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 >= 3.7 and PyTorch >= 1.12
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-2023.8.29.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9a72ba61a801eafea679ab81f4563df7d9cc97863c784e9260278853028fc14e |
|
MD5 | b5d99773b0f4c06ec79d02f6526c4c6d |
|
BLAKE2b-256 | 884dc549b5dc2e0e4e46e4344da5046d6a1cec1aead2988fa1f6ae7264a36a18 |
Close
Hashes for torchsnapshot_nightly-2023.8.29-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 227cc5dc570d14751c05fbc114f97a43e3be22d1bf5a648a397ae2546f5dd1d0 |
|
MD5 | 75da1be5e1a5e922b37f9d7f6f586079 |
|
BLAKE2b-256 | 675b59aafae6fd4cd3c15a94f63356fedad3f36d2dc1fe96e506d3d3b3a4884d |