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Merged
ericspod merged 10 commits into from
Sep 27, 2026
Merged

Add Explicit hash_type Arguments and Device Fallback #2077

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fdb5436
Add Explicit hash_type Arguments
ericspod Sep 21, 2026
6f0f3dc
Adjusting device selection to account for no CUDA
ericspod Sep 21, 2026
935599a
Undo needless metadata changes made by vscode
ericspod Sep 21, 2026
e38cb29
Fixes from comments
ericspod Sep 22, 2026
47ac6d0
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 22, 2026
c799861
Fixes
ericspod Sep 22, 2026
aac309e
Merge branch '2076_explicit_hash_type' of github.com:ericspod/tutoria…
ericspod Sep 22, 2026
957a8ac
Further comment updates
ericspod Sep 22, 2026
b44936f
Adding `date` to action to keep track of running time
ericspod Sep 22, 2026
f80f02b
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 22, 2026
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1 change: 1 addition & 0 deletions .github/workflows/test-modified.yml
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Expand Up @@ -53,6 +53,7 @@ jobs:
do
if [[ $line == *.ipynb ]]
then
date
./runner.sh -p " -and -wholename './${line}'"
fi
done
2 changes: 1 addition & 1 deletion 2d_classification/mednist_tutorial.ipynb
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Expand Up @@ -194,7 +194,7 @@
"compressed_file = os.path.join(root_dir, \"MedNIST.tar.gz\")\n",
"data_dir = os.path.join(root_dir, \"MedNIST\")\n",
"if not os.path.exists(data_dir):\n",
" download_and_extract(resource, compressed_file, root_dir, md5)"
" download_and_extract(resource, compressed_file, root_dir, md5, \"md5\")"
]
},
{
Expand Down
9 changes: 5 additions & 4 deletions 2d_classification/monai_201.ipynb
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Expand Up @@ -195,12 +195,13 @@
"max_epochs = 5\n",
"save_interval = 2\n",
"out_dir = \"./eval\"\n",
"model = densenet121(spatial_dims=2, in_channels=1, out_channels=6).to(\"cuda:0\")\n",
"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
"model = densenet121(spatial_dims=2, in_channels=1, out_channels=6).to(device)\n",
"\n",
"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
"\n",
"evaluator = SupervisedEvaluator(\n",
" device=torch.device(\"cuda:0\"),\n",
" device=device,\n",
" val_data_loader=DataLoader(valdata, batch_size=512, shuffle=False, num_workers=4),\n",
" network=model,\n",
" inferer=SimpleInferer(),\n",
Expand All @@ -209,7 +210,7 @@
")\n",
"\n",
"trainer = SupervisedTrainer(\n",
" device=torch.device(\"cuda:0\"),\n",
" device=device,\n",
" max_epochs=max_epochs,\n",
" train_data_loader=DataLoader(dataset, batch_size=512, shuffle=True, num_workers=4),\n",
" network=model,\n",
Expand Down Expand Up @@ -313,7 +314,7 @@
],
"source": [
"evaluator = SupervisedEvaluator(\n",
" device=torch.device(\"cuda:0\"),\n",
" device=device,\n",
" val_data_loader=DataLoader(testdata, batch_size=1, num_workers=0),\n",
" network=model,\n",
" inferer=SimpleInferer(),\n",
Expand Down
2 changes: 1 addition & 1 deletion 2d_registration/registration_mednist.ipynb
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Expand Up @@ -376,7 +376,7 @@
}
],
"source": [
"device = torch.device(\"cuda:0\")\n",
"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
"model = GlobalNet(\n",
" image_size=(64, 64), spatial_dims=2, in_channels=2, num_channel_initial=16, depth=3 # moving and fixed\n",
").to(device)\n",
Expand Down
2 changes: 1 addition & 1 deletion 3d_classification/densenet_training_array.ipynb
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Expand Up @@ -206,7 +206,7 @@
" dataset_dir = os.path.join(root_dir, \"ixi\")\n",
" tarfile_name = f\"{dataset_dir}.tar\"\n",
"\n",
" download_and_extract(resource, tarfile_name, dataset_dir, md5)"
" download_and_extract(resource, tarfile_name, dataset_dir, md5, \"md5\")"
