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[App] Fix multi-node pytorch example CI #15753

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Merged
merged 4 commits into from
Nov 21, 2022
Merged

[App] Fix multi-node pytorch example CI #15753

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cbe5092
[App] Fix multi-node pytorch example CI
ethanwharris Nov 21, 2022
7a8a94d
Update train_pytorch.py
ethanwharris Nov 21, 2022
ddd83c9
Fix docs
ethanwharris Nov 21, 2022
a14346b
Trigger CI
carmocca Nov 21, 2022
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2 changes: 1 addition & 1 deletion .github/checkgroup.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -246,7 +246,7 @@ subprojects:
- ".github/workflows/ci-app-examples.yml"
- "src/lightning_app/**"
- "tests/tests_app_examples/**"
- "examples/app_*"
- "examples/app_*/**"
- "requirements/app/**"
- "setup.py"
- ".actions/**"
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2 changes: 1 addition & 1 deletion .github/workflows/ci-app-examples.yml
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Expand Up @@ -11,7 +11,7 @@ on:
- ".github/workflows/ci-app-examples.yml"
- "src/lightning_app/**"
- "tests/tests_app_examples/**"
- "examples/app_*"
- "examples/app_*/**"
- "requirements/app/**"
- "setup.py"
- ".actions/**"
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2 changes: 1 addition & 1 deletion docs/source-app/levels/basic/hello_components/pt_multinode.py
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Expand Up @@ -22,7 +22,7 @@ def distributed_train(local_rank: int, main_address: str, main_port: int, num_no
# 2. PREPARE DISTRIBUTED MODEL
model = torch.nn.Linear(32, 2)
device = torch.device(f"cuda:{local_rank}") if torch.cuda.is_available() else torch.device("cpu")
model = DistributedDataParallel(model, device_ids=[local_rank]).to(device)
model = DistributedDataParallel(model, device_ids=[local_rank] if torch.cuda.is_available() else None).to(device)

# 3. SETUP LOSS AND OPTIMIZER
criterion = torch.nn.MSELoss()
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4 changes: 2 additions & 2 deletions examples/app_multi_node/train_pytorch.py
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Expand Up @@ -23,7 +23,7 @@ def distributed_train(local_rank: int, main_address: str, main_port: int, num_no
# 2. PREPARE DISTRIBUTED MODEL
model = torch.nn.Linear(32, 2)
device = torch.device(f"cuda:{local_rank}") if torch.cuda.is_available() else torch.device("cpu")
model = DistributedDataParallel(model, device_ids=[local_rank]).to(device)
model = DistributedDataParallel(model, device_ids=[local_rank] if torch.cuda.is_available() else None).to(device)

# 3. SETUP LOSS AND OPTIMIZER
criterion = torch.nn.MSELoss()
Expand Down Expand Up @@ -55,7 +55,7 @@ def run(self, main_address: str, main_port: int, num_nodes: int, node_rank: int)
)


# 32 GPUs: (8 nodes x 4 v 100)
# 8 GPUs: (2 nodes x 4 v 100)
compute = L.CloudCompute("gpu-fast-multi") # 4xV100
component = MultiNode(PyTorchDistributed, num_nodes=2, cloud_compute=compute)
app = L.LightningApp(component)