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-2022.7.7.tar.gz (28.7 kB view details)

Uploaded Source

Built Distribution

torchsnapshot_nightly-2022.7.7-py3-none-any.whl (38.0 kB view details)

Uploaded Python 3

File details

Details for the file torchsnapshot-nightly-2022.7.7.tar.gz.

File metadata

File hashes

Hashes for torchsnapshot-nightly-2022.7.7.tar.gz
Algorithm Hash digest
SHA256 b1365e13e97a917e90a33cc8b86998f632a0d0bee6bdbd6470197bc494c3880c
MD5 b675a56508d5c85fdeaf982acc2ca44e
BLAKE2b-256 c6e23a124e342a09092e5b7fd4035ab1f347caee7e5f5ed9fc7097c1748c5d57

See more details on using hashes here.

Provenance

File details

Details for the file torchsnapshot_nightly-2022.7.7-py3-none-any.whl.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2022.7.7-py3-none-any.whl
Algorithm Hash digest
SHA256 693448bb4dfaee012923aa6e9cafb5e9b5989c264c2d8a532f93b17dfb6a6df9
MD5 3c885a58d0db4187c20aa641993c7197
BLAKE2b-256 a9fefdde0f083b62a162550c71ec189b9c1b6fd95195aa272c9bff35a9d2ba89

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