DOC-766 | Add new optional parameters to GraphML (profiles, enableGpu). (#833)

* add new parameters to ui, update screenshots

* add parameters to notebook-api file

* Optimize and rename screenshots

---------

Co-authored-by: Simran Spiller <simran@arangodb.com>

Author: nerpaula

site/content/ai-suite/graphml/notebooks-api.md

@@ -165,6 +165,7 @@ but you can substitute them as follows for a schema description in terms of JSON
 
 - `jobConfiguration` (dict, _optional): A set of configurations that are applied to the job.
   - `batchSize` (int): The number of documents to process in a single batch. Default is `32`.
+  - `profiles` (list): One or more profiles to specify pod configurations for the project (e.g., `["gpu-g4dn-xlarge"]`). Default is `None`.
   - `runAnalysisChecks` (bool): Whether to run analysis checks, used  to perform a high-level analysis of the data quality before proceeding. Default is `true`.
   - `skipLabels` (bool): Skips the featurization process for attributes marked as `label`. Default is `false`.
   - `useFeatureStore` (bool): Enables the use of the Feature Store database, which allows you to store features separately from your Source Database. Default is `false`, therefore features are written to the source graph.
@@ -417,11 +418,17 @@ The Training Service depends on a **Training Specification**:
     - `inputFeatures` (str): The name of the feature to be used as input.
     - `labelField` (str): The name of the attribute to be predicted.
     - `batchSize` (int): The number of documents to process in a single training batch. Default is `64`.
+    - `dataLoadBatchSize` (int): The number of documents loaded from ArangoDB into memory in a single batch during the data loading phase. Default is `50000`.
+    - `dataLoadParallelism` (int): The number of parallel processes used when loading data from ArangoDB into memory for training. Default is `10`.
+    - `enableGpu` (bool): Enables GPU-accelerated training using GPU-capable profiles configured for the project. Default is `false`.
   - `graphEmbeddings` (dict): Dictionary to describe the Graph Embedding Task Specification.
     - `targetCollection` (str): The ArangoDB collection used to generate the embeddings. 
     - `embeddingSize` (int): The size of the embedding vector. Default is `128`.
     - `batchSize` (int): The number of documents to process in a single training batch. Default is `64`.
     - `generateEmbeddings` (bool): Whether to generate embeddings on the training dataset. Default is `false`.
+    - `dataLoadBatchSize` (int): The number of documents loaded from ArangoDB into memory in a single batch during the data loading phase. Default is `50000`.
+    - `dataLoadParallelism` (int): The number of parallel processes used when loading data from ArangoDB into memory for training. Default is `10`.
+    - `enableGpu` (bool): Enables GPU-accelerated training using GPU-capable profiles configured for the project. Default is `false`.
 
 - `metagraph` (dict): Metadata to represent the node & edge collections of the graph. If `featureSetID` is provided, this can be omitted.
   - `graph` (str): The ArangoDB graph name.
@@ -736,6 +743,9 @@ The Prediction Service depends on a **Prediction Specification**:
 - `modelID` (str): The model ID to use for generating predictions.
 - `featurizeNewDocuments` (bool): Boolean for enabling or disabling the featurization of new documents. Useful if you don't want to re-train the model upon new data. Default is `false`.
 - `featurizeOutdatedDocuments` (bool): Boolean for enabling or disabling the featurization of outdated documents. Outdated documents are those whose features have changed since the last featurization. Default is `false`.
+- `dataLoadBatchSize` (int): The number of documents to load in a single batch. Default is `500000`.
+- `dataLoadParallelism` (int): The number of parallel threads used to process the prediction workload. Default is `10`.
+- `enableGpu` (bool): Enables GPU-accelerated prediction using GPU-capable profiles configured for the project. Default is `false`.
 - `schedule` (str): A cron expression to schedule the prediction job. The cron syntax is a set of
   five fields in a line, indicating when the job should be executed. The format must follow
   the following order: `minute` `hour` `day-of-month` `month` `day-of-week`

site/content/ai-suite/graphml/ui.md

@@ -23,7 +23,7 @@ To create a new GraphML project using the Arango Data Platform web interface, fo
 
 1. From the left-hand sidebar, select the database where you want to create the project.
 2. In the left-hand sidebar, click **AI Suite** to open the GraphML project management interface, then click **Run GraphML**.
-   ![Create GraphML Project](../../images/create-graphml-project-ui.png)  
+   ![Create GraphML Project](../../images/graphml-ui-create-project.png)  
 3. In the **GraphML projects** view, click **Add new project**.
 4. The **Create ML project** modal opens. Enter a **Name** for your machine learning project.
 5. Click the **Create project** button to finalize the creation.
@@ -54,6 +54,8 @@ format on the right side of the screen for transparency.
 In the **Configuration** tab, you can control the overall featurization job and
 how features are stored.
 - **Batch size**: The number of documents to process in a single batch.
+- **Profiles**: Add one or more profiles to specify pod configurations for the
+  project (e.g., `gpu-g4dn-xlarge`).
 - **Run analysis checks**: Whether to run analysis checks to perform a high-level
   analysis of the data quality before proceeding. The default value is `true`.
 - **Skip labels**: Skip the featurization process for attributes marked as labels.
@@ -73,20 +75,20 @@ Real-world datasets often contain missing values or mismatched data types. Use
 the strategies below to control how each feature type (**Text**, **Numeric**,
 **Category**, **Label**) handles these issues during featurization.
 
