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
# 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-2023.3.15.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1de86660cf8408f6686e2461728e6f2aacfe31cbf05b9fc9655075cf6b480edd |
|
MD5 | 9067cede5e1095262f70af0b32875e5e |
|
BLAKE2b-256 | 8a3c55ff142b9b2f20459e8f8b220876fcb6910390caee363c522bcf71b9a409 |
Close
Hashes for torchsnapshot_nightly-2023.3.15-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9c52cfdf2639b3680bdf50a21e16fada3355616ed7c0a110acde15a8b8db25f4 |
|
MD5 | 9ddb36ac895e0ae2e54d39a05f48f8f7 |
|
BLAKE2b-256 | d6b7841a3db976d648bc73f54530deb04a7c2885675db50dd9050daf9197ee70 |