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Update notebooks to match format requirements (#1160)
### Description This PR updated several tutorials to match the format requirements of contribution guideline. - [x] module tutorials - [x] pathology - [x] microscopy/multichannel_microscopy_classification.ipynb - [x] full_gpu_inference_pipeline/client/non_ensemble/client.ipynb - [x] federated_learning/openfl/openfl_mednist_2d_registration/workspace/Monai_MedNIST.ipynb - [x] deployment ### Checks <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Avoid including large-size files in the PR. - [x] Clean up long text outputs from code cells in the notebook. - [x] For security purposes, please check the contents and remove any sensitive info such as user names and private key. - [x] Ensure (1) hyperlinks and markdown anchors are working (2) use relative paths for tutorial repo files (3) put figure and graphs in the `./figure` folder - [x] Notebook runs automatically `./runner.sh -t <path to .ipynb file>` Signed-off-by: KumoLiu <yunl@nvidia.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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‎deployment/Triton/client/client.py‎

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# Copyright 2020 - 2021 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎deployment/Triton/client/client_mednist.py‎

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# Copyright 2020 - 2021 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎deployment/Triton/models/mednist_class/1/model.py‎

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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without

‎deployment/Triton/models/monai_covid/1/model.py‎

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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without

‎deployment/bentoml/mednist_classifier_bentoml.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Copyright (c) MONAI Consortium \n",
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"Licensed under the Apache License, Version 2.0 (the \"License\"); \n",
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"you may not use this file except in compliance with the License. \n",
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"You may obtain a copy of the License at \n",
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"&nbsp;&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0 \n",
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"Unless required by applicable law or agreed to in writing, software \n",
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"distributed under the License is distributed on an \"AS IS\" BASIS, \n",
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"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n",
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"See the License for the specific language governing permissions and \n",
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"limitations under the License."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm]\"\n",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MONAI version: 0.4.0+119.g9898a89\n",
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"Numpy version: 1.19.2\n",
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"Pytorch version: 1.7.1\n",
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"MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
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"MONAI rev id: 9898a89d24364a9be3525d066a7492adf00b9e6b\n",
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"MONAI version: 1.1.0+11.g7de6c336.dirty\n",
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"Numpy version: 1.22.2\n",
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"Pytorch version: 1.13.0+cu117\n",
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"MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
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"MONAI rev id: 7de6c33656a99087ca3b89a817b0879cf093febc\n",
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"MONAI __file__: /workspace/Code/MONAI/monai/__init__.py\n",
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"\n",
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"Optional dependencies:\n",
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"Pytorch Ignite version: 0.4.2\n",
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"Nibabel version: 3.2.1\n",
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"scikit-image version: 0.18.1\n",
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"Pillow version: 8.1.0\n",
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"Tensorboard version: 2.4.1\n",
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"gdown version: 3.12.2\n",
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"TorchVision version: 0.8.2\n",
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"ITK version: 5.1.2\n",
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"tqdm version: 4.56.0\n",
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"lmdb version: 1.0.0\n",
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"psutil version: 5.8.0\n",
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"Pytorch Ignite version: 0.4.10\n",
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"Nibabel version: 4.0.2\n",
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"scikit-image version: 0.19.3\n",
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"Pillow version: 9.0.1\n",
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"Tensorboard version: 2.11.0\n",
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"gdown version: 4.6.0\n",
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"TorchVision version: 0.14.0+cu117\n",
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"tqdm version: 4.64.1\n",
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"lmdb version: 1.3.0\n",
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"psutil version: 5.9.2\n",
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"pandas version: 1.1.5\n",
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"einops version: 0.6.0\n",
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"transformers version: 4.21.3\n",
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"mlflow version: 2.0.1\n",
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"pynrrd version: 1.0.0\n",
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"\n",
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"For details about installing the optional dependencies, please visit:\n",
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" https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
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}
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],
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"source": [
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"# Copyright 2020 MONAI Consortium\n",
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"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"# you may not use this file except in compliance with the License.\n",
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"# You may obtain a copy of the License at\n",
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"# http://www.apache.org/licenses/LICENSE-2.0\n",
