Skip to main content

A performant, memory-efficient checkpointing library for PyTorch applications, designed with large, complex distributed workloads in mind.

Project description

TorchSnapshot (Beta Release)

build status pypi version conda version pypi nightly version codecov bsd license

A performant, memory-efficient checkpointing library for PyTorch applications, designed with large, complex distributed workloads in mind.

Install

Requires Python >= 3.8 and PyTorch >= 2.0.0

From pip:

# Stable
pip install torchsnapshot
# Or, using conda
conda install -c conda-forge torchsnapshot

# Nightly
pip install --pre torchsnapshot-nightly

From source:

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

Why TorchSnapshot

Performance

  • TorchSnapshot provides a fast checkpointing implementation employing various optimizations, including zero-copy serialization for most tensor types, overlapped device-to-host copy and storage I/O, parallelized storage I/O.
  • TorchSnapshot greatly speeds up checkpointing for DistributedDataParallel workloads by distributing the write load across all ranks (benchmark).
  • When host memory is abundant, TorchSnapshot allows training to resume before all storage I/O completes, reducing the time blocked by checkpoint saving.

Memory Usage

  • TorchSnapshot's memory usage adapts to the host's available resources, greatly reducing the chance of out-of-memory issues when saving and loading checkpoints.
  • TorchSnapshot supports efficient random access to individual objects within a snapshot, even when the snapshot is stored in a cloud object storage.

Usability

  • Simple APIs that are consistent between distributed and non-distributed workloads.
  • Out of the box integration with commonly used cloud object storage systems.
  • Automatic resharding (elasticity) on world size change for supported workloads (more details).

Security

  • Secure tensor serialization without pickle dependency [WIP].

Getting Started

from torchsnapshot import Snapshot

# Taking a snapshot
app_state = {"model": model, "optimizer": optimizer}
snapshot = Snapshot.take(path="/path/to/snapshot", app_state=app_state)

# Restoring from a snapshot
snapshot.restore(app_state=app_state)

See the documentation for more details.

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-2024.7.19.tar.gz (93.0 kB view details)

Uploaded Source

Built Distribution

torchsnapshot_nightly-2024.7.19-py3-none-any.whl (84.7 kB view details)

Uploaded Python 3

File details

Details for the file torchsnapshot_nightly-2024.7.19.tar.gz.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2024.7.19.tar.gz
Algorithm Hash digest
SHA256 18aa968e03cf2d4119ee15fa43989a13838917eccd7c948a71527ce6709ff262
MD5 fb745bf4c93e5d0f5f0a038b85a5cc98
BLAKE2b-256 31b54c14a29af24e65b6270dcb2d08e6e0f881b341931e61ba654477e4f932a1

See more details on using hashes here.

Provenance

File details

Details for the file torchsnapshot_nightly-2024.7.19-py3-none-any.whl.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2024.7.19-py3-none-any.whl
Algorithm Hash digest
SHA256 e9c6e9f1eb2fd6234852e9f8cd2f4db6980ae0352cfa552dcf9dd3fd7dd9b665
MD5 e2e47c560d282361d2ff908ae452857f
BLAKE2b-256 5ea3746435482df119bad3b8ddba6a082952871a82fa8c7ce2f71449b41b6ab0

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