How to Train an PyTorch Image Classification Model on AWS SageMaker Using Deep Lake Datasets
provides scalable infrastructure for developing, training, and deploying deep learning models. In this tutorial, we demonstrate how to run SageMaker training jobs for training a PyTorch image classification model using a Deep Lake dataset. This tutorial will focus on the SageMaker integration, and less so on the details of the training (see other for details)
Dataset
In this tutorial we will use the , which classifies the make+model+year of various vehicles. Though the dataset contains bounding boxes, we ignore those and only use the data for classification purposes.
Running the Sagemaker Job
We run the SageMaker job using the docker container below that can be found among .
The SageMaker job is initiated using the script below. By also running this script in a SageMaker notebook, the permissions and role access are automatically taken care of within the AWS environment.
import sagemaker
sess = sagemaker.Session()
role = sagemaker.get_execution_role()
The training script (entry_point) and the directory (source_dir) containing the training script and requirements.txt file is passed to the . The argparse parameters for the training script are passed via the hyperparameters dictinary in the Estimator. Note that we also pass the Deep Lake paths to the training and validation datasets via this input.
estimator = sagemaker.estimator.Estimator(
source_dir = "./train_code", # Directory of the training script
entry_point = "train_cars.py", # File for the training script
image_uri = image_name,
role = role,
instance_count = 1,
instance_type = instance_type,
output_path = output_path,
sagemaker_session = sess,
max_run = 2*60*60,
hyperparameters = {"train-dataset": "hub://activeloop/stanford-cars-train",
"val-dataset": "hub://activeloop/stanford-cars-test",
"batch-size": 64, "num-epochs": 40,
})
The training job is triggered using the command below. Typically, the .fit() function accepts as inputs the S3 bucket containing the training data, which is then downloaded onto the local storage of the SageMaker job. Since we've passed the Deep Lake dataset paths via the hyperparameters, and since Deep Lake does not require data to be downloaded prior to training, we skip these inputs.
estimator.fit()
Training Script
The contents of the train_code folder, as well as the train_cars.py file, are shown below. The training script follow the same workflow as other PyTorch training workflows using Deep Lake. As mentioned above, the inputs to the argparse function are those from the hyperparameters inputs in the estimator.
import deeplake
import argparse
import logging
import os
import sys
import time
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision import transforms, models
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler(sys.stdout))
#----------- Define transformations and their parameters -----------#
WIDTH = 320
HEIGHT = 320
tform_train = transforms.Compose([
# transforms.ToPILImage(), # Not needed because decode_method is set to PIL in the dataloader
transforms.RandomResizedCrop((WIDTH, HEIGHT), scale=(0.75, 1.0), ratio=(0.75, 1.25)),
transforms.RandomRotation(25),
transforms.ColorJitter(brightness=(0.8,1.2), contrast=(0.8,1.2), saturation=(0.8,1.2), hue=(-0.1,0.1)),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.repeat(int(3 / x.shape[0]), 1, 1)), # Adjust tensor if the image is grayscale
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
tform_val = transforms.Compose([
# transforms.ToPILImage(), # Not needed because decode_method is set to PIL in the dataloader
transforms.Resize((WIDTH, HEIGHT)),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.repeat(int(3 / x.shape[0]), 1, 1)), # Adjust tensor if the image is grayscale
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
#----------- Define helper functions -----------#
# Helper function for loading the model
def get_model_classification(num_classes):
# Load a pre-trained classification model
model = models.resnet34(pretrained=True)
# Adjust the fully connected layer based on the number of classes in the dataset
model.fc = torch.nn.Linear(model.fc.in_features, num_classes)
return model
# Helper function for training for 1 epoch
def train_one_epoch(model, optimizer, criterion, data_loader, device, log_interval):
# Set the model to train mode
model.train()
# Zero the performance stats for each epoch
running_loss = 0.0
start_time = time.time()
total = 0
correct = 0
for i, data in enumerate(data_loader):
# Parse the inputs
inputs = data['images']
labels = data['car_models'][:, 0] # Get rid of the extra axis
inputs = inputs.to(device)
labels = labels.to(device)
# Zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs.float())
loss = criterion(outputs, labels.long())
loss.backward()
optimizer.step()
# Update the accuracy for the epoch
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = 100 * correct / total
# Print statistics
running_loss += loss.item()
batch_time = time.time()
if i % log_interval == 0: # print every 100 mini-batches
speed_cumulative = (i+1)/(batch_time-start_time)
logger.debug('[%5d] running loss: %.3f, epoch accuracy: %.3f, cumulative speed: %.2f ' %
(i, running_loss, accuracy, speed_cumulative))
running_loss = 0.0
# Helper function for testing the model
def test_model(model, data_loader, device):
# Set the model to eval mode
model.eval()
total = 0
correct = 0
with torch.no_grad():
for i, data in enumerate(data_loader):
# Parse the inputs
inputs = data['images']
labels = data['car_models'][:, 0]
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs.float())
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = 100 * correct / total
return accuracy
# Helper function for saving the model
def save_model(model, model_dir):
logger.info("Saving the model")
path = os.path.join(model_dir, "model.pth")
torch.save(model.state_dict(), path)
def train(args):
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# Load dataset and create dataloaders.
