enable deterministic training
Browse files- configs/metadata.json +3 -2
- configs/multi_gpu_train.json +1 -2
- configs/train.json +1 -2
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": "0.4.
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"changelog": {
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"0.4.4": "update numbers",
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"0.4.3": "adapt to BundleWorkflow interface",
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"0.4.2": "fix train params of use_checkpoint",
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@@ -21,7 +22,7 @@
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"0.1.0": "complete the model package",
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"0.0.1": "initialize the model package structure"
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},
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-
"monai_version": "1.2.
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"pytorch_version": "1.13.1",
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"numpy_version": "1.22.2",
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"optional_packages_version": {
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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": "0.4.5",
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"changelog": {
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+
"0.4.5": "enable deterministic training",
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"0.4.4": "update numbers",
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"0.4.3": "adapt to BundleWorkflow interface",
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"0.4.2": "fix train params of use_checkpoint",
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"0.1.0": "complete the model package",
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"0.0.1": "initialize the model package structure"
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},
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+
"monai_version": "1.2.0rc4",
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"pytorch_version": "1.13.1",
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"numpy_version": "1.22.2",
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"optional_packages_version": {
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configs/multi_gpu_train.json
CHANGED
@@ -28,8 +28,7 @@
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"$import torch.distributed as dist",
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"$dist.is_initialized() or dist.init_process_group(backend='nccl')",
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"$torch.cuda.set_device(@device)",
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-
"$monai.utils.set_determinism(seed=123)"
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-
"$setattr(torch.backends.cudnn, 'benchmark', True)"
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],
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"run": [
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"$@train#trainer.run()"
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"$import torch.distributed as dist",
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"$dist.is_initialized() or dist.init_process_group(backend='nccl')",
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"$torch.cuda.set_device(@device)",
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+
"$monai.utils.set_determinism(seed=123)"
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],
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"run": [
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"$@train#trainer.run()"
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configs/train.json
CHANGED
@@ -319,8 +319,7 @@
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}
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},
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"initialize": [
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-
"$monai.utils.set_determinism(seed=123)"
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-
"$setattr(torch.backends.cudnn, 'benchmark', True)"
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],
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"run": [
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"$@train#trainer.run()"
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}
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},
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"initialize": [
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+
"$monai.utils.set_determinism(seed=123)"
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],
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"run": [
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"$@train#trainer.run()"
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