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.
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-2023.3.11.tar.gz
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 19423c6164cc0a0aefd427682bc77436c5022dd45f2982a34f1307cbb712dcad |
|
| MD5 | 85043406b90926bf5242abe91f15696b |
|
| BLAKE2b-256 | cb769ab6816b292233114870b8178d6d5e935f36c8226c731d7646ba7b4d825d |
Close
Hashes for torchsnapshot_nightly-2023.3.11-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 5157a5cd2f97b8786a2fe5819e839c22320fbb50b189e97cf99f2a27a8e376a0 |
|
| MD5 | 5d35249e8b6e7100df418ac116e073de |
|
| BLAKE2b-256 | 8edbd7be7c54e68b9108f1fab974f062eacebe5321307a401e54db41463b3ef0 |