-| **Strategy type** | **Option**            | **Description**                                                                                     | **When to use**                                               |
-|-------------------|-----------------------|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------|
-| Missing           | **Raise**             | Stops the job and reports an error when a value is missing.                                         | When missing data indicates a critical issue.                 |
-|                   | **Replace**           | Substitutes missing values with a default you provide (e.g., `0` for numbers, `"unknown"` for text).                                  | When missing values are expected.         |
-| Mismatch          | **Raise**             | The strictest option. Stops the job on any data type mismatch.                                                            | When any data type mismatch indicates a critical error.                            |
-|                   | **Replace**           | Replaces mismatched values with a default you provide, without trying to convert it first.                              | When mismatched values are unreliable, and you prefer to substitute it directly.       |
-|                   | **Coerce and Raise**  | Attempts to convert (coerce) the value to the correct type (e.g. string "123" to number `123`). If the conversion is successful, it uses the new value. If it fails, the job stops.              | A balanced approach, often the best default strategy. |
-|                   | **Coerce and Replace**| The most forgiving option. The system first tries to convert the value. If it fails, it replaces the value with the specified default and continues the job.                                | For very dirty datasets where completing the job is the highest priority.          | 
+| **Strategy type** | **Option**             | **Description**                                                                                      | **When to use**                                                                  |
+|-------------------|------------------------|------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------|
+| Missing           | **Raise**              | Stops the job and reports an error when a value is missing.                                          | When missing data indicates a critical issue.                                    |
+|                   | **Replace**            | Substitutes missing values with a default you provide (e.g., `0` for numbers, `"unknown"` for text). | When missing values are expected.                                                |
+| Mismatch          | **Raise**              | The strictest option. Stops the job on any data type mismatch.                                       | When any data type mismatch indicates a critical error.                          |
+|                   | **Replace**            | Replaces mismatched values with a default you provide, without trying to convert it first.           | When mismatched values are unreliable, and you prefer to substitute it directly. |
+|                   | **Coerce and Raise**   | Attempts to convert (coerce) the value to the correct type (e.g. string "123" to number `123`). If the conversion is successful, it uses the new value. If it fails, the job stops. | A balanced approach, often the best default strategy.                     |
+|                   | **Coerce and Replace** | The most forgiving option. The system first tries to convert the value. If it fails, it replaces the value with the specified default and continues the job.                        | For very dirty datasets where completing the job is the highest priority. |
 
 Once you’ve set your strategies, click **Begin featurization** to start the node
 embedding-compatible featurization job. When the job status updates to
 **Ready for training**, proceed to the **Training** step.
 
-![Navigate to Featurization](../../images/graph-ml-ui-featurization.png) 
+![Navigate to Featurization](../../images/graphml-ui-featurization.png) 
 
 ## Training phase
 
@@ -112,10 +114,12 @@ features and structural connections within the graph.
 - **Batch Size**: The number of documents processed in a single training iteration. (e.g. `256`)
 - **Data Load Batch Size**: The number of documents loaded from ArangoDB into memory in a single batch during the data loading phase (e.g. `50000`).
 - **Data Load Parallelism**: The number of parallel processes used when loading data from ArangoDB into memory for training (e.g. `10`).
+- **Enable GPU**: Enables GPU-accelerated training using GPU-capable profiles
+  configured for the project (e.g., `gpu-g4dn-xlarge`).
 
 After setting these values, click the **Begin training** button to start the job.
 
-![Node Classification](../../images/ml-nodeclassification.png)
+![Node Classification](../../images/graphml-ui-node-classification.png)
 
 ####  Node embeddings
 
@@ -135,7 +139,7 @@ The target collection is where the model's predictions are stored when running a
 
 Once the configuration is complete, click **Begin training** to start the embedding job.
 
-![Node Embeddings](../../images/ml-node-embedding.png)
+![Node Embeddings](../../images/graphml-ui-node-embedding.png)
 
 ## Model selection phase
 
@@ -147,7 +151,7 @@ A list of trained models is displayed, along with performance metrics
 (**Accuracy**, **Precision**, **Recall**, **F1 score**, **Loss**). Review the results of different
 model runs and configurations.
 
-![GraphML Model Selection](../../images/graph-ml-model.png)
+![GraphML Model Selection](../../images/graphml-ui-model.png)
 
 Select the best performing model suitable for your prediction task. You can also
 open the **Confusion Matrix** to compare predicted values versus actual values.
@@ -186,8 +190,10 @@ predictions relevant without repeating the entire ML workflow.
 - **Data load parallelism**: The number of parallel threads used to process
   the prediction workload (e.g. `10`).
 - **Prediction field**: The field in the documents where the predicted values are stored.
+- **Enable GPU**: Enables GPU-accelerated prediction using GPU-capable profiles
+  configured for the project (e.g., `gpu-g4dn-xlarge`).
 
-![GraphML prediction phase](../../images/graph-prediction.png)
+![GraphML prediction phase](../../images/graphml-ui-prediction.png)
 
 ### Configuration options