Add Explicit hash_type Arguments and Device Fallback (#2077) · Project-MONAI/tutorials@b4b61f8 · GitHub
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Add Explicit hash_type Arguments and Device Fallback (#2077)
Fixes #2076. ### Description MONAI 1.6.1 hardened the use of downloading and extraction functions to use sha256 by default. This can be made compatible with old usage of these functions by explicitly adding `"md5"` as the `hash_type` to use this algorithm instead. In the future these should be changed to use sha256, this is lower priority since all the usage here relates to data downloading. ### Checks <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [ ] Avoid including large-size files in the PR. - [ ] Clean up long text outputs from code cells in the notebook. - [ ] For security purposes, please check the contents and remove any sensitive info such as user names and private key. - [ ] 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 - [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>` <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Bug Fixes** - Dataset downloads across tutorials and workflows now explicitly use MD5 checksum verification. - Training, inference, and preprocessing examples now automatically use available CUDA hardware or fall back to CPU when no GPU is detected. - CUDA-specific operations now avoid running when CUDA is unavailable. - **Documentation** - Updated notebook download steps and execution states for improved reproducibility. - The Deep Atlas tutorial is now included in the standard notebook execution workflow. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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‎.github/workflows/test-modified.yml‎

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‎2d_classification/mednist_tutorial.ipynb‎

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"compressed_file = os.path.join(root_dir, \"MedNIST.tar.gz\")\n",
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"data_dir = os.path.join(root_dir, \"MedNIST\")\n",
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"if not os.path.exists(data_dir):\n",
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" download_and_extract(resource, compressed_file, root_dir, md5)"
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" download_and_extract(resource, compressed_file, root_dir, md5, \"md5\")"
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]
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{

‎2d_classification/monai_201.ipynb‎

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"max_epochs = 5\n",
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"save_interval = 2\n",
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"out_dir = \"./eval\"\n",
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"model = densenet121(spatial_dims=2, in_channels=1, out_channels=6).to(\"cuda:0\")\n",
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"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
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"model = densenet121(spatial_dims=2, in_channels=1, out_channels=6).to(device)\n",
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"\n",
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"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
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"\n",
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"evaluator = SupervisedEvaluator(\n",
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" device=torch.device(\"cuda:0\"),\n",
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" device=device,\n",
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" val_data_loader=DataLoader(valdata, batch_size=512, shuffle=False, num_workers=4),\n",
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" network=model,\n",
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" inferer=SimpleInferer(),\n",
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")\n",
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"\n",
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"trainer = SupervisedTrainer(\n",
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" device=torch.device(\"cuda:0\"),\n",
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" device=device,\n",
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" max_epochs=max_epochs,\n",
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" train_data_loader=DataLoader(dataset, batch_size=512, shuffle=True, num_workers=4),\n",
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" network=model,\n",
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],
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"source": [
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"evaluator = SupervisedEvaluator(\n",
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" device=torch.device(\"cuda:0\"),\n",
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" device=device,\n",
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" val_data_loader=DataLoader(testdata, batch_size=1, num_workers=0),\n",
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" network=model,\n",
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" inferer=SimpleInferer(),\n",

‎2d_registration/registration_mednist.ipynb‎

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}
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],
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"source": [
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"device = torch.device(\"cuda:0\")\n",
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"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
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"model = GlobalNet(\n",
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" image_size=(64, 64), spatial_dims=2, in_channels=2, num_channel_initial=16, depth=3 # moving and fixed\n",
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").to(device)\n",

‎3d_classification/densenet_training_array.ipynb‎

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" dataset_dir = os.path.join(root_dir, \"ixi\")\n",
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" tarfile_name = f\"{dataset_dir}.tar\"\n",
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"\n",
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" download_and_extract(resource, tarfile_name, dataset_dir, md5)"
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" download_and_extract(resource, tarfile_name, dataset_dir, md5, \"md5\")"
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]
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{

‎3d_registration/learn2reg_nlst_paired_lung_ct.ipynb‎

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"outputs": [],
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"# device, optimizer, epoch and batch settings\n",
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"device = \"cuda:0\"\n",
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"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
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"batch_size = 4\n",
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"lr = 1e-4\n",
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"weight_decay = 1e-5\n",

‎3d_regression/densenet_training_array.ipynb‎

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" dataset_dir = os.path.join(root_dir, \"ixi\")\n",
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" tarfile_name = f\"{dataset_dir}.tar\"\n",
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"\n",
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" download_and_extract(resource, tarfile_name, dataset_dir, md5)"
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" download_and_extract(resource, tarfile_name, dataset_dir, md5, \"md5\")"
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]
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{

‎3d_segmentation/brats_segmentation_3d.ipynb‎

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"VAL_AMP = True\n",
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"\n",
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"# standard PyTorch program style: create SegResNet, DiceLoss and Adam optimizer\n",
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"device = torch.device(\"cuda:0\")\n",
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"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
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"model = SegResNet(\n",
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" blocks_down=[1, 2, 2, 4],\n",
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" blocks_up=[1, 1, 1],\n",

‎3d_segmentation/spleen_segmentation_3d.ipynb‎

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"compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
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"data_dir = os.path.join(root_dir, \"Task09_Spleen\")\n",
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"if not os.path.exists(data_dir):\n",
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" download_and_extract(resource, compressed_file, root_dir, md5)"
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" download_and_extract(resource, compressed_file, root_dir, md5, \"md5\")"
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"outputs": [],
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"source": [
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"# standard PyTorch program style: create UNet, DiceLoss and Adam optimizer\n",
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"device = torch.device(\"cuda:0\")\n",
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"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
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"model = UNet(\n",
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" spatial_dims=3,\n",
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" in_channels=1,\n",

‎3d_segmentation/spleen_segmentation_3d_lightning.ipynb‎

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