e746273aad0f899a Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 1 Sep 2025 22:24:27 -0400 Subject: [PATCH 59/72] Update legate-boost.rst --- docs/source/legate-boost.rst | 253 ----------------------------------- 1 file changed, 253 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index 392fabe1..0e26756f 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -60,31 +60,6 @@ demonstrates how to create an instance of the ``LBRegressor`` model, use the ``fit()`` function to train it on a dataset, and then apply the ``predict()`` function to generate predictions on new data. Here’s how to set it up: -.. code-block:: python - - from sklearn.datasets import make_regression - from sklearn.model_selection import train_test_split - - import legateboost as lb - - # creating synthetic dataset - X, y = make_regression(n_samples=100, n_features=4, noise=8, random_state=42) - - # splitting the data - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - # regression model with 100 estimators - regression_model = lb.LBRegressor(n_estimators=100) - - # fit the model - regression_model.fit(X_train, y_train) - - # predict - y_pred = regression_model.predict(X_test) - - print(y_pred) .. literalinclude:: ../../examples/tutorial_examples/LBRegressor.py :language: python @@ -117,31 +92,6 @@ classification, and sentiment analysis. The example below demonstrates how to implement a classification model using the ``LBClassifier`` estimator from ``legate-boost``: -.. code-block:: python - - from sklearn.datasets import make_classification - from sklearn.model_selection import train_test_split - - import legateboost as lb - - # creating synthetic dataset - X, y = make_classification(n_samples=100, n_features=4, n_classes=2, random_state=42) - - # splitting the data - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - # classification model with 50 estimators - classification_model = lb.LBClassifier(n_estimators=50) - - # train the model - classification_model.fit(X_train, y_train) - - # predictions - y_pred = classification_model.predict(X_test) - print(y_pred) - .. literalinclude:: ../../examples/tutorial_examples/LBClassifier.py :language: python @@ -204,54 +154,6 @@ allowing to monitor progress and internal operations. Code module ----------- -.. code-block:: python - - # [Import libraries] - from sklearn.datasets import fetch_california_housing, make_regression - from sklearn.metrics import mean_squared_error - from sklearn.model_selection import train_test_split - - import os - import legateboost as lb - from legate.timing import time - - # [Import data] - if os.environ.get("CI"): - X, y = make_regression(n_samples=100, n_features=5, n_targets=1, random_state=42) - total_estimators = 10 - else: - data = fetch_california_housing() - X, y = data.data, data.target - total_estimators = 100 - - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - # [Create and fit Legate Boost regressor] - model = lb.LBRegressor( - n_estimators= total_estimators, - base_models=(lb.models.Tree(max_depth=8),), - objective="squared_error", - learning_rate=0.1, - verbose=True, - ) - - start = time() - model.fit(X_train, y_train) - end = time() - - # [Prediction] - istart = time() - y_pred = model.predict(X_test) - iend = time() - - # [Evaluation] - mse = mean_squared_error(y_test, y_pred) - print(f"Test MSE: {mse:.4f}") - print(f"\nThe training time for housing exp is: {(end - start)/1000:.6f} ms") - print(f"\nThe inference time for housing exp is {(iend - istart)/1000:.6f} ms") - .. literalinclude:: ../../examples/tutorial_examples/housing.py :language: python @@ -367,45 +269,6 @@ Dataframe``. ``LogicalTables`` internally break data into ``logical columns``, enabling Legate’s runtime to partition, distribute, and schedule computations across multiple GPUs and nodes. -.. code-block:: python - - # [import libraries] - import os - import cudf - import cupy as cp - import numpy as np - import pandas - import pyarrow as pa - from joblib import dump - from legate_dataframe.lib.core.column import LogicalColumn - from legate_dataframe.lib.core.table import LogicalTable - from legate_dataframe.lib.replace import replace_nulls - from sklearn.datasets import fetch_openml - from sklearn.metrics import accuracy_score - - import legate.core as lg - import legateboost as lb - from legate.timing import time - - rt = lg.get_legate_runtime() - - # [import data] - data = fetch_openml(data_id=46929, as_frame=True) - xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas - df = xd.DataFrame(data.data, columns=data.feature_names) - df["Target"] = data.target - - if os.environ.get("CI"): - df = df.sample(n=100, random_state=42).reset_index(drop=True) - - # [convert to LogicalTable] - if cp.cuda.runtime.getDeviceCount() > 0: - ldf = LogicalTable.from_cudf(df) - else: - df = pa.Table.from_pandas(df) - ldf = LogicalTable.from_arrow(df) - - .. