# Quickstart

## Quickstart

🌍 Optimum Habana was designed with one goal in mind: **making training and evaluation straightforward for any** 🌍 **Transformers user while leveraging the complete power of Gaudi processors**. There are two main classes one needs to know:

* [`GaudiTrainer`](https://huggingface.co/docs/optimum/habana/package_reference/trainer): the trainer class that takes care of compiling (lazy or eager mode) and distributing the model to run on HPUs, and of performing training and evaluation.
* [`GaudiConfig`](https://huggingface.co/docs/optimum/habana/package_reference/gaudi_config): the class that enables to configure Habana Mixed Precision and to decide whether optimized operators and optimizers should be used or not.

The [`GaudiTrainer`](https://huggingface.co/docs/optimum/habana/package_reference/trainer) is very similar to the 🌍[ Transformers Trainer](https://huggingface.co/docs/transformers/main_classes/trainer), and adapting a script using the Trainer to make it work with Gaudi will mostly consist in simply swapping the `Trainer` class for the `GaudiTrainer` one. That is how most of the [example scripts](https://github.com/huggingface/optimum-habana/tree/main/examples) were adapted from their [original counterparts](https://github.com/huggingface/transformers/tree/main/examples/pytorch).

Copied

```
- from transformers