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-2023.11.22.tar.gz (90.1 kB view details)

Uploaded Source

Built Distribution

torchsnapshot_nightly-2023.11.22-py3-none-any.whl (83.1 kB view details)

Uploaded Python 3

File details

Details for the file torchsnapshot-nightly-2023.11.22.tar.gz.

File metadata

File hashes

Hashes for torchsnapshot-nightly-2023.11.22.tar.gz
Algorithm Hash digest
SHA256 caf3dbecdb00dc034a6fc3435a8e2780d359162096619e95f745ec09f5d14f53
MD5 3d321267028468e59ef998abac7d7337
BLAKE2b-256 2340b00b82c6abe531695c6092ee39ef982711f52b37b41632aa2db25cabc32c

See more details on using hashes here.

Provenance

File details

Details for the file torchsnapshot_nightly-2023.11.22-py3-none-any.whl.

File metadata

File hashes

Hashes for torchsnapshot_nightly-2023.11.22-py3-none-any.whl
Algorithm Hash digest
SHA256 e2d4740b7cf39f5f8e4d169162647e4b6a1ccfa2a336a3d80a838d10eeef4e0a
MD5 822f166baa467ad0183a2a232522b86d
BLAKE2b-256 4d9cf55aaad8709c1946022e08f663b4ab4959e5430126a963ca502018e0eea0

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