]
},
{
Expand Down
2 changes: 1 addition & 1 deletion 3d_registration/learn2reg_nlst_paired_lung_ct.ipynb
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Expand Up @@ -598,7 +598,7 @@
"outputs": [],
"source": [
"# device, optimizer, epoch and batch settings\n",
"device = \"cuda:0\"\n",
"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
"batch_size = 4\n",
"lr = 1e-4\n",
"weight_decay = 1e-5\n",
Expand Down
2 changes: 1 addition & 1 deletion 3d_regression/densenet_training_array.ipynb
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Expand Up @@ -211,7 +211,7 @@
" dataset_dir = os.path.join(root_dir, \"ixi\")\n",
" tarfile_name = f\"{dataset_dir}.tar\"\n",
"\n",
" download_and_extract(resource, tarfile_name, dataset_dir, md5)"
" download_and_extract(resource, tarfile_name, dataset_dir, md5, \"md5\")"
]
},
{
Expand Down
2 changes: 1 addition & 1 deletion 3d_segmentation/brats_segmentation_3d.ipynb
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Expand Up @@ -442,7 +442,7 @@
"VAL_AMP = True\n",
"\n",
"# standard PyTorch program style: create SegResNet, DiceLoss and Adam optimizer\n",
"device = torch.device(\"cuda:0\")\n",
"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
Comment thread
ericspod marked this conversation as resolved.
"model = SegResNet(\n",
" blocks_down=[1, 2, 2, 4],\n",
" blocks_up=[1, 1, 1],\n",
Expand Down
4 changes: 2 additions & 2 deletions 3d_segmentation/spleen_segmentation_3d.ipynb
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Expand Up @@ -210,7 +210,7 @@
"compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
"data_dir = os.path.join(root_dir, \"Task09_Spleen\")\n",
"if not os.path.exists(data_dir):\n",
" download_and_extract(resource, compressed_file, root_dir, md5)"
" download_and_extract(resource, compressed_file, root_dir, md5, \"md5\")"
]
},
{
Expand Down Expand Up @@ -432,7 +432,7 @@
"outputs": [],
"source": [
"# standard PyTorch program style: create UNet, DiceLoss and Adam optimizer\n",
"device = torch.device(\"cuda:0\")\n",
"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
"model = UNet(\n",
" spatial_dims=3,\n",
" in_channels=1,\n",
Expand Down
6 changes: 3 additions & 3 deletions 3d_segmentation/spleen_segmentation_3d_lightning.ipynb
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Expand Up @@ -206,7 +206,7 @@
"compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
"data_dir = os.path.join(root_dir, \"Task09_Spleen\")\n",
"if not os.path.exists(data_dir):\n",
" download_and_extract(resource, compressed_file, root_dir, md5)"
" download_and_extract(resource, compressed_file, root_dir, md5, \"md5\")"
]
},
{
Expand Down Expand Up @@ -432,7 +432,7 @@
"\n",
"# initialise Lightning's trainer.\n",
"trainer = pytorch_lightning.Trainer(\n",
" devices=[0],\n",
" devices=1,\n",
" max_epochs=600,\n",
" logger=tb_logger,\n",
" enable_checkpointing=True,\n",
Expand Down Expand Up @@ -652,7 +652,7 @@
],
"source": [
"net.eval()\n",
"device = torch.device(\"cuda:0\")\n",
"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
"net.to(device)\n",
"with torch.no_grad():\n",
" for i, val_data in enumerate(net.val_dataloader()):\n",
Expand Down
2 changes: 1 addition & 1 deletion 3d_segmentation/spleen_segmentation_3d_visualization_basic.ipynb
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Expand Up @@ -230,7 +230,7 @@
"compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
"data_dir = os.path.join(root_dir, \"Task09_Spleen\")\n",
"if not os.path.exists(data_dir):\n",
" download_and_extract(resource, compressed_file, root_dir, md5)"
" download_and_extract(resource, compressed_file, root_dir, md5, \"md5\")"
]
},
{
Expand Down
2 changes: 1 addition & 1 deletion 3d_segmentation/unet_segmentation_3d_ignite.ipynb
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Expand Up @@ -263,7 +263,7 @@