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python :start-after: [import libraries] @@ -423,30 +286,6 @@ GPU-accelerated array type that leverages logical partitioning for distributed computation. This enables the subsequent machine learning tasks to execute efficiently across multiple GPUs or nodes. -.. code-block:: python - - # [Replace nulls] - median_salary = df["MonthlyIncome"].median() - median_dependents = df["NumberOfDependents"].median() - - mmi = LogicalColumn(replace_nulls(LogicalColumn(ldf["MonthlyIncome"]), median_salary)) - mnd = LogicalColumn( - replace_nulls(LogicalColumn(ldf["NumberOfDependents"]), median_dependents) - ) - - # [Create new LogicalTable with updated columns] - - features = ldf.get_column_names() - nldf = LogicalTable( - [ldf[0], ldf[1], ldf[2], ldf[3], mmi, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], - features, - ) - - # [Convert to cupynumeric array] - - data_arr = nldf.to_array() - - .. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python :start-after: [Replace nulls] @@ -472,38 +311,6 @@ high-dimensional relationships. This ensemble design results in a more accurate and robust classifier than either model could achieve individually. -.. code-block:: python - - # [preparing data for training and testing] - x = data_arr[:, :-1] # all columns except last - y = data_arr[:, -1] - - # [Splitting the data into training and testing] - num_samples = x.shape[0] - split_ratio = 0.8 - split_index = int(num_samples * split_ratio) - - x_train = x[:split_index] - y_train = y[:split_index] - x_test = x[split_index:] - y_test = y[split_index:] - - # [training] - rt.issue_execution_fence() - start = time() - nn_iter = 2 if os.environ.get("CI") else 10 - hidden_layers = (2,2) if os.environ.get("CI") else (10,10) - - model = lb.LBClassifier( - base_models=( - lb.models.Tree(max_depth=5), - lb.models.NN(max_iter= nn_iter, hidden_layer_sizes= hidden_layers, verbose=True), - ) - ).fit(x_train, y_train) - - rt.issue_execution_fence() - end = time() - .. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python :start-after: [preparing data for training and testing] @@ -513,27 +320,6 @@ The trained ensemble model is used to generate predictions on the test set, and its accuracy is evaluated using ``accuracy_score()``. Finally, the model is saved with Joblib for future inference without retraining. -.. code-block:: python - - # [Prediction] - predictions = model.predict(x_test) - print(type(predictions)) - - # [Evaluation] - acc = accuracy_score(y_test, predictions) - print("Accuracy:", acc) - print(f"\nThe training time for creditscore exp is: {(end - start)/1000:.6f} ms") - - # [Save model] - dump(model, "legate_boost_model.joblib") - - # [Save test data] - x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) - y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) - - pandas.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) - pandas.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) - .. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python :start-after: [Prediction] @@ -594,45 +380,6 @@ GPU environments. This approach provides a simple yet powerful way to compare inference performance across hardware, offering clear insights into the speedup and variability achieved with GPU acceleration. -.. code-block:: python - - import pandas as pd - from joblib import load - - import cupynumeric as cpn - import legate.core as lg - from legate.timing import time - - rt = lg.get_legate_runtime() - - timings = [] - model = load("legate_boost_housing.joblib") - X = pd.read_csv("x_test_housing.csv") - - for _ in range(10): - rt.issue_execution_fence() - start = time() - model.predict(X) - rt.issue_execution_fence() - end = time() - timings.append(end - start) - - timings = timings[1:] - timings_gpu = cpn.array(timings) - - mean_time = cpn.mean(timings_gpu) - median_time = cpn.median(timings_gpu) - min_time = cpn.min(timings_gpu) - max_time = cpn.max(timings_gpu) - var_time = cpn.var(timings_gpu) - std = cpn.sqrt(var_time) - - print(f"Mean: {float(mean_time)/1000:.2f} ms") - print(f"Median: {float(median_time)/1000:.2f} ms") - print(f"Min: {float(min_time)/1000:.2f} ms") - print(f"Max: {float(max_time)/1000:.2f} ms") - print(f"Variance: {float(var_time)/1000:.2f} ms") - print(f"standard deviation: {float(std)/1000:.2f} ms") .. literalinclude:: ../../examples/tutorial_examples/inference.py :language: python From f1622d300e706e6a082b25273c120098d0e69ecc Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 1 Sep 2025 22:51:37 -0400 Subject: [PATCH 60/72] Update legate-boost.rst --- docs/source/legate-boost.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index 0e26756f..e08d6fcf 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -202,7 +202,7 @@ To run with GPU, use the following command. .. code-block:: sh - legate --gpus 2 ./housing.py + legate --gpus 1 ./housing.py This produces the following output: @@ -340,14 +340,14 @@ To run with CPU, use the following command. .. code-block:: sh - legate --cpus 1 --gpus 0 ./creditscore.py + legate --cpus 8 --gpus 0 ./creditscore.py This produces the following output: .. code-block:: text Accuracy: 0.9343 - The training time for credit score exp is : 45337.714000 ms + The training time for credit score exp is : 11003.714000 ms GPU execution ~~~~~~~~~~~~~ From e43f96df1fd3cd9f8587468f69df89360a7b8ecf Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 1 Sep 2025 23:03:07 -0400 Subject: [PATCH 61/72] Add files via upload --- .../legate_boost_model.joblib | Bin 0 -> 243774 bytes examples/tutorial_examples/x_test.csv | 30001 ++++++++++++++++ 2 files changed, 30001 insertions(+) create mode 100644 examples/tutorial_examples/legate_boost_model.joblib create mode 100644 examples/tutorial_examples/x_test.csv diff --git a/examples/tutorial_examples/legate_boost_model.joblib 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Date: Mon, 1 Sep 2025 23:06:09 -0400 Subject: [PATCH 62/72] Update inference.py --- examples/tutorial_examples/inference.