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-2024.6.21.tar.gz (93.0 kB view details)

Uploaded Source

Built Distribution

torchsnapshot_nightly-2024.6.21-py3-none-any.whl (84.6 kB view details)

Uploaded Python 3

File details

Details for the file torchsnapshot_nightly-2024.6.21.tar.gz.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2024.6.21.tar.gz
Algorithm Hash digest
SHA256 7f3eb258b5bbcc5d4530ac8baa720559513644fca34118b2a5ac7f0e77f1afa9
MD5 1a79f5d626e23b5ff5bbc5011fec9a3b
BLAKE2b-256 67642802a784a5c126b93aac2b52f720651dad2b394080663302da79eac89b06

See more details on using hashes here.

Provenance

File details

Details for the file torchsnapshot_nightly-2024.6.21-py3-none-any.whl.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2024.6.21-py3-none-any.whl
Algorithm Hash digest
SHA256 d149391913b47d218ab9a48636acff7e6b0e615fef23114417debf1605c3d137
MD5 687b5fde13f6a949a529f38308282e9c
BLAKE2b-256 3c534d8848d03fe8c8602fe82c9716fd48ef1e76b4dc21994b7f7c8931cc6842

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