Adding notebook for MNIST training using PyTorch and StepFunctions (aws#4599)

* Update training_pipeline_pytorch_mnist.ipynb * Update mnist.py # Set a fixed random seed for reproducibility SEED = 42 torch.manual_seed(SEED) np.random.seed(SEED) random.seed(SEED) * Update mnist.py The main change is replacing with torch.no_grad(): with with torch.inference_mode():. * Added CI badge in notebook * Added CI badge in notebook * Reformatted the code * Reformatted the code

Author: Avs163

step-functions-data-science-sdk/training_pipeline_pytorch_mnist/code/mnist.py

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+import argparse
+import json
+import logging
+import os
+import sys
+import random
+import sagemaker_containers
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torch.utils.data
+import torch.utils.data.distributed
+import numpy as np
+from torchvision import datasets, transforms
+
+# Set a fixed random seed for reproducibility
+
+SEED = 42
+torch.manual_seed(SEED)
+np.random.seed(SEED)
+random.seed(SEED)
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+logger.addHandler(logging.StreamHandler(sys.stdout))
+
+
+# Based on https://github.com/pytorch/examples/blob/master/mnist/main.py
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+ self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+ self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+ self.conv2_drop = nn.Dropout2d()
+ self.fc1 = nn.Linear(320, 50)
+ self.fc2 = nn.Linear(50, 10)
+
+ def forward(self, x):
+ x = F.relu(F.max_pool2d(self.conv1(x), 2))
+ x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
+ x = x.view(-1, 320)
+ x = F.relu(self.fc1(x))
+ x = F.dropout(x, training=self.training)
+ x = self.fc2(x)
+ return F.log_softmax(x, dim=1)
+
+
+def _get_train_data_loader(batch_size, training_dir, is_distributed, **kwargs):
+ logger.info("Printing the Training Dir path")
+ logger.info(training_dir)
+ logger.info(os.listdir(training_dir + '/MNIST'))
+ logger.info("Get train data loader")
+ dataset = datasets.MNIST(
+ training_dir,
+ train=True,
+ transform=transforms.Compose(
+ [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+ ),
+ )
+ train_sampler = (
+ torch.utils.data.distributed.DistributedSampler(dataset) if is_distributed else None
+ )
+ return torch.utils.data.DataLoader(
+ dataset,
+ batch_size=batch_size,
+ shuffle=train_sampler is None,
+ sampler=train_sampler,
+ **kwargs
+ )
+
+
+def _get_test_data_loader(test_batch_size, training_dir, **kwargs):
+ logger.info("Get test data loader")
+ return torch.utils.data.DataLoader(
+ datasets.MNIST(
+ training_dir,
+ train=False,
+ transform=transforms.Compose(
+ [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+ ),
+ ),
+ batch_size=test_batch_size,
+ shuffle=True,
+ **kwargs
+ )
+
+
+def _average_gradients(model):
+ # Gradient averaging.
+ size = float(dist.get_world_size())
+ for param in model.parameters():
+ dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM)
+ param.grad.data /= size
+
+
+def train(args):
+ is_distributed = len(args.hosts) > 1 and args.backend is not None
+ logger.debug("Distributed training - {}".format(is_distributed))
+ use_cuda = args.num_gpus > 0
+ logger.debug("Number of gpus available - {}".format(args.num_gpus))
+ kwargs = {"num_workers": 1, "pin_memory": True} if use_cuda else {}
+ device = torch.device("cuda" if use_cuda else "cpu")
+
+ if is_distributed:
+ # Initialize the distributed environment.
+ world_size = len(args.hosts)
+ os.environ["WORLD_SIZE"] = str(world_size)
+ host_rank = args.hosts.index(args.current_host)
+ os.environ["RANK"] = str(host_rank)
+ dist.init_process_group(backend=args.backend, rank=host_rank, world_size=world_size)
+ logger.info(
+ "Initialized the distributed environment: '{}' backend on {} nodes. ".format(
+ args.backend, dist.get_world_size()
+ )
+ + "Current host rank is {}. Number of gpus: {}".format(dist.get_rank(), args.num_gpus)
+ )
+
+ # set the seed for generating random numbers
+ torch.manual_seed(args.seed)
+ if use_cuda:
+ torch.cuda.manual_seed(args.seed)
+
+ train_loader = _get_train_data_loader(args.batch_size, args.data_dir, is_distributed, **kwargs)
+ test_loader = _get_test_data_loader(args.test_batch_size, args.data_dir, **kwargs)
