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-2022.10.13.tar.gz (48.3 kB view details)

Uploaded Source

Built Distribution

torchsnapshot_nightly-2022.10.13-py3-none-any.whl (59.6 kB view details)

Uploaded Python 3

File details

Details for the file torchsnapshot-nightly-2022.10.13.tar.gz.

File metadata

File hashes

Hashes for torchsnapshot-nightly-2022.10.13.tar.gz
Algorithm Hash digest
SHA256 1968cf977d9864c8912014f6865236da515d05dedbbf8aeaa811642549790659
MD5 530c6425e731f335de0e170d057b10de
BLAKE2b-256 7046353b32668df0285d3395beb6522c80492a197a7f681d8845a52b0fd53660

See more details on using hashes here.

Provenance

File details

Details for the file torchsnapshot_nightly-2022.10.13-py3-none-any.whl.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2022.10.13-py3-none-any.whl
Algorithm Hash digest
SHA256 9c6e95e5b4145a2e4af20123ea077d0e4b9ebff79b6a7bccc105954378b41332
MD5 66ec2c702f98c1a3c45bbf89995bdcbf
BLAKE2b-256 98b093510ae7055357d9a3262768095734b4e458c1b75c731b12dd554fc49748

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