add non-deterministic note

README.md

@@ -62,11 +62,13 @@ Dice score was used for evaluating the performance of the model. This model achi
 - Enhancing tumor (ET): 0.7905
 - Average: 0.8518
 
+Please note that this bundle is non-deterministic because of the trilinear interpolation used in the network. Therefore, reproducing the training process may not get exactly the same performance.
+Please refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.
+
 #### Training Loss and Dice
 ![A graph showing the training loss and the mean dice over 300 epochs](https://developer.download.nvidia.com/assets/Clara/Images/monai_brats_mri_segmentation_train.png)
 
 #### Validation Dice
-
 ![A graph showing the validation mean dice over 300 epochs](https://developer.download.nvidia.com/assets/Clara/Images/monai_brats_mri_segmentation_val.png)
 
 ## MONAI Bundle Commands
@@ -75,11 +77,13 @@ In addition to the Pythonic APIs, a few command line interfaces (CLI) are provid
 For more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).
 
 #### Execute training:
+
 ```
 python -m monai.bundle run --config_file configs/train.json
 ```
 
 #### Override the `train` config to execute multi-GPU training:
+
 ```
 torchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run --config_file "['configs/train.json','configs/multi_gpu_train.json']"
 ```
@@ -87,11 +91,13 @@ torchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run --config
 Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
 
 #### Override the `train` config to execute evaluation with the trained model:
+
 ```
 python -m monai.bundle run --config_file "['configs/train.json','configs/evaluate.json']"
 ```
 
 #### Execute inference:
+
 ```
 python -m monai.bundle run --config_file configs/inference.json
 ```

configs/metadata.json

@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.4.0",
+    "version": "0.4.1",
     "changelog": {
+        "0.4.1": "add non-deterministic note",
         "0.4.0": "adapt to BundleWorkflow interface",
         "0.3.9": "black autofix format and add name tag",
         "0.3.8": "modify dataset key name",
@@ -18,7 +19,7 @@
         "0.1.1": "update for MetaTensor",
         "0.1.0": "complete the model package"
     },
-    "monai_version": "1.2.0rc3",
+    "monai_version": "1.2.0rc4",
     "pytorch_version": "1.13.1",
     "numpy_version": "1.22.2",
     "optional_packages_version": {

docs/README.md

@@ -55,11 +55,13 @@ Dice score was used for evaluating the performance of the model. This model achi
 - Enhancing tumor (ET): 0.7905
 - Average: 0.8518
 
+Please note that this bundle is non-deterministic because of the trilinear interpolation used in the network. Therefore, reproducing the training process may not get exactly the same performance.
+Please refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.
+
 #### Training Loss and Dice
 ![A graph showing the training loss and the mean dice over 300 epochs](https://developer.download.nvidia.com/assets/Clara/Images/monai_brats_mri_segmentation_train.png)
 
 #### Validation Dice
-
 ![A graph showing the validation mean dice over 300 epochs](https://developer.download.nvidia.com/assets/Clara/Images/monai_brats_mri_segmentation_val.png)
 
 ## MONAI Bundle Commands
@@ -68,11 +70,13 @@ In addition to the Pythonic APIs, a few command line interfaces (CLI) are provid
 For more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).
 
 #### Execute training:
+
 ```
 python -m monai.bundle run --config_file configs/train.json
 ```
 
 #### Override the `train` config to execute multi-GPU training:
+
 ```
 torchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run --config_file "['configs/train.json','configs/multi_gpu_train.json']"
 ```
@@ -80,11 +84,13 @@ torchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run --config
 Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
 
 #### Override the `train` config to execute evaluation with the trained model:
+
 ```
 python -m monai.bundle run --config_file "['configs/train.json','configs/evaluate.json']"
 ```
 
 #### Execute inference:
+
 ```
 python -m monai.bundle run --config_file configs/inference.json
 ```