ds_train = deeplake.load(args.train_dataset, read_only = True, token = args.token, creds = args.creds)
ds_val = deeplake.load(args.val_dataset, read_only = True, token = args.token, creds = args.creds)
train_loader = ds_train.dataloader()\
.batch(args.batch_size)\
.shuffle(args.shuffle)\
.transform(transform = {'images': tform_train, 'car_models': None})\
.pytorch(num_workers = args.num_workers, decode_method = {'images': 'pil'})
val_loader = ds_val.dataloader()\
.batch(args.batch_size)\
.transform(transform = {'images': tform_val, 'car_models': None})\
.pytorch(num_workers = args.num_workers, decode_method = {'images': 'pil'})
# Load the model
model = get_model_classification(len(ds_train.car_models.info.class_names))
model = model.to(device)
# Define the optimizer, loss, and learning rate scheduler
optimizer = optim.Adam(model.parameters(), lr=args.lr)
criterion = nn.CrossEntropyLoss()
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.2)
# Run the training
for epoch in range(args.num_epochs):
logger.debug("Training Epoch: {}".format(epoch))
train_one_epoch(model, optimizer, criterion, train_loader, device, args.log_interval)
lr_scheduler.step()
accuracy = test_model(model, val_loader, device)
logger.debug("Validation Accuracy: {}".format(accuracy))
logger.debug('Finished Training')
save_model(model, args.model_dir)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--train-dataset",
type=str,
required=True,
help="path to deeplake training dataset",
)
parser.add_argument(
"--val-dataset",
type=str,
required=True,
help="path to deeplake validation dataset",
)
parser.add_argument(
"--batch-size",
type=int,
default=64,
help="input batch size for training (default: 64)",
)
parser.add_argument(
"--num-workers",
type=int,
default=8,
help="number of workers for the dataloaders (default: 8)",
)
parser.add_argument(
"--shuffle",
type=bool,
default=True,
help="shuffling for the training dataloader (default: True)",
)
parser.add_argument(
"--num-epochs",
type=int,
default=10,
help="number of epochs to train (default: 10)",
)
parser.add_argument(
"--lr",
type=float,
default=0.001,
help="learning rate (default: 0.001)"
)
parser.add_argument(
"--log-interval",
type=int,
default=10,
metavar="N",
help="how many batches to wait before logging training status (default: 10)",
)
parser.add_argument(
"--token",
type=str,
default=None,
help="token for accessing the Deep Lake dataset (default: None)"
)
parser.add_argument(
"--creds",
type=dict,
default=None,
help="creds dictionary for accessing the Deep Lake dataset (default: None)"
)
parser.add_argument(
'--model_dir',
type=str,
default=os.environ['SM_MODEL_DIR'])
train(parser.parse_args())
Congrats! You're now able to train models using AWS SageMaker Jobs while streaming Deep Lake Datasets! 🎉
SageMaker offers a variety of method for advanced data logging. In this example, we can monitor the training performance in real-time in the training notebook where the jobs are triggered, or in the logs for each job. We observe that the validation accuracy after 40 epochs is 75%.
