Hugging Face Accelerate: Making Device-Agnostic ML Training and Inference Easy...
summary
An open-source library that turns a raw PyTorch loop into distributed training and inference by changing four lines of code: create an accelerator, call accelerator.prepare, adjust device/optimizer, and use accelerate.backward. It supports CPU GPU TPU XPU NPU MLU, fp16 bf16 fp8, DeepSpeed FSDP Megatron and pippy pipeline parallelism, plus device_map='auto' for big-model inference (Llama 7/8/70B and larger). It offers YAML launch configs, 7M monthly users and 110M downloads, and is stabilising toward 1.0.
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chapters (10)
- 0:00Introduction to Accelerate
- 0:34Core training capabilities
- 4:00Working with the accelerator
- 5:14Inference and big models
- 7:13CLI and configuration
- 9:23Advancements and internals
- 13:04Pipeline parallelism
- 15:13Community adoption
- 17:40Future goals and 1.0
- 19:57Q&A session
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