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-2022.7.4.tar.gz (26.6 kB view details)

Uploaded Source

Built Distribution

torchsnapshot_nightly-2022.7.4-py3-none-any.whl (35.4 kB view details)

Uploaded Python 3

File details

Details for the file torchsnapshot-nightly-2022.7.4.tar.gz.

File metadata

File hashes

Hashes for torchsnapshot-nightly-2022.7.4.tar.gz
Algorithm Hash digest
SHA256 9a700de573fca0daa56f03efa9c60ada95091a7376d214b8d7b64963671a0c6f
MD5 bc5f31f14b2e79b874c758b5c335137a
BLAKE2b-256 1db6e2899743c5e518dbda153f79115751d2e615d73e311901af8d6e0a5fdaec

See more details on using hashes here.

Provenance

File details

Details for the file torchsnapshot_nightly-2022.7.4-py3-none-any.whl.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2022.7.4-py3-none-any.whl
Algorithm Hash digest
SHA256 93e2063a43675b1d7d95c0c90bb9074a6689547e6de1002272cd90d06022386e
MD5 62ef082271bfbafe83bc9df57b8a53ae
BLAKE2b-256 7a402de931228c54afc8c3fa6a0a0220a2893873d0a6dc440809517e0f96a57f

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