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/tutorial_examples/inference.py b/examples/tutorial_examples/inference.py index bf217ab0..24c1d943 100644 --- a/examples/tutorial_examples/inference.py +++ b/examples/tutorial_examples/inference.py @@ -8,8 +8,8 @@ rt = lg.get_legate_runtime() timings = [] -model = load("legate_boost_housing.joblib") -X = pd.read_csv("x_test_housing.csv") +model = load("legate_boost_model.joblib") +X = pd.read_csv("x_test.csv") for _ in range(10): rt.issue_execution_fence() From 370aff02540ef8f720e562e2737ea525438ee7ff Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Mon, 1 Sep 2025 23:26:21 -0400 Subject: [PATCH 63/72] Update legate-boost.rst --- docs/source/legate-boost.rst | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index e08d6fcf..68b9970c 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -401,12 +401,12 @@ This produces the following output: .. code-block:: text - Mean: 265.66 ms - Median: 262.97 ms - Min: 249.78 ms - Max: 284.44 ms - Variance: 117319.15 ms - Standard deviation: 10.83 ms + Mean: 230.55 ms + Median: 232.81 ms + Min: 215.29 ms + Max: 242.65 ms + Variance: 94.77 ms + standard deviation: 9.73 ms GPU execution @@ -422,12 +422,12 @@ This produces the following output: .. code-block:: text - Mean: 122.35 ms - Median: 122.11 ms - Min: 121.28 ms - Max: 125.97 ms - Variance: 1793.76 ms - Standard deviation: 1.34 ms + Mean: 132.44 ms + Median: 131.58 ms + Min: 130.56 ms + Max: 136.72 ms + Variance: 3.42 ms + standard deviation: 1.85 ms These results clearly show the performance benefits of running inference on a GPU compared to a CPU using ``cupynumeric`` arrays. On the CPU, the From 906b64a1067d5afc461bdf466600dcffde4d8f47 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Tue, 2 Sep 2025 22:45:51 -0400 Subject: [PATCH 64/72] Update creditscore.py --- examples/tutorial_examples/creditscore.py | 18 ++++++------------ 1 file changed, 6 insertions(+), 12 deletions(-) diff --git a/examples/tutorial_examples/creditscore.py b/examples/tutorial_examples/creditscore.py index 0eba268f..ed1cdb57 100644 --- a/examples/tutorial_examples/creditscore.py +++ b/examples/tutorial_examples/creditscore.py @@ -1,10 +1,9 @@ # [import libraries] import os -import cudf import cupy as cp import numpy as np -import pandas +import pandas as pd import pyarrow as pa from joblib import dump from legate_dataframe.lib.core.column import LogicalColumn @@ -21,20 +20,15 @@ # [import data] data = fetch_openml(data_id=46929, as_frame=True) -xd = cudf if cp.cuda.runtime.getDeviceCount() > 0 else pandas -df = xd.DataFrame(data.data, columns=data.feature_names) +df = pd.DataFrame(data.data, columns=data.feature_names) df["Target"] = data.target if os.environ.get("CI"): df = df.sample(n=100, random_state=42).reset_index(drop=True) # [convert to LogicalTable] -if cp.cuda.runtime.getDeviceCount() > 0: - ldf = LogicalTable.from_cudf(df) -else: - df = pa.Table.from_pandas(df) - ldf = LogicalTable.from_arrow(df) - +df = pa.Table.from_pandas(df) +ldf = LogicalTable.from_arrow(df) # [covert to LogicalTable end] # [Replace nulls] @@ -107,5 +101,5 @@ x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) -pandas.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) -pandas.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) +pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) +pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) From b90ef3438f1920173241228cfdf49a8d9f09871f Mon Sep 17 00:00:00 2001 From: Rory Mitchell Date: Thu, 4 Sep 2025 02:19:06 -0700 Subject: [PATCH 65/72] Fix docs build, remove binary files Signed-off-by: Rory Mitchell --- conda/environments/all_cuda-122.yaml | 1 + dependencies.yaml | 1 + docs/source/index.rst | 1 + .../legate_boost_model.joblib | Bin 243774 -> 0 bytes examples/tutorial_examples/x_test.csv | 30001 ---------------- 5 files changed, 3 insertions(+), 30001 deletions(-) delete mode 100644 examples/tutorial_examples/legate_boost_model.joblib delete mode 100644 examples/tutorial_examples/x_test.csv diff --git a/conda/environments/all_cuda-122.yaml b/conda/environments/all_cuda-122.yaml index cae2f766..0e440e99 100644 --- a/conda/environments/all_cuda-122.yaml +++ b/conda/environments/all_cuda-122.yaml @@ -40,6 +40,7 @@ dependencies: - rich - scikit-build-core>=0.10.0 - scikit-learn>=1.6 +- scikit-learn>=1.6,!