# Original Code here:
# https://github.com/pytorch/examples/blob/master/mnist/main.py
import argparse
import logging
import os
import torch
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel
import ray
from ray import tune
from ray.tune.examples.mnist_pytorch import (train, test, get_data_loaders,
ConvNet)
from ray.tune.integration.torch import (DistributedTrainableCreator,
distributed_checkpoint_dir)
logger = logging.getLogger(__name__)
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)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--num-workers",
"-n",
type=int,
default=2,
help="Sets number of workers for training.")
parser.add_argument(
"--use-gpu",
action="store_true",
default=False,
help="enables CUDA training")
parser.add_argument(
"--cluster",
action="store_true",
default=False,
help="enables multi-node tuning")
args = parser.parse_args()
if args.cluster:
options = dict(address="auto")
else:
options = dict(num_cpus=2)
ray.init(**options)
trainable_cls = DistributedTrainableCreator(
train_mnist, num_workers=args.num_workers, use_gpu=args.use_gpu)
tune.run(trainable_cls, num_samples=4, stop={"training_iteration": 10})