def train_mnist(config, checkpoint_dir=False):
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
train_loader, test_loader = get_data_loaders()
model = ConvNet().to(device)
optimizer = optim.SGD(model.parameters(), lr=0.1)
if checkpoint_dir:
with open(os.path.join(checkpoint_dir, "checkpoint")) as f:
model_state, optimizer_state = torch.load(f)
model.load_state_dict(model_state)
optimizer.load_state_dict(optimizer_state)
model = DistributedDataParallel(model)
for epoch in range(40):
train(model, optimizer, train_loader, device)
acc = test(model, test_loader, device)
if epoch % 3 == 0:
with distributed_checkpoint_dir(step=epoch) as checkpoint_dir:
path = os.path.join(checkpoint_dir, "checkpoint")
torch.save((model.state_dict(), optimizer.state_dict()), path)
tune.report(mean_accuracy=acc)
def test_checkpoint(ray_start_2_cpus, rank): # noqa: F811
with patch("torch.distributed.get_rank") as rank_method:
rank_method.return_value = rank
with distributed_checkpoint_dir(step="test") as path:
if rank == 0:
assert path
if rank != 0:
assert not os.path.exists(path)