=1.7.1 - seaborn>=0.13 - sphinx>=8.0,<8.2.0 - typing-extensions>=4.0 diff --git a/dependencies.yaml b/dependencies.yaml index 14a5bd4b..05c5fe53 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -128,6 +128,7 @@ dependencies: common: - output_types: [conda, pyproject, requirements] packages: + - scikit-learn>=1.6,!=1.7.1 # 1.7.1 has doc build issues - myst-parser>=4.0 - pydata-sphinx-theme>=0.16 # the ceiling on sphinx can be removed when https://github.com/spatialaudio/nbsphinx/issues/825 is resolved diff --git a/docs/source/index.rst b/docs/source/index.rst index cc0dd88a..cb3f9655 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -11,6 +11,7 @@ legate-boost documentation Introduction Contributing Python API Reference + Tutorial Indices and tables ================== diff --git a/examples/tutorial_examples/legate_boost_model.joblib b/examples/tutorial_examples/legate_boost_model.joblib deleted file mode 100644 index ca37b3e5407908f9033a9d1c514c225f1c40cd1b..0000000000000000000000000000000000000000 GIT binary patch literal 0 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Pre-commit Signed-off-by: Rory Mitchell --- examples/tutorial_examples/creditscore.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/tutorial_examples/creditscore.py b/examples/tutorial_examples/creditscore.py index ed1cdb57..6d02dc7e 100644 --- a/examples/tutorial_examples/creditscore.py +++ b/examples/tutorial_examples/creditscore.py @@ -1,7 +1,6 @@ # [import libraries] import os -import cupy as cp import numpy as np import pandas as pd import pyarrow as pa From 7b397f3b0f3f82a1e839f5a49bb5cf414b8a8932 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 4 Sep 2025 11:25:51 -0400 Subject: [PATCH 67/72] Update creditscore.py --- examples/tutorial_examples/creditscore.py | 38 +++++++++++++++++------ 1 file changed, 29 insertions(+), 9 deletions(-) diff --git a/examples/tutorial_examples/creditscore.py b/examples/tutorial_examples/creditscore.py index 6d02dc7e..4a04186d 100644 --- a/examples/tutorial_examples/creditscore.py +++ b/examples/tutorial_examples/creditscore.py @@ -11,6 +11,7 @@ from sklearn.datasets import fetch_openml from sklearn.metrics import accuracy_score +import cupynumeric as cpn import legate.core as lg import legateboost as lb from legate.timing import time @@ -93,12 +94,31 @@ print("Accuracy:", acc) print(f"\nThe training time for creditscore exp is: {(end - start)/1000:.6f} ms") -# [Save model] -dump(model, "legate_boost_model.joblib") - -# [Save test data] -x_test_cpu = x_test.get() if hasattr(x_test, "get") else np.array(x_test) -y_test_cpu = y_test.get() if hasattr(y_test, "get") else np.array(y_test) - -pd.DataFrame(x_test_cpu).to_csv("x_test.csv", index=False) -pd.DataFrame(y_test_cpu, columns=["Target"]).to_csv("y_test.csv", index=False) +# [Inference] +rt = lg.get_legate_runtime() +timings = [] + +for _ in range(10): + rt.issue_execution_fence() + start = time() + model.predict(x_test) + rt.issue_execution_fence() + end = time() + timings.append(end - start) + +timings = timings[1:] +timings_gpu = cpn.array(timings) + +mean_time = cpn.mean(timings_gpu) +median_time = cpn.median(timings_gpu) +min_time = cpn.min(timings_gpu) +max_time = cpn.max(timings_gpu) +var_time = cpn.var(timings_gpu) +std = cpn.sqrt(var_time) + +print(f"Mean: {float(mean_time)/1000:.2f} ms") +print(f"Median: {float(median_time)/1000:.2f} ms") +print(f"Min: {float(min_time)/1000:.2f} ms") +print(f"Max: {float(max_time)/1000:.2f} ms") +print(f"Variance: {float(var_time)/1000:.2f} ms") +print(f"standard deviation: {float(std)/1000:.2f} ms") From 7ea6274e312c9ff3acf9222704cdba1e287e0c29 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 4 Sep 2025 12:25:56 -0400 Subject: [PATCH 68/72] Update legate-boost.rst --- docs/source/legate-boost.rst | 31 ++++++++++++++++--------------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index 68b9970c..3e773e6a 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -317,12 +317,12 @@ individually. :end-before: [training end] The trained ensemble model is used to generate predictions on the test -set, and its accuracy is evaluated using ``accuracy_score()``. Finally, the -model is saved with Joblib for future inference without retraining. +set, and its accuracy is evaluated using ``accuracy_score()``. .. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python :start-after: [Prediction] + :end-before: [Inference] This workflow illustrates how ``Legate Dataframe`` provides a scalable preprocessing layer, ``cupynumeric`` arrays enable distributed GPU @@ -347,7 +347,7 @@ This produces the following output: .. code-block:: text Accuracy: 0.9343 - The training time for credit score exp is : 11003.714000 ms + The training time for credit score exp is : 10912.335000 ms GPU execution ~~~~~~~~~~~~~ @@ -362,7 +362,7 @@ This produces the following output: .. code-block:: text - Accuracy: 0.9353 + Accuracy: 0.9357 The training time for credit score exp is : 2688.233000 ms Inference performance @@ -372,8 +372,8 @@ Let’s explore how ``cupynumeric`` can be leveraged to measure inference performance statistics seamlessly across both CPU and GPU all without modifying the code. In this example, we evaluate a pre-trained machine