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-2024.1.3.tar.gz (91.2 kB view details)

Uploaded Source

Built Distribution

torchsnapshot_nightly-2024.1.3-py3-none-any.whl (83.4 kB view details)

Uploaded Python 3

File details

Details for the file torchsnapshot-nightly-2024.1.3.tar.gz.

File metadata

File hashes

Hashes for torchsnapshot-nightly-2024.1.3.tar.gz
Algorithm Hash digest
SHA256 5809fb8313359573ee07fecd62c02070fc0915929a1c80584b1d9e929400271e
MD5 5af6215486ea5baa1197a2b072ca932e
BLAKE2b-256 59ba7ee37a18850e3ffc6ffcf4ebea817e9ec04ee1df158e7351d04230e057cc

See more details on using hashes here.

Provenance

File details

Details for the file torchsnapshot_nightly-2024.1.3-py3-none-any.whl.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2024.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 7099d7530b3e97847297c431ddab51f51c2b86abe6984a81cd4dc553bcfda9cc
MD5 5cc900f19782e0434c3c8ddf087cc0c4
BLAKE2b-256 b9b431b1e51837c38edae5bb78906a4f458ed8a8b6826e80c6bce7c45e241e61

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