42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
import torch
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import lightning as L
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from lightning.app.components import LiteMultiNode
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from lightning.lite import LightningLite
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class LitePyTorchDistributed(L.LightningWork):
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def run(self):
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# 1. Prepare the model
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model = torch.nn.Sequential(
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torch.nn.Linear(1, 1),
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torch.nn.ReLU(),
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torch.nn.Linear(1, 1),
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)
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# 2. Create LightningLite.
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lite = LightningLite(strategy="ddp", precision=16)
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model, optimizer = lite.setup(model, torch.optim.SGD(model.parameters(), lr=0.01))
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criterion = torch.nn.MSELoss()
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# 3. Train the model for 1000 steps.
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for step in range(1000):
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model.zero_grad()
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x = torch.tensor([0.8]).to(lite.device)
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target = torch.tensor([1.0]).to(lite.device)
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output = model(x)
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loss = criterion(output, target)
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print(f"global_rank: {lite.global_rank} step: {step} loss: {loss}")
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lite.backward(loss)
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optimizer.step()
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# Run over 2 nodes of 4 x V100
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app = L.LightningApp(
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LiteMultiNode(
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LitePyTorchDistributed,
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cloud_compute=L.CloudCompute("gpu-fast-multi"), # 4 x V100
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num_nodes=2,
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)
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)
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