learning model by calculating key metrics such as mean, median, minimum, -maximum, variance, and standard deviation of inference times. The model -is loaded using joblib, and predictions are executed multiple times on +maximum, variance, and standard deviation of inference times. The pretrained model +is used here and the predictions are executed multiple times on the test dataset. By utilizing ``cupynumeric`` arrays, the timing results are efficiently processed while ensuring compatibility with both CPU and GPU environments. This approach provides a simple yet powerful way to @@ -381,8 +381,9 @@ compare inference performance across hardware, offering clear insights into the speedup and variability achieved with GPU acceleration. -.. literalinclude:: ../../examples/tutorial_examples/inference.py +.. literalinclude:: ../../examples/tutorial_examples/creditscore.py :language: python + :start-after: [Inference] Running on CPU and GPU @@ -395,18 +396,18 @@ To run with CPU, use the following command. .. code-block:: sh - legate --cpus 1 --gpus 0 ./inference.py + legate --cpus 8 --gpus 0 ./inference.py This produces the following output: .. code-block:: text - Mean: 230.55 ms - Median: 232.81 ms - Min: 215.29 ms - Max: 242.65 ms - Variance: 94.77 ms - standard deviation: 9.73 ms + Mean: 167.97 ms + Median: 170.25 ms + Min: 161.46 ms + Max: 176.31 ms + Variance: 23.52 ms + standard deviation: 4.85 ms GPU execution @@ -416,7 +417,7 @@ To run with GPU, use the following command. .. code-block:: sh - legate --gpus 1 ./inference.py + legate --gpus 2 ./inference.py This produces the following output: From 7a4ea3ad53121dfc890710a35fbf7a009ec23451 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 4 Sep 2025 12:29:22 -0400 Subject: [PATCH 69/72] merged creditscore.py and inference.py --- examples/tutorial_examples/creditscore.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/examples/tutorial_examples/creditscore.py b/examples/tutorial_examples/creditscore.py index 4a04186d..c21b4a3f 100644 --- a/examples/tutorial_examples/creditscore.py +++ b/examples/tutorial_examples/creditscore.py @@ -21,14 +21,14 @@ # [import data] data = fetch_openml(data_id=46929, as_frame=True) df = pd.DataFrame(data.data, columns=data.feature_names) -df["Target"] = data.target +df["Target"] = data.target.map({"No": 0, "Yes": 1}).astype(np.int8) if os.environ.get("CI"): df = df.sample(n=100, random_state=42).reset_index(drop=True) # [convert to LogicalTable] -df = pa.Table.from_pandas(df) -ldf = LogicalTable.from_arrow(df) +df_arrow = pa.Table.from_pandas(df) +ldf = LogicalTable.from_arrow(df_arrow) # [covert to LogicalTable end] # [Replace nulls] @@ -47,7 +47,6 @@ [ldf[0], ldf[1], ldf[2], ldf[3], mmi, ldf[5], ldf[6], ldf[7], ldf[8], mnd, ldf[10]], features, ) - # [Convert to cupynumeric array] data_arr = nldf.to_array() @@ -120,5 +119,5 @@ print(f"Median: {float(median_time)/1000:.2f} ms") print(f"Min: {float(min_time)/1000:.2f} ms") print(f"Max: {float(max_time)/1000:.2f} ms") -print(f"Variance: {float(var_time)/1000:.2f} ms") +print(f"Variance: {float(var_time)/1000000:.2f} ms") print(f"standard deviation: {float(std)/1000:.2f} ms") From 9a5182eb1938f3eb29c058dc14cb910cb07b3a75 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 4 Sep 2025 12:29:56 -0400 Subject: [PATCH 70/72] Delete examples/tutorial_examples/inference.py --- examples/tutorial_examples/inference.py | 37 ------------------------- 1 file changed, 37 deletions(-) delete mode 100644 examples/tutorial_examples/inference.py diff --git a/examples/tutorial_examples/inference.py b/examples/tutorial_examples/inference.py deleted file mode 100644 index 24c1d943..00000000 --- a/examples/tutorial_examples/inference.py +++ /dev/null @@ -1,37 +0,0 @@ -import pandas as pd -from joblib import load - -import cupynumeric as cpn -import legate.core as lg -from legate.timing import time - -rt = lg.get_legate_runtime() - -timings = [] -model = load("legate_boost_model.joblib") -X = pd.read_csv("x_test.csv") - -for _ in range(10): - rt.issue_execution_fence() - start = time() - model.predict(X) - rt.issue_execution_fence() - end = time() - timings.append(end - start) - -timings = timings[1:] -timings_gpu = cpn.array(timings) - -mean_time = cpn.mean(timings_gpu) -median_time = cpn.median(timings_gpu) -min_time = cpn.min(timings_gpu) -max_time = cpn.max(timings_gpu) -var_time = cpn.var(timings_gpu) -std = cpn.sqrt(var_time) - -print(f"Mean: {float(mean_time)/1000:.2f} ms") -print(f"Median: {float(median_time)/1000:.2f} ms") -print(f"Min: {float(min_time)/1000:.2f} ms") -print(f"Max: {float(max_time)/1000:.2f} ms") -print(f"Variance: {float(var_time)/1000:.2f} ms") -print(f"standard deviation: {float(std)/1000:.2f} ms") From a432096aefdb420eb2b180f87604df8db5a15523 Mon Sep 17 00:00:00 2001 From: Nihaal Chowdary Surpani <70621514+NihaalChowdary@users.noreply.github.com> Date: Thu, 4 Sep 2025 18:09:45 -0400 Subject: [PATCH 71/72] Updated the link to cupynumeric tutorial --- docs/source/legate-boost.