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https://github.com/pytorch/torchsnapshot
A performant, memory-efficient checkpointing library for PyTorch applications, designed with large, complex distributed workloads in mind.
https://github.com/pytorch/torchsnapshot
Last synced: about 1 month ago
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A performant, memory-efficient checkpointing library for PyTorch applications, designed with large, complex distributed workloads in mind.
- Host: GitHub
- URL: https://github.com/pytorch/torchsnapshot
- Owner: pytorch
- License: other
- Created: 2022-05-25T19:12:27.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-21T00:19:41.000Z (6 months ago)
- Last Synced: 2024-05-22T00:53:04.570Z (6 months ago)
- Language: Python
- Homepage: https://pytorch.org/torchsnapshot
- Size: 5.73 MB
- Stars: 132
- Watchers: 21
- Forks: 39
- Open Issues: 17
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Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# 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:
```bash
# Stable
pip install torchsnapshot
# Or, using conda
conda install -c conda-forge torchsnapshot
# Nightly
pip install --pre torchsnapshot-nightly
```
From source:
```bash
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](https://github.com/pytorch/torchsnapshot/tree/main/benchmarks/ddp)).
- 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](https://pytorch.org/torchsnapshot/getting_started.html#elasticity-experimental)).
**Security**
- Secure tensor serialization without pickle dependency [WIP].
## Getting Started
```python
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](https://pytorch.org/torchsnapshot/main/getting_started.html) for more details.
## License
torchsnapshot is BSD licensed, as found in the [LICENSE](LICENSE) file.