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 >= 3.8 and PyTorch >= 2.0.0
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.
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-2023.11.20.tar.gz
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 65bbdbb57884317752536602a6c6c5557d89d33541a79d66e03128a67a873180 |
|
| MD5 | d4c1ca8c2bb3f732cee738a70d32847f |
|
| BLAKE2b-256 | ecb0ec87881dd7c87c6d5332a4a1a262f8dadbdc3dc35a3d4a2ab5dd5b659f16 |
Close
Hashes for torchsnapshot_nightly-2023.11.20-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 6e9409f4afe4fec8c8825db41841556c14eb9cac91de673391f1fcf74e5f588c |
|
| MD5 | 0c1dee6caac9ef030f9147575bfff3d8 |
|
| BLAKE2b-256 | 152e38aebabe59295a0390f79a91abdebc6aad9ef4335af45eda0928346121ed |