"outputs": [],
"source": [
"# Create UNet, DiceLoss and Adam optimizer\n",
"device = torch.device(\"cuda:0\")\n",
"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
"net = UNet(\n",
" spatial_dims=3,\n",
" in_channels=1,\n",
Expand Down
2 changes: 1 addition & 1 deletion 3d_segmentation/unetr_btcv_segmentation_3d_lightning.ipynb
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Expand Up @@ -637,7 +637,7 @@
"\n",
"# initialise Lightning's trainer.\n",
"trainer = pytorch_lightning.Trainer(\n",
" devices=[0],\n",
" devices=1,\n",
" max_epochs=net.max_epochs,\n",
" check_val_every_n_epoch=net.check_val,\n",
" callbacks=checkpoint_callback,\n",
Expand Down
2 changes: 1 addition & 1 deletion acceleration/TensorRT_inference_acceleration.ipynb
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Expand Up @@ -169,7 +169,7 @@
"compressed_file = os.path.join(root_dir, \"endoscopic_tool_dataset.zip\")\n",
"data_root = os.path.join(root_dir, \"endoscopic_tool_dataset\")\n",
"if not os.path.exists(data_root):\n",
" download_and_extract(resource, compressed_file, root_dir, md5)"
" download_and_extract(resource, compressed_file, root_dir, md5, \"md5\")"
]
},
{
Expand Down
36 changes: 23 additions & 13 deletions acceleration/automatic_mixed_precision.ipynb
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Expand Up @@ -90,12 +90,17 @@
" ScaleIntensityRanged,\n",
" Spacingd,\n",
")\n",
"from monai.utils import get_torch_version_tuple, set_determinism\n",
"from monai.utils import set_determinism\n",
"\n",
"print_config()\n",
"\n",
"if get_torch_version_tuple() < (1, 6):\n",
" raise RuntimeError(\"AMP feature only exists in PyTorch version greater than v1.6.\")"
"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
"\n",
"if not torch.cuda.is_available() or torch.cuda.device_count() == 0:\n",
" print(\n",
" \"Warning: no CUDA device available, this notebook will still run but the AMP \"\n",
" \"feature will not provide any acceleration.\"\n",
" )"
]
},
{
Expand Down Expand Up @@ -145,7 +150,7 @@
"compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
"data_root = os.path.join(root_dir, \"Task09_Spleen\")\n",
"if not os.path.exists(data_root):\n",
" download_and_extract(resource, compressed_file, root_dir, md5)"
" download_and_extract(resource, compressed_file, root_dir, md5, \"md5\")"
]
},
{
Expand Down Expand Up @@ -277,7 +282,6 @@
" num_workers=1,\n",
" )\n",
" val_loader = DataLoader(val_ds, batch_size=1, num_workers=1)\n",
" device = torch.device(\"cuda:0\")\n",
" model = UNet(\n",
" spatial_dims=3,\n",
" in_channels=1,\n",
Expand Down Expand Up @@ -431,7 +435,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -471,8 +475,11 @@
}
],
"source": [
"print(torch.cuda.get_device_name(0))\n",
"print(torch.cuda.memory_summary(0, abbreviated=True))"
"if device.type == \"cuda\":\n",
" print(torch.cuda.get_device_name(0))\n",
" print(torch.cuda.memory_summary(0, abbreviated=True))\n",
"else:\n",
" print(\"Not using a CUDA device!\")"
]
},
{
Expand Down Expand Up @@ -511,7 +518,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -551,8 +558,11 @@
}
],
"source": [
"print(torch.cuda.get_device_name(0))\n",
"print(torch.cuda.memory_summary(0, abbreviated=True))"
"if device.type == \"cuda\":\n",
" print(torch.cuda.get_device_name(0))\n",
" print(torch.cuda.memory_summary(0, abbreviated=True))\n",
"else:\n",
" print(\"Not using a CUDA device!\")"
]
},
{
Expand Down Expand Up @@ -787,7 +797,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "monai",
"language": "python",
"name": "python3"
},
Expand All @@ -801,7 +811,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
"version": "3.10.20"
}
},
"nbformat": 4,
Expand Down
4 changes: 2 additions & 2 deletions acceleration/dataset_type_performance.ipynb
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Expand Up @@ -156,7 +156,7 @@
