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.
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
Hashes for torchsnapshot-nightly-2022.9.3.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | f81605d7de9a3dcb77654a086641cadbe325d6e32406d1aa13e5416984ceaca9 |
|
MD5 | 54bac6d217984033def0d9cbfbda4338 |
|
BLAKE2b-256 | dcfd0d4ed7e76ed0a571509519d08eddbf4ee900e596c3768b40f16e7c06f770 |
Hashes for torchsnapshot_nightly-2022.9.3-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0c529b38539d18eb5fe8b5975ad2a23ffd6a68304489f3d0a928110626b6005d |
|
MD5 | c4cf782e7ba7d0ef12b233a8380f0f17 |
|
BLAKE2b-256 | aeedc740e1a58b59d1fe255dd964286cdfc71628fe29c5f1db0ce60520562420 |