Skip to main content

A lightweight library for adding fault tolerance to large-scale PyTorch distributed training workloads.

Project description

torchsnapshot

build status pypi version pypi nightly version codecov bsd license

This library is currently in Alpha and currently does not have a stable release. The API may change and may not be backward compatible. If you have suggestions for improvements, please open a GitHub issue. We'd love to hear your feedback.

A light-weight library for adding fault tolerance to large-scale PyTorch distributed training workloads.

Install

Requires Python >= 3.7 and PyTorch >= 1.11

From pip:

pip install --pre torchsnapshot-nightly

From source:

git clone https://github.com/facebookresearch/torchsnapshot
cd torchsnapshot
pip install -r requirements.txt
python setup.py install

Concepts

  • Stateful object - an object that whose state can be obtained via .state_dict() and restored via .load_state_dict(). Most PyTorch components (e.g. Module, Optimizer, LRScheduler) already implement this protocol.
  • App state - the application state described using multiple stateful objects.
  • Snapshot - the persisted app state.

Basic Usage

Describing the application state with multiple stateful objects:

app_state = {"model": model, "optimizer": optimizer}

Taking a snapshot of the application state:

from torchsnapshot import Snapshot

# File System
snapshot = Snapshot.take(path="/foo/bar/baz", app_state=app_state)

# S3
snapshot = Snapshot.take(path="s3://foo/bar", app_state=app_state)

# Google Cloud Storage
snapshot = Snapshot.take(path="gcs://foo/bar", app_state=app_state)

Referencing an existing snapshot:

snapshot = Snapshot(path="foo/bar/baz")

Restoring the application state from a snapshot:

snapshot.restore(app_state=app_state)

See the example directory for more examples.

License

torchsnapshot is BSD licensed, as found in the LICENSE file.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torchsnapshot-nightly-2022.9.29.tar.gz (40.5 kB view details)

Uploaded Source

Built Distribution

torchsnapshot_nightly-2022.9.29-py3-none-any.whl (50.1 kB view details)

Uploaded Python 3

File details

Details for the file torchsnapshot-nightly-2022.9.29.tar.gz.

File metadata

File hashes

Hashes for torchsnapshot-nightly-2022.9.29.tar.gz
Algorithm Hash digest
SHA256 a11c6f7e80a8e5a90b0e24907afbdc80472e056e736aa27fdf7dfe250bb121d4
MD5 a9cc195750d0b69b3c0a5f9986629606
BLAKE2b-256 06e83c51353d9abf3023e8f690ef86c59e9515183ce739848e96c1d9d0287b38

See more details on using hashes here.

Provenance

File details

Details for the file torchsnapshot_nightly-2022.9.29-py3-none-any.whl.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2022.9.29-py3-none-any.whl
Algorithm Hash digest
SHA256 4ed89f02790d005a99a36058390be681397c9f96e85b52c6a59adf38da939b50
MD5 7ee59e5d57a577bb74fe33c1549e15ba
BLAKE2b-256 6ffc88019e693cc4c3002133978eca310983974873a336878a7dc7565f596dd8

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page