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/legate-boost.rst b/docs/source/legate-boost.rst index 3e773e6a..ce76bec7 100644 --- a/docs/source/legate-boost.rst +++ b/docs/source/legate-boost.rst @@ -36,7 +36,7 @@ Please refer to `Distributed Computing with cupynumeric`_ and `legate boost`_ for more information and detailed instructions on installation and setup. -.. _Distributed Computing with cupynumeric: https://github.com/NVIDIA/accelerated-computing-hub/blob/main/Accelerated_Python_User_Guide/notebooks/Chapter_11_Distributed_Computing_cuPyNumeric.ipynb +.. _Distributed Computing with cupynumeric: https://docs.nvidia.com/cupynumeric/latest/user/tutorial.html .. _legate boost: https://github.com/rapidsai/legate-boost?tab=readme-ov-file#installation. From 07f19b2b1f02fe150bb3dda88c9c36fa7a34264a Mon Sep 17 00:00:00 2001 From: NihaalChowdary Date: Fri, 5 Sep 2025 13:29:50 -0400 Subject: [PATCH 72/72] Update docs and tutorial examples --- examples/tutorial_examples/creditscore.py | 1 - legateboost/callbacks.py | 4 +-- legateboost/encoder.py | 22 +++++++-------- legateboost/legateboost.py | 33 ++++++++++++----------- legateboost/metrics.py | 8 +++--- legateboost/models/base_model.py | 9 ++++--- legateboost/models/krr.py | 6 ++--- legateboost/models/linear.py | 10 +++---- legateboost/objectives.py | 24 +++++++++-------- legateboost/utils.py | 7 ++--- 10 files changed, 64 insertions(+), 60 deletions(-) diff --git a/examples/tutorial_examples/creditscore.py b/examples/tutorial_examples/creditscore.py index c21b4a3f..b6574b29 100644 --- a/examples/tutorial_examples/creditscore.py +++ b/examples/tutorial_examples/creditscore.py @@ -4,7 +4,6 @@ import numpy as np import pandas as pd import pyarrow as pa -from joblib import dump from legate_dataframe.lib.core.column import LogicalColumn from legate_dataframe.lib.core.table import LogicalTable from legate_dataframe.lib.replace import replace_nulls diff --git a/legateboost/callbacks.py b/legateboost/callbacks.py index e17b26dc..bacf2693 100644 --- a/legateboost/callbacks.py +++ b/legateboost/callbacks.py @@ -57,8 +57,8 @@ def after_iteration( class EarlyStopping(TrainingCallback): - """Callback for early stopping during training. The last evaluation dataset is - used for early stopping. + """Callback for early stopping during training. The last evaluation dataset + is used for early stopping. Args: rounds (int): The number of rounds to wait for improvement before stopping. diff --git a/legateboost/encoder.py b/legateboost/encoder.py index 16737fd7..69008ba4 100644 --- a/legateboost/encoder.py +++ b/legateboost/encoder.py @@ -16,18 +16,18 @@ class TargetEncoder(TransformerMixin, BaseEstimator, PickleCupynumericMixin): - """TargetEncoder is a transformer that encodes categorical features using the - mean of the target variable. When `fit_transform` is called, a cross- + """TargetEncoder is a transformer that encodes categorical features using + the mean of the target variable. When `fit_transform` is called, a cross- validation procedure is used to generate encodings for each training fold, which are then applied to the test fold. `fit().transform()` differs from `fit_transform()` in that the former fits the encoder on all the data and - generates encodings for each feature. This encoder is modelled on the sklearn - TargetEncoder with only minor differences in how the CV folds are generated. - As it is difficult to rearrange and gather data from each fold in distributed - environment, training rows are kept in place and then assigned a cv fold by - generating a random integer in the range [0, n_folds). As per sklearn, when - smooth="auto", an empirical Bayes estimate per [#]_ is used to avoid - overfitting. + generates encodings for each feature. This encoder is modelled on the + sklearn TargetEncoder with only minor differences in how the CV folds are + generated. As it is difficult to rearrange and gather data from each fold + in distributed environment, training rows are kept in place and then + assigned a cv fold by generating a random integer in the range [0, + n_folds). As per sklearn, when smooth="auto", an empirical Bayes estimate + per [#]_ is used to avoid overfitting. .. [#] Micci-Barreca, Daniele. "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems." ACM SIGKDD explorations newsletter 3.1 (2001): 27-32. @@ -264,8 +264,8 @@ def _get_category_means( """Compute some label summary statistics for each category in the input data. - Returns a 3D array of shape (n_categories, n_outputs, 2) containing the - sum, count of the labels for each category. + Returns a 3D array of shape (n_categories, n_outputs, 2) + containing the sum, count of the labels for each category. """ task = get_legate_runtime().create_auto_task( user_context, user_lib.cffi.TARGET_ENCODER_MEAN diff --git a/legateboost/legateboost.py b/legateboost/legateboost.py index 80c8beb2..6f283302 100644 --- a/legateboost/legateboost.py +++ b/legateboost/legateboost.py @@ -313,8 +313,8 @@ def update( eval_result: EvalResult = {}, ) -> Self: """Update a gradient boosting model from the training set (X, y). This - method does not add any new models to the ensemble, only updates existing - models to fit the new data. + method does not