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 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 >= 3.7 and PyTorch >= 1.12

From pip:

# Stable
pip install 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-2022.11.21.tar.gz (52.3 kB view details)

Uploaded Source

Built Distribution

torchsnapshot_nightly-2022.11.21-py3-none-any.whl (68.6 kB view details)

Uploaded Python 3

File details

Details for the file torchsnapshot-nightly-2022.11.21.tar.gz.

File metadata

File hashes

Hashes for torchsnapshot-nightly-2022.11.21.tar.gz
Algorithm Hash digest
SHA256 d6a71be3b3411667faa3a293f9ca56c0d63575716847e3f77039b3e94b912092
MD5 b291eed56bd39cdafaa05abc1a744482
BLAKE2b-256 e23aba7c4576072ec45990b310a07c66d51a571d0b9db52e50d1d2c3a9b4206a

See more details on using hashes here.

Provenance

File details

Details for the file torchsnapshot_nightly-2022.11.21-py3-none-any.whl.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2022.11.21-py3-none-any.whl
Algorithm Hash digest
SHA256 90c61e82bb49fcef7c4a8bd75543a31dbb9929f02a20d5d72bca374b9ef01e19
MD5 9ed25f73f49fa9dade1991783ccd7fa5
BLAKE2b-256 fcf00cebae141982d0c43ad9a12c825ba244d2d51f75da98f45c008c0232e17b

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