A performant, memory-efficient checkpointing library for PyTorch applications, designed with large, complex distributed workloads in mind.
Project description
TorchSnapshot (Beta Release)
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
Built Distribution
Close
Hashes for torchsnapshot_nightly-2024.7.9.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | b45dc756b33e58805e080ed34c192ccd9b7834b7af196739e28723a6fb0c9b09 |
|
MD5 | 0f679794e67bbeedd8fc9f75f9a78137 |
|
BLAKE2b-256 | 55c33b360be6313f182b4918a7dfff9b51ff16342cd7abdc7c87cbe5ae92fcbb |
Close
Hashes for torchsnapshot_nightly-2024.7.9-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0abadac3df648af4bab1a94ebbd3671aa7b08e27ee47a1ae41dfe9ff2e338a08 |
|
MD5 | 3103ad2063d68208717e1d037833466c |
|
BLAKE2b-256 | 3c5719f1daaf79da537dbd04e134f506b2544454c04d5668a56d429ef08e6d2e |