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"# Unless required by applicable law or agreed to in writing, software\n",
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"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"# See the License for the specific language governing permissions and\n",
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"# limitations under the License.\n",
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"\n",
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"import os\n",
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"import shutil\n",
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"import tempfile\n",
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"from monai.networks.nets import DenseNet121\n",
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"from monai.engines import SupervisedTrainer\n",
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"from monai.transforms import (\n",
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" AddChannel,\n",
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" EnsureChannelFirst,\n",
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" Compose,\n",
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" LoadImage,\n",
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" RandFlip,\n",
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"print_config()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup data directory\n",
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"You can specify a directory with the MONAI_DATA_DIRECTORY environment variable.\n",
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"This allows you to save results and reuse downloads.\n",
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"If not specified a temporary directory will be used."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/workspace/Data\n"
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]
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}
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],
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"source": [
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"directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
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"root_dir = tempfile.mkdtemp() if directory is None else directory\n",
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"print(root_dir)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 3,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"MedNIST.tar.gz: 0.00B [00:00, ?B/s]"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/tmp/tmpxxp5z205\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"MedNIST.tar.gz: 59.0MB [00:04, 15.4MB/s] \n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"downloaded file: /tmp/tmpxxp5z205/MedNIST.tar.gz.\n",
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"Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
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"Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
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"root_dir = tempfile.mkdtemp() if directory is None else directory\n",
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"print(root_dir)\n",
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"\n",
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"resource = \"https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/MedNIST.tar.gz\"\n",
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"md5 = \"0bc7306e7427e00ad1c5526a6677552d\"\n",
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"\n",
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"cell_type": "code",
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_transforms = Compose(\n",
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" [\n",
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" LoadImage(image_only=True),\n",
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" AddChannel(),\n",
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" EnsureChannelFirst(),\n",
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" ScaleIntensity(),\n",
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" RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),\n",
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" RandFlip(spatial_axis=0, prob=0.5),\n",
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"cell_type": "code",
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"execution_count": 6,
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"execution_count": 7,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"device = torch.device(\"cuda:0\")\n",
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},
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"execution_count": 8,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/5 Loss: 0.231450617313385\n",
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"Epoch 2/5 Loss: 0.07256477326154709\n",
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"Epoch 3/5 Loss: 0.04309789836406708\n",
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"Epoch 4/5 Loss: 0.04549304023385048\n",
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"Epoch 5/5 Loss: 0.025731785222887993\n"
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"2023-01-13 07:55:59,514 - Engine run resuming from iteration 0, epoch 0 until 5 epochs\n",
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"Epoch 1/5 Loss: 0.19491052627563477\n",
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"2023-01-13 07:56:20,118 - Epoch[1] Complete. Time taken: 00:00:20.343\n",
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"Epoch 2/5 Loss: 0.11047982424497604\n",
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"2023-01-13 07:56:39,910 - Epoch[2] Complete. Time taken: 00:00:19.792\n",
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"Epoch 3/5 Loss: 0.023833362385630608\n",
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"2023-01-13 07:56:59,798 - Epoch[3] Complete. Time taken: 00:00:19.887\n",
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"Epoch 4/5 Loss: 0.02349323034286499\n",
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"2023-01-13 07:57:19,839 - Epoch[4] Complete. Time taken: 00:00:20.041\n",
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"Epoch 5/5 Loss: 0.01211395114660263\n",
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"2023-01-13 07:57:39,858 - Epoch[5] Complete. Time taken: 00:00:20.018\n",
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"2023-01-13 07:57:39,859 - Engine run complete. Time taken: 00:01:40.344\n"
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]
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}
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],
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"!docker image ls"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Cleanup data directory\n",
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"Remove directory if a temporary was used."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.10"
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"version": "3.8.13"
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}
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},
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"nbformat": 4,

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