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# Intel Gaudi

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.

[`TrainingArguments`], [`Trainer`], and [`Pipeline`] detect Intel Gaudi devices and set the backend to `hpu` automatically.

## Environment variables

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.

```bash
export PT_HPU_LAZY_MODE=0
```

You may also need to enable int64 support to avoid casting issues with long integers.

```bash
export PT_ENABLE_INT64_SUPPORT=1
```

## Mixed precision

All Gaudi generations support bf16 natively.

```python
from transformers import TrainingArguments

training_args = TrainingArguments(
    output_dir="./outputs",
    bf16=True,  # supported on all Gaudi generations
)
```

## torch.compile

Gaudi supports [torch.compile](). [`TrainingArguments`] automatically sets `torch_compile_backend` to `"hpu_backend"` when HPU is detected.

```python
from transformers import TrainingArguments

training_args = TrainingArguments(
    output_dir="./outputs",
    torch_compile=True,
)
```

## Distributed training

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.

```python
from transformers import TrainingArguments

training_args = TrainingArguments(
    output_dir="./outputs",
    ddp_backend="hccl",
)
```

## Next steps

- See the [Gaudi docs](https://docs.habana.ai/en/latest/index.html) for more detailed information about training.
- Try [Optimum for Intel Gaudi](https://huggingface.co/docs/optimum/main/en/habana/index) for Gaudi-optimized model implementations during training and inference.
