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-2022.8.22.tar.gz (38.8 kB view details)

Uploaded Source

Built Distribution

torchsnapshot_nightly-2022.8.22-py3-none-any.whl (49.4 kB view details)

Uploaded Python 3

File details

Details for the file torchsnapshot-nightly-2022.8.22.tar.gz.

File metadata

File hashes

Hashes for torchsnapshot-nightly-2022.8.22.tar.gz
Algorithm Hash digest
SHA256 4e8cb3384dbd0cefe70b09d65642f49b65cf439fe4475c45b4797ab3a7826633
MD5 ceaef3e9efe40eb35877da5a5fd80d3b
BLAKE2b-256 f245b0fd08d6fd1a94e19e0df7d7581ae0fdf49358cbea76ba3eeb081a712b98

See more details on using hashes here.

Provenance

File details

Details for the file torchsnapshot_nightly-2022.8.22-py3-none-any.whl.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2022.8.22-py3-none-any.whl
Algorithm Hash digest
SHA256 8c47346fd4036268f0b2c64d3928458b613760ec7b5754010c9e2e1621b02a31
MD5 e16a1c18d2d0fc8d06076128f064e26e
BLAKE2b-256 827b2adad6f06978c0bc16676d982a8d6c6cc80114ed082e4a1f717fce87b02c

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