A lightweight library for adding fault tolerance to large-scale PyTorch distributed training workloads.
Project description
torchsnapshot
This library is currently in Alpha and currently does not have a stable release. The API may change and may not be backward compatible. If you have suggestions for improvements, please open a GitHub issue. We'd love to hear your feedback.
A light-weight library for adding fault tolerance to large-scale PyTorch distributed training workloads.
Install
Requires Python >= 3.7 and PyTorch >= 1.11
From pip:
pip install --pre torchsnapshot-nightly
From source:
git clone https://github.com/facebookresearch/torchsnapshot
cd torchsnapshot
pip install -r requirements.txt
python setup.py install
Concepts
- Stateful object - an object that whose state can be obtained via
.state_dict()and restored via.load_state_dict(). Most PyTorch components (e.g.Module,Optimizer,LRScheduler) already implement this protocol. - App state - the application state described using multiple stateful objects.
- Snapshot - the persisted app state.
Basic Usage
Describing the application state with multiple stateful objects:
app_state = {"model": model, "optimizer": optimizer}
Taking a snapshot of the application state:
from torchsnapshot import Snapshot
# File System
snapshot = Snapshot.take(path="/foo/bar/baz", app_state=app_state)
# S3
snapshot = Snapshot.take(path="s3://foo/bar", app_state=app_state)
# Google Cloud Storage
snapshot = Snapshot.take(path="gcs://foo/bar", app_state=app_state)
Referencing an existing snapshot:
snapshot = Snapshot(path="foo/bar/baz")
Restoring the application state from a snapshot:
snapshot.restore(app_state=app_state)
See the example directory for more examples.
License
torchsnapshot is BSD licensed, as found in the LICENSE file.
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
Hashes for torchsnapshot-nightly-2022.10.13.tar.gz
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 1968cf977d9864c8912014f6865236da515d05dedbbf8aeaa811642549790659 |
|
| MD5 | 530c6425e731f335de0e170d057b10de |
|
| BLAKE2b-256 | 7046353b32668df0285d3395beb6522c80492a197a7f681d8845a52b0fd53660 |
Hashes for torchsnapshot_nightly-2022.10.13-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 9c6e95e5b4145a2e4af20123ea077d0e4b9ebff79b6a7bccc105954378b41332 |
|
| MD5 | 66ec2c702f98c1a3c45bbf89995bdcbf |
|
| BLAKE2b-256 | 98b093510ae7055357d9a3262768095734b4e458c1b75c731b12dd554fc49748 |
