s3torchconnectorclient 1.1.4

Internal S3 client implementation for s3torchconnector

Amazon S3 Connector for PyTorch

The Amazon S3 Connector for PyTorch delivers high throughput for PyTorch training jobs that access or store data in Amazon S3. Using the S3 Connector for PyTorch automatically optimizes performance when downloading training data from and writing checkpoints to Amazon S3, eliminating the need to write your own code to list S3 buckets and manage concurrent requests.

Amazon S3 Connector for PyTorch provides implementations of PyTorch's dataset primitives that you can use to load training data from Amazon S3. It supports both map-style datasets for random data access patterns and iterable-style datasets for streaming sequential data access patterns. The S3 Connector for PyTorch also includes a checkpointing interface to save and load checkpoints directly to Amazon S3, without first saving to local storage.

Getting Started

Prerequisites

• Python 3.8 or greater is installed (Note: Using 3.12+ is not recommended as PyTorch does not support).
• PyTorch >= 2.0 (TODO: Check with PyTorch 1.x)

Installation

pip install s3torchconnector

Amazon S3 Connector for PyTorch supports only Linux via Pip for now. For other platforms, see DEVELOPMENT for build instructions.

Configuration

To use s3torchconnector, AWS credentials must be provided through one of the following methods:

• If you are using this library on an EC2 instance, specify an IAM role and then give the EC2 instance access to that role.
• Install and configure awscli and run aws configure.
• Set credentials in the AWS credentials profile file on the local system, located at: ~/.aws/credentials on Unix or macOS.
• Set the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables.

Examples

API docs are showing API of the public components. End to end example of how to use s3torchconnector can be found under the examples directory.

Sample Examples

The simplest way to use the S3 Connector for PyTorch is to construct a dataset, either a map-style or iterable-style dataset, by specifying an S3 URI (a bucket and optional prefix) and the region the bucket is located in:

from s3torchconnector import S3MapDataset, S3IterableDataset

# You need to update <BUCKET> and <PREFIX>
DATASET_URI="s3://<BUCKET>/<PREFIX>"
REGION = "us-east-1"

map_dataset = S3MapDataset.from_prefix(DATASET_URI, region=REGION)

iterable_dataset = S3IterableDataset.from_prefix(DATASET_URI, region=REGION)

# Randomly access to an item in map_dataset.
object = map_dataset[0]

# Learn about bucket, key, and content of the object
bucket = object.bucket
key = object.key
content = object.read()
len(content)

# Datasets are also iterators. 
for object in iterable_dataset:
  print(object.key)

In addition to data loading primitives, the S3 Connector for PyTorch also provides an interface for saving and loading model checkpoints directly to and from an S3 bucket.

from s3torchconnector import S3Checkpoint

import torchvision
import torch

CHECKPOINT_URI="s3://<BUCKET>/<KEY>/"
REGION = "us-east-1"
checkpoint = S3Checkpoint(region=REGION)

model = torchvision.models.resnet18()

# Save checkpoint to S3
with checkpoint.writer(CHECKPOINT_URI + "epoch0.ckpt") as writer:
    torch.save(model.state_dict(), writer)

# Load checkpoint from S3
with checkpoint.reader(CHECKPOINT_URI + "epoch0.ckpt") as reader:
    state_dict = torch.load(reader)

model.load_state_dict(state_dict)

Using datasets or checkpoints with Amazon S3 Express One Zone directory buckets requires only to update the URI, following base-name--azid--x-s3 bucket name format. For example, assuming the following directory bucket name my-test-bucket--usw2-az1--x-s3 with the Availability Zone ID usw2-az1, then the URI used will look like: s3://my-test-bucket--usw2-az1--x-s3/<PREFIX> (please note that the prefix for Amazon S3 Express One Zone should end with '/'), paired with region us-west-2.

Contributing

We welcome contributions to Amazon S3 Connector for PyTorch. Please see CONTRIBUTING For more information on how to report bugs or submit pull requests.

Development

See DEVELOPMENT for information about code style, development process, and guidelines.

Compatibility with other storage services

S3 Connector for PyTorch delivers high throughput for PyTorch training jobs that access or store data in Amazon S3. While it may be functional against other storage services that use S3-like APIs, they may inadvertently break when we make changes to better support Amazon S3. We welcome contributions of minor compatibility fixes or performance improvements for these services if the changes can be tested against Amazon S3.

Security issue notifications

If you discover a potential security issue in this project we ask that you notify AWS Security via our vulnerability reporting page.

Code of conduct

This project has adopted the Amazon Open Source Code of Conduct. See CODE_OF_CONDUCT.md for more details.

License

Amazon S3 Connector for PyTorch has a BSD 3-Clause License, as found in the LICENSE file.