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How to integrate a PyTorch script to log metrics to W&B |
W&B provides first class support for PyTorch. To automatically log gradients and store the network topology, you can call watch and pass in your PyTorch model.
import wandb
wandb.init(config=args)
# Magic
wandb.watch(model)
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % args.log_interval == 0:
wandb.log({"loss": loss})Gradients, metrics and the graph won't be logged until
wandb.logis called after a forward and backward pass.
See this Colab notebook for an end to end example of integrating wandb with PyTorch. You can also find more examples in our example projects section.
By default the hook only logs gradients. If you want to log histograms of parameter values as well, you can specify wandb.watch(model, log="all"). Valid options for the log argument are: "gradients", "parameters", "all", or None.
You can pass PyTorch tensors with image data into wandb.Image and torchvision utils will be used to log them automatically.
To log images and view them in the Media panel, you can use the following syntax:
wandb.log({"examples" : [wandb.Image(i) for i in images]})If you need to track multiple models in the same script, you can wall wandb.watch() on each model separately.