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-2022.9.22.tar.gz
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 7aff5d07ae6bae31d5eddf04ed1094d36aefb79c38f7d4e020ac1c2c0834f30b |
|
| MD5 | cb11f32025226a1867b718edda2d4feb |
|
| BLAKE2b-256 | 8fe68b34822c1e1f68efa6a593a3071a6d38c1aed438902b1d1f314a10d6a7d8 |
Hashes for torchsnapshot_nightly-2022.9.22-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | a04c7e56791b170604d84c3770a2d9a474e56aac1d35e5d049cabe16599ca58b |
|
| MD5 | 49e668540e2b6c6c1ee0165378188df7 |
|
| BLAKE2b-256 | 1a2732fa335fb5b1fc72ae5fc1ab3967023ff30350eec0da698f7a6f301e0785 |