add any new models to the ensemble, only updates + existing models to fit the new data. Parameters ---------- @@ -476,8 +476,8 @@ def __iter__(self) -> Any: return iter(self.models_) def __mul__(self, scalar: Any) -> Self: - """Gradient boosted models are linear in the predictions before the non- - linear link function is applied. This means that the model can be + """Gradient boosted models are linear in the predictions before the + non- linear link function is applied. This means that the model can be multiplied by a scalar, which subsequently scales all raw output predictions. This is useful for ensembling models. @@ -550,8 +550,8 @@ def global_attributions( n_samples: int = 5, check_efficiency: bool = False, ) -> Tuple[cn.array, cn.array]: - r"""Compute global feature attributions for the model. Global attributions - show the effect of a feature on a model's loss function. + r"""Compute global feature attributions for the model. Global + attributions show the effect of a feature on a model's loss function. We use a Shapley value approach to compute the attributions: :math:`Sh_i(v)=\frac{1}{|N|!} \sum_{\sigma \in \mathfrak{S}_d} \big[ v([\sigma]_{i-1} \cup\{i\}) - v([\sigma]_{i-1}) \big],` @@ -612,10 +612,11 @@ def local_attributions( n_samples: int = 5, check_efficiency: bool = False, ) -> Tuple[cn.array, cn.array]: - r"""Local feature attributions for model predictions. Shows the effect of a - feature on each output prediction. See the definition of Shapley values in - :func:`~legateboost.BaseModel.global_attributions`, where the :math:`v` - function is here the model prediction instead of the loss function. + r"""Local feature attributions for model predictions. Shows the effect + of a feature on each output prediction. See the definition of Shapley + values in :func:`~legateboost.BaseModel.global_attributions`, where the + :math:`v` function is here the model prediction instead of the loss + function. Parameters ---------- @@ -749,8 +750,8 @@ def partial_fit( eval_set: List[Tuple[cn.ndarray, ...]] = [], eval_result: EvalResult = {}, ) -> LBBase: - """This method is used for incremental (online) training of the model. An - additional `n_estimators` models will be added to the ensemble. + """This method is used for incremental (online) training of the model. + An additional `n_estimators` models will be added to the ensemble. Parameters ---------- @@ -927,8 +928,8 @@ def partial_fit( eval_result: EvalResult = {}, ) -> LBBase: """This method is used for incremental fitting on a batch of samples. - Requires the classes to be provided up front, as they may not be inferred - from the first batch. + Requires the classes to be provided up front, as they may not be + inferred from the first batch. Parameters ---------- @@ -1032,8 +1033,8 @@ def fit( return self def predict_raw(self, X: cn.ndarray) -> cn.ndarray: - """Predict pre-transformed values for samples in X. E.g. before applying a - sigmoid function. + """Predict pre-transformed values for samples in X. E.g. before + applying a sigmoid function. Parameters ---------- diff --git a/legateboost/metrics.py b/legateboost/metrics.py index 6f4065b5..68e6a028 100644 --- a/legateboost/metrics.py +++ b/legateboost/metrics.py @@ -145,8 +145,8 @@ def name(self) -> str: class GammaLLMetric(BaseMetric): - """The mean negative log likelihood of the labels, given parameters predicted - by the model.""" + """The mean negative log likelihood of the labels, given parameters + predicted by the model.""" @override def metric(self, y: cn.ndarray, pred: cn.ndarray, w: cn.ndarray) -> cn.ndarray: @@ -253,8 +253,8 @@ def name(self) -> str: class LogLossMetric(BaseMetric): - """Class for computing the logarithmic loss (logloss) metric between the true - labels and predicted labels. + """Class for computing the logarithmic loss (logloss) metric between the + true labels and predicted labels. For binary classification: diff --git a/legateboost/models/base_model.py b/legateboost/models/base_model.py index a1e88011..39987f88 100644 --- a/legateboost/models/base_model.py +++ b/legateboost/models/base_model.py @@ -11,9 +11,9 @@ class BaseModel(PickleCupynumericMixin, ABC): """Base class for all models in LegateBoost. - Defines the interface for fitting, updating, and predicting a model, as well - as string representation and equality comparison. Implement these methods to - create a custom model. + Defines the interface for fitting, updating, and predicting a model, + as well as string representation and equality comparison. Implement + these methods to create a custom model. """ def set_random_state(self, random_state: np.random.RandomState) -> "BaseModel": @@ -27,7 +27,8 @@ def fit( g: cn.ndarray, h: cn.ndarray, ) -> "BaseModel": - """Fit the model to a second order Taylor expansion of the loss function. + """Fit the model to a second order Taylor expansion of the loss + function. Parameters ---------- diff --git a/legateboost/models/krr.py b/legateboost/models/krr.py index 31af8d5a..ef0de7ef 100644 --- a/legateboost/models/krr.py +++ b/legateboost/models/krr.py @@ -35,9 +35,9 @@ def rbf(x: cn.ndarray, sigma: