monai
medical
katielink commited on
Commit
01573e4
1 Parent(s): a23faaa

enhance readme on commands example

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Files changed (2) hide show
  1. configs/metadata.json +2 -1
  2. docs/readme.md +4 -0
configs/metadata.json CHANGED
@@ -1,7 +1,8 @@
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  {
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  "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
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- "version": "0.2.0",
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  "changelog": {
 
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  "0.2.0": "update license files",
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  "0.1.0": "complete the first version model package",
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  "0.0.1": "initialize the model package structure"
 
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  {
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  "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
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+ "version": "0.2.1",
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  "changelog": {
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+ "0.2.1": "enhance readme on commands example",
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  "0.2.0": "update license files",
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  "0.1.0": "complete the first version model package",
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  "0.0.1": "initialize the model package structure"
docs/readme.md CHANGED
@@ -58,6 +58,7 @@ python -m monai.bundle run training \
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  ```
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  Override the `train` config to execute multi-GPU training:
 
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  ```
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  torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training \
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  --meta_file configs/metadata.json \
@@ -65,6 +66,9 @@ torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training
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  --logging_file configs/logging.conf
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  ```
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  Override the `train` config to execute evaluation with the trained model:
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  ```
 
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  ```
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  Override the `train` config to execute multi-GPU training:
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+
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  ```
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  torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training \
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  --meta_file configs/metadata.json \
 
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  --logging_file configs/logging.conf
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  ```
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+ Please note that the distributed training related options depend on the actual running environment, thus you may need to remove `--standalone`, modify `--nnodes` or do some other necessary changes according to the machine you used.
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+ Please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) for more details.
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+
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  Override the `train` config to execute evaluation with the trained model:
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  ```