+
+ logger.debug(
+ "Processes {}/{} ({:.0f}%) of train data".format(
+ len(train_loader.sampler),
+ len(train_loader.dataset),
+ 100.0 * len(train_loader.sampler) / len(train_loader.dataset),
+ )
+ )
+
+ logger.debug(
+ "Processes {}/{} ({:.0f}%) of test data".format(
+ len(test_loader.sampler),
+ len(test_loader.dataset),
+ 100.0 * len(test_loader.sampler) / len(test_loader.dataset),
+ )
+ )
+
+ model = Net().to(device)
+ if is_distributed and use_cuda:
+ # multi-machine multi-gpu case
+ model = torch.nn.parallel.DistributedDataParallel(model)
+ else:
+ # single-machine multi-gpu case or single-machine or multi-machine cpu case
+ model = torch.nn.DataParallel(model)
+
+ optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
+
+ for epoch in range(1, args.epochs + 1):
+ model.train()
+ for batch_idx, (data, target) in enumerate(train_loader, 1):
+ data, target = data.to(device), target.to(device)
+ optimizer.zero_grad()
+ output = model(data)
+ loss = F.nll_loss(output, target)
+ loss.backward()
+ if is_distributed and not use_cuda:
+ # average gradients manually for multi-machine cpu case only
+ _average_gradients(model)
+ optimizer.step()
+ if batch_idx % args.log_interval == 0:
+ logger.info(
+ "Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}".format(
+ epoch,
+ batch_idx * len(data),
+ len(train_loader.sampler),
+ 100.0 * batch_idx / len(train_loader),
+ loss.item(),
+ )
+ )
+ test(model, test_loader, device)
+ save_model(model, args.model_dir)
+
+
+def test(model, test_loader, device):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.inference_mode():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device)
+ output = model(data)
+ test_loss += F.nll_loss(output, target, size_average=False).item() # sum up batch loss
+ pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+ logger.info(
+ "Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
+ test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset)
+ )
+ )
+
+
+def model_fn(model_dir):
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ model = torch.nn.DataParallel(Net())
+ with open(os.path.join(model_dir, "model.pth"), "rb") as f:
+ model.load_state_dict(torch.load(f))
+ return model.to(device)
+
+
+def save_model(model, model_dir):
+ logger.info("Saving the model.")
+ path = os.path.join(model_dir, "model.pth")
+ scripted_module = torch.jit.trace(model, torch.randn((1, 1, 28, 28)))
+ torch.jit.save(scripted_module, path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+
+ # Data and model checkpoints directories
+ parser.add_argument(
+ "--batch-size",
+ type=int,
+ default=64,
+ metavar="N",
+ help="input batch size for training (default: 64)",
+ )
+ parser.add_argument(
+ "--test-batch-size",
+ type=int,
+ default=1000,
+ metavar="N",
+ help="input batch size for testing (default: 1000)",
+ )
+ parser.add_argument(
+ "--epochs",
+ type=int,
+ default=10,
+ metavar="N",
+ help="number of epochs to train (default: 10)",
+ )
+ parser.add_argument(
+ "--lr", type=float, default=0.01, metavar="LR", help="learning rate (default: 0.01)"
+ )
+ parser.add_argument(
+ "--momentum", type=float, default=0.5, metavar="M", help="SGD momentum (default: 0.5)"
+ )
+ parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
+ parser.add_argument(
+ "--log-interval",
+ type=int,
+ default=100,
+ metavar="N",
+ help="how many batches to wait before logging training status",
+ )
+ parser.add_argument(
+ "--backend",
+ type=str,
+ default=None,
+ help="backend for distributed training (tcp, gloo on cpu and gloo, nccl on gpu)",
+ )
+
+ # Container environment
+ parser.add_argument("--hosts", type=list, default=json.loads(os.environ["SM_HOSTS"]))
+ parser.add_argument("--current-host", type=str, default=os.environ["SM_CURRENT_HOST"])
+ parser.add_argument("--model-dir", type=str, default=os.environ["SM_MODEL_DIR"])
+ parser.add_argument("--data-dir", type=str, default=os.environ["SM_CHANNEL_TRAINING"])
+ parser.add_argument("--num-gpus", type=int, default=os.environ["SM_NUM_GPUS"])
+ 
+ train(parser.parse_args())

step-functions-data-science-sdk/training_pipeline_pytorch_mnist/code/requirements.txt

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+sagemaker_containers