PyTorch

DVCLive allows you to add experiment tracking capabilities to your PyTorch projects.

Usage

You need to add Live.log_metric() calls to each place where you would like to log metrics and one single Live.next_step() call to indicate that the epoch has ended.

let's consider the following example, extracted from the official PyTorch ImageNet example:

from dvclive import Live

live = Live()

for epoch in range(args.start_epoch, args.epochs):
    lr = adjust_learning_rate(optimizer, epoch, args)
    live.log_metric("learning_rate", lr)

    train_acc1 = train(
        train_loader, model, criterion, optimizer, epoch, args)
    live.log_metric("train/accuracy", train_acc1)

    val_acc1 = validate(val_loader, model, criterion, args)
    live.log_metric("validation/accuracy", val_acc1)

    is_best = val_acc1 > best_acc1
    best_acc1 = max(val_acc1, best_acc1)

    save_checkpoint({
        'epoch': epoch + 1,
        'arch': args.arch,
        'state_dict': model.state_dict(),
        'best_acc1': best_acc1,
        'optimizer' : optimizer.state_dict(),
    }, is_best)

    live.next_step()

PyTorch

Usage

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