float) -> cn.ndarray: class KRR(BaseModel): - """Kernel Ridge Regression model using the Nyström approximation. The accuracy - of the approximation is governed by the parameter `n_components` <= `n`. - Effectively, `n_components` rows will be randomly sampled (without + """Kernel Ridge Regression model using the Nyström approximation. The + accuracy of the approximation is governed by the parameter `n_components` + <= `n`. Effectively, `n_components` rows will be randomly sampled (without replacement) from X in each boosting iteration. The kernel is fixed to be the RBF kernel: diff --git a/legateboost/models/linear.py b/legateboost/models/linear.py index ec34594e..f8757772 100644 --- a/legateboost/models/linear.py +++ b/legateboost/models/linear.py @@ -9,11 +9,11 @@ class Linear(BaseModel): - """Generalised linear model. Boosting linear models is equivalent to fitting a - single linear model where each boosting iteration is a newton step. Note that - the l2 penalty is applied to the weights of each model, as opposed to the sum - of all models. This can lead to different results when compared to fitting a - linear model with sklearn. + """Generalised linear model. Boosting linear models is equivalent to + fitting a single linear model where each boosting iteration is a newton + step. Note that the l2 penalty is applied to the weights of each model, as + opposed to the sum of all models. This can lead to different results when + compared to fitting a linear model with sklearn. It is recommended to normalize the data before fitting. This ensures regularisation is evenly applied to all features and prevents numerical issues. diff --git a/legateboost/objectives.py b/legateboost/objectives.py index 52865bb3..eafa0b98 100644 --- a/legateboost/objectives.py +++ b/legateboost/objectives.py @@ -100,12 +100,13 @@ def initialise_prediction( class ClassificationObjective(BaseObjective): - """Extension of BaseObjective for classification problems, use can optionaly - define a method of extracting a class output from probabilities.""" + """Extension of BaseObjective for classification problems, use can + optionaly define a method of extracting a class output from + probabilities.""" def output_class(self, pred: cn.ndarray) -> cn.ndarray: - """Defined how to output class labels from transfored output. This may be - as simple as argmax over probabilities. + """Defined how to output class labels from transfored output. This may + be as simple as argmax over probabilities. Args: pred (cn.ndarray): The transformed predictions. @@ -339,8 +340,8 @@ def initialise_prediction( class GammaObjective(FitInterceptRegMixIn, Forecast): - """Regression with the :math:`\\Gamma` distribution function using the shape - scale parameterization.""" + """Regression with the :math:`\\Gamma` distribution function using the + shape scale parameterization.""" @override def gradient(self, y: cn.ndarray, pred: cn.ndarray) -> GradPair: @@ -419,7 +420,8 @@ def var(self, param: cn.ndarray) -> cn.ndarray: class QuantileObjective(BaseObjective): - """Minimises the quantile loss, otherwise known as check loss or pinball loss. + """Minimises the quantile loss, otherwise known as check loss or pinball + loss. :math:`L(y_i, p_i) = \\frac{1}{k}\\sum_{j=1}^{k} (q_j - \\mathbb{1})(y_i - p_{i, j})` @@ -475,8 +477,8 @@ def initialise_prediction( class LogLossObjective(ClassificationObjective): - """The Log Loss objective function for binary and multi-class classification - problems. + """The Log Loss objective function for binary and multi-class + classification problems. This objective function computes the log loss between the predicted and true labels. @@ -565,8 +567,8 @@ def initialise_prediction( class ExponentialObjective(ClassificationObjective, FitInterceptRegMixIn): - """Exponential loss objective function for binary classification. Equivalent - to the AdaBoost multiclass exponential loss in [1]. + """Exponential loss objective function for binary classification. + Equivalent to the AdaBoost multiclass exponential loss in [1]. Defined as: diff --git a/legateboost/utils.py b/legateboost/utils.py index c38d62d0..fb7c3f95 100644 --- a/legateboost/utils.py +++ b/legateboost/utils.py @@ -156,8 +156,8 @@ def get_store(input: Any) -> LogicalStore: def solve_singular(a: cn.ndarray, b: cn.ndarray) -> cn.ndarray: - """Solve a singular linear system Ax = b for x. The same as np.linalg.solve, - but if A is singular, then we use Algorithm 3.3 from: + """Solve a singular linear system Ax = b for x. The same as + np.linalg.solve, but if A is singular, then we use Algorithm 3.3 from: Nocedal, Jorge, and Stephen J. Wright, eds. Numerical optimization. New York, NY: Springer New York, 1999. @@ -202,7 +202,8 @@ def solve_singular(a: cn.ndarray, b: cn.ndarray) -> cn.ndarray: def sample_average( y: cn.ndarray, sample_weight: Optional[cn.ndarray] = None ) -> cn.ndarray: - """Compute weighted average on the first axis (usually the sample dimension). + """Compute weighted average on the first axis (usually the sample + dimension). Returns 0 if sum weight is zero or if the input is empty. """