A performant, memory-efficient checkpointing library for PyTorch applications, designed with large, complex distributed workloads in mind.
Project description
TorchSnapshot (Beta Release)
A performant, memory-efficient checkpointing library for PyTorch applications, designed with large, complex distributed workloads in mind.
Install
Requires Python >= 3.7 and PyTorch >= 1.12
From pip:
# Stable
pip install torchsnapshot
# Or, using conda
conda install -c conda-forge torchsnapshot
# Nightly
pip install --pre torchsnapshot-nightly
From source:
git clone https://github.com/pytorch/torchsnapshot
cd torchsnapshot
pip install -r requirements.txt
python setup.py install
Why TorchSnapshot
Performance
- TorchSnapshot provides a fast checkpointing implementation employing various optimizations, including zero-copy serialization for most tensor types, overlapped device-to-host copy and storage I/O, parallelized storage I/O.
- TorchSnapshot greatly speeds up checkpointing for DistributedDataParallel workloads by distributing the write load across all ranks (benchmark).
- When host memory is abundant, TorchSnapshot allows training to resume before all storage I/O completes, reducing the time blocked by checkpoint saving.
Memory Usage
- TorchSnapshot's memory usage adapts to the host's available resources, greatly reducing the chance of out-of-memory issues when saving and loading checkpoints.
- TorchSnapshot supports efficient random access to individual objects within a snapshot, even when the snapshot is stored in a cloud object storage.
Usability
- Simple APIs that are consistent between distributed and non-distributed workloads.
- Out of the box integration with commonly used cloud object storage systems.
- Automatic resharding (elasticity) on world size change for supported workloads (more details).
Security
- Secure tensor serialization without pickle dependency [WIP].
Getting Started
from torchsnapshot import Snapshot
# Taking a snapshot
app_state = {"model": model, "optimizer": optimizer}
snapshot = Snapshot.take(path="/path/to/snapshot", app_state=app_state)
# Restoring from a snapshot
snapshot.restore(app_state=app_state)
See the documentation for more details.
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
Close
Hashes for torchsnapshot-nightly-2023.9.30.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | d79658e0dde94695139c6e37790aa954922e513ba58fe247470410ef3d8d7a69 |
|
MD5 | eadaeeb95c0c36d3dd5a6128760eb2a6 |
|
BLAKE2b-256 | b2f60a39517d640c1f91792564dfe33cd32a8b4218f68c385105b4b96e907331 |
Close
Hashes for torchsnapshot_nightly-2023.9.30-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | cbe2201032d01f1ec8173d8a70e451e572a5f5571bb5677e16bc9b9bd0aa01ab |
|
MD5 | 133c8e9146267c448989d44a4353190f |
|
BLAKE2b-256 | a49a546216dd191b90fa333d4a38ac908972e8ca61a5d1a8f7907ff0a4bd74e8 |