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-2022.7.1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 971e629b6c1138bacbdd8a069db1864cfb2ef9840dd85549943121752c3c6068 |
|
MD5 | 18e69323309688c16d4090bb616671f4 |
|
BLAKE2b-256 | 9dbca27da8e56164b558c07806d3e28c1d0aaa49c8778a1cd4c95901f66434e9 |
Hashes for torchsnapshot_nightly-2022.7.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b8399248fd4a88b9e6b0e80c01667924f08acf4a04a341415661bac5b136c622 |
|
MD5 | ee8e677b99c99316cae163c746e19742 |
|
BLAKE2b-256 | 032ddcc8672a961e7da8dfee8243f406abc4a0f40d14f4a233e4c2012cc885c9 |