Merge branch 'main' into docs/fix-typo-unet-metadata

Author: ericspod

README.md

@@ -41,6 +41,50 @@ Running:
 
 in a cell will verify this has worked and show you what kind of hardware you have access to.
 
+#### Google Colab Setup (CUDA 12.x, PyTorch 2.6, MONAI 1.5)
+
+In Google Colab, the default environment may cause version conflicts with MONAI.
+To ensure compatibility, install PyTorch and MONAI explicitly as follows:
+
+# Install PyTorch 2.6.0 with CUDA 12.4
+pip install --index-url https://download.pytorch.org/whl/cu124 \
+  torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0
+
+# Install MONAI and common dependencies
+pip install "monai[all]" nibabel pydicom ipywidgets==8.1.2
+
+
+### Known issues and fixes
+
+- Torchaudio mismatch
+  Colab may come with torchaudio 2.8.0, which is incompatible with torch 2.6.0.
+  Installing the versions above resolves this issue.
+
+- filelock conflicts with nni
+  Some preinstalled packages (such as pytensor with newer filelock) may conflict.
+  Use the following commands to fix:
+
+  pip uninstall -y pytensor
+  pip install -U filelock
+
+- Too many workers warning
+  Colab has limited CPU resources, and high num_workers settings may freeze execution.
+  It is recommended to use --num_workers=2 when running tutorials and adjust the `num_workers` parameters where it is used in notebooks (eg. for data loaders).
+
+
+### Quick smoke test
+
+After installation, verify the environment by running:
+
+git clone https://github.com/Project-MONAI/tutorials.git
+cd tutorials/3d_segmentation/torch
+python -u unet_training_array.py --max_epochs 2 --batch_size 1 --num_workers 2
+
+If the logs show decreasing training loss and a Dice score, the setup is correct.
+
+**Note:** In most cases, users can run MONAI tutorials directly in Colab notebooks without additional installation.
+The steps above are mainly for resolving dependency conflicts when installing extra packages.
+
 #### Data
 
 Some notebooks will require additional data.

generation/maisi/maisi_train_vae_tutorial.ipynb

@@ -692,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "4c251a32-390f-46dd-a613-75b12a7884c1",
    "metadata": {
     "scrolled": true
@@ -850,7 +850,7 @@
     "            with torch.no_grad():\n",
     "                with autocast(\"cuda\", enabled=args.amp):\n",
     "                    images = batch[\"image\"]\n",
-    "                    reconstruction, _, _ = dynamic_infer(val_inferer, autoencoder, images)\n",
+    "                    reconstruction, z_mu, z_sigma = dynamic_infer(val_inferer, autoencoder, images)\n",
     "                    reconstruction = reconstruction.to(device)\n",
     "                    val_epoch_losses[\"recons_loss\"] += intensity_loss(reconstruction, images.to(device)).item()\n",
     "                    val_epoch_losses[\"kl_loss\"] += KL_loss(z_mu, z_sigma).item()\n",

runner.sh

@@ -131,6 +131,7 @@ skip_run_papermill=("${skip_run_papermill[@]}" .*unet_segmentation_3d_ignite_cle
 skip_run_papermill=("${skip_run_papermill[@]}" .*vista_2d_tutorial_monai.ipynb*)
 skip_run_papermill=("${skip_run_papermill[@]}" .*learn2reg_oasis_unpaired_brain_mr.ipynb*)
 skip_run_papermill=("${skip_run_papermill[@]}" .*finetune_vista3d_for_hugging_face_pipeline.ipynb*)
+skip_run_papermill=("${skip_run_papermill[@]}" .*TCIA_PROSTATEx_Prostate_MRI_Anatomy_Model.ipynb*)  # https://github.com/Project-MONAI/tutorials/issues/2029
 
 # output formatting
 separator=""