PyTorch training helper for distributed execution
Top 5.8% on sourcepulse
Hugging Face Accelerate simplifies distributed PyTorch training and inference across diverse hardware configurations, including multi-CPU, multi-GPU, and TPUs. It targets PyTorch users who want to leverage distributed computing and mixed precision without extensive boilerplate code modifications, enabling faster and more scalable model development.
How It Works
Accelerate acts as a thin wrapper around PyTorch's distributed capabilities, abstracting away device placement and distributed communication logic. By initializing an Accelerator
object and calling accelerator.prepare()
on models, optimizers, and data loaders, users can seamlessly transition their existing PyTorch training scripts to run on various distributed setups and mixed precision formats (FP16, BF16, FP8) with minimal code changes. This approach preserves the user's control over the training loop while handling the complexities of distributed execution.
Quick Start & Requirements
pip install accelerate
Highlighted Details
accelerate config
) for easy environment setup and script launching.notebook_launcher
for distributed training within notebooks (e.g., Colab, Kaggle).Maintenance & Community
transformers
, fastai
, and stable-diffusion-webui
.Licensing & Compatibility
Limitations & Caveats
2 days ago
1 day