Accelerate
Hugging Face library to run PyTorch training on CPU, single GPU, multi-GPU, or TPU with minimal code changes.
Why it is included
Default portability layer for HF training scripts and many community fine-tune stacks.
Best for
Authors who want one codebase spanning laptop debug and cluster training.
Strengths
- Simple API
- DeepSpeed/FSDP hooks
- HF ecosystem
Limitations
- Not a full experiment platform alone
Good alternatives
PyTorch Lightning · DeepSpeed raw · Ray Train
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Microsoft library for extreme-scale model training: ZeRO optimizer states, pipeline parallelism, and inference kernels.
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State-of-the-art pretrained models for PyTorch, TensorFlow, and JAX.
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Deep learning framework with strong research-to-production paths.
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YAML-configured fine-tuning for LLMs: LoRA, QLoRA, FSDP, and many architectures on top of Hugging Face trainers.
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Distributed compute framework for Python: scale data loading, training, hyperparameter search, and online serving (Ray Serve).