" num_workers=8,\n",
" )\n",
" val_loader = DataLoader(val_ds, batch_size=1, num_workers=4)\n",
" device = torch.device(\"cuda:0\")\n",
" device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
" model = UNet(\n",
" spatial_dims=3,\n",
" in_channels=1,\n",
Expand Down Expand Up @@ -311,7 +311,7 @@
"compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
"data_dir = os.path.join(root_dir, \"Task09_Spleen\")\n",
"if not os.path.exists(data_dir):\n",
" download_and_extract(resource, compressed_file, root_dir, md5)"
" download_and_extract(resource, compressed_file, root_dir, md5, \"md5\")"
]
},
{
Expand Down
2 changes: 1 addition & 1 deletion acceleration/fast_training_tutorial.ipynb
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Expand Up @@ -252,7 +252,7 @@
"compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
"data_root = os.path.join(root_dir, \"Task09_Spleen\")\n",
"if not os.path.exists(data_root):\n",
" download_and_extract(resource, compressed_file, root_dir, md5)"
" download_and_extract(resource, compressed_file, root_dir, md5, \"md5\")"
]
},
{
Expand Down
2 changes: 1 addition & 1 deletion acceleration/threadbuffer_performance.ipynb
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Expand Up @@ -120,7 +120,7 @@
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(\"cuda:0\")\n",
"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
"net = UNet(2, 1, 1, (8, 16, 32), (2, 2), num_res_units=2).to(device)\n",
"loss_function = Dice(sigmoid=True)\n",
"optimizer = torch.optim.Adam(net.parameters(), 1e-5)\n",
Expand Down
26 changes: 17 additions & 9 deletions acceleration/transform_speed.ipynb
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Expand Up @@ -84,7 +84,12 @@
")\n",
"from monai.utils import first\n",
"\n",
"print_config()"
"print_config()\n",
"\n",
"device = torch.device(\"cuda:0\" if torch.cuda.device_count() > 0 else \"cpu\")\n",
"\n",
"if not torch.cuda.is_available() or torch.cuda.device_count() == 0:\n",
" print(\"Warning: no CUDA device available, this notebook will run but no GPU acceleration will be present.\")"
]
},
{
Expand Down Expand Up @@ -308,7 +313,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {
"tags": []
},
Expand All @@ -332,15 +337,15 @@
" translate_range=(96, 96, 96),\n",
" spatial_size=(64, 64, 64),\n",
" mode=\"bilinear\",\n",
" device=torch.device(\"cuda:0\"),\n",
" device=device,\n",
")\n",
"rand_affine_seg = RandAffine(\n",
" prob=1.0,\n",
" rotate_range=np.pi / 4,\n",
" translate_range=(96, 96, 96),\n",
" spatial_size=(64, 64, 64),\n",
" mode=\"nearest\",\n",
" device=torch.device(\"cuda:0\"),\n",
" device=device,\n",
")\n",
"\n",
"imtrans = Compose([LoadImage(image_only=True), ScaleIntensity(), EnsureChannelFirst(), rand_affine_img])\n",
Expand Down Expand Up @@ -377,7 +382,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {
"tags": []
},
Expand Down Expand Up @@ -415,8 +420,11 @@
}
],
"source": [
"print(torch.cuda.get_device_name(0))\n",
"print(torch.cuda.memory_summary(0, abbreviated=True))"
"if device.type == \"cuda\":\n",
" print(torch.cuda.get_device_name(0))\n",
" print(torch.cuda.memory_summary(0, abbreviated=True))\n",
"else:\n",
" print(\"Not using a CUDA device!\")"
]
},
{
Expand Down Expand Up @@ -458,7 +466,7 @@
" spatial_size=(64, 64, 64),\n",
" mode=3,\n",
" padding_mode=\"reflect\",\n",
" device=torch.device(\"cuda:0\"),\n",
" device=device,\n",
")\n",
"rand_affine_seg = RandAffine(\n",
" prob=1.0,\n",
Expand All @@ -467,7 +475,7 @@
" spatial_size=(64, 64, 64),\n",
" mode=0,\n",
" padding_mode=\"reflect\",\n",
" device=torch.device(\"cuda:0\"),\n",
" device=device,\n",
")\n",
"\n",
"imtrans = Compose([LoadImage(image_only=True), ScaleIntensity(), EnsureChannelFirst(), rand_affine_img])\n",
Expand Down
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