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79 lines
2.8 KiB
Markdown
79 lines
2.8 KiB
Markdown
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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rendered properly in your Markdown viewer.
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# Intel Gaudi
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The Intel Gaudi AI accelerator family includes [Intel Gaudi 1](https://habana.ai/products/gaudi/), [Intel Gaudi 2](https://habana.ai/products/gaudi2/), and [Intel Gaudi 3](https://habana.ai/products/gaudi3/). Each server has 8 Habana Processing Units (HPUs) with 128GB of memory on Gaudi 3, 96GB on Gaudi 2, and 32GB on first-gen Gaudi. The [Gaudi Architecture](https://docs.habana.ai/en/latest/Gaudi_Overview/Gaudi_Architecture.html) overview covers the hardware in depth.
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[`TrainingArguments`], [`Trainer`], and [`Pipeline`] detect Intel Gaudi devices and set the backend to `hpu` automatically.
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## Environment variables
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HPU lazy mode isn't compatible with all Transformers modeling code. Set the environment variable below to switch to eager mode if there are errors.
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```bash
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export PT_HPU_LAZY_MODE=0
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```
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You may also need to enable int64 support to avoid casting issues with long integers.
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```bash
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export PT_ENABLE_INT64_SUPPORT=1
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```
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## Mixed precision
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All Gaudi generations support bf16 natively.
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```python
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from transformers import TrainingArguments
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training_args = TrainingArguments(
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output_dir="./outputs",
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bf16=True, # supported on all Gaudi generations
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)
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```
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## torch.compile
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Gaudi supports [torch.compile](). [`TrainingArguments`] automatically sets `torch_compile_backend` to `"hpu_backend"` when HPU is detected.
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```python
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from transformers import TrainingArguments
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training_args = TrainingArguments(
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output_dir="./outputs",
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torch_compile=True,
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)
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```
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## Distributed training
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Multi-HPU training uses [HCCL](https://docs.habana.ai/en/latest/API_Reference_Guides/HCCL_APIs/index.html) (Habana Collective Communications Library) as the distributed backend. HCCL is the default, but you can also set `ddp_backend` explicitly.
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```python
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from transformers import TrainingArguments
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training_args = TrainingArguments(
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output_dir="./outputs",
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ddp_backend="hccl",
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)
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```
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## Next steps
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- See the [Gaudi docs](https://docs.habana.ai/en/latest/index.html) for more detailed information about training.
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- Try [Optimum for Intel Gaudi](https://huggingface.co/docs/optimum/main/en/habana/index) for Gaudi-optimized model implementations during training and inference.
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