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-2022.9.12.tar.gz
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d4e32125c77b77ca8d9bb608147fe38fa5a0a5f0c147b647c1525af70b2b435c |
|
| MD5 | e85b54ebc56b1032ff93f4337a5f1aee |
|
| BLAKE2b-256 | e9a82e41c71b46634f6645a907f97babd48b7e44793e9cb458cbadabb83f4f46 |
Hashes for torchsnapshot_nightly-2022.9.12-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 250e66f9cc5d7f9027245e1b817be4df65df9fb6b929cd1c71914032b83e2e44 |
|
| MD5 | b756c6e1a9da12dd2388e7494c3e11eb |
|
| BLAKE2b-256 | e57209a63cfe07937a57674f29f4b6d0f1a0a560abf7f5fc85ea9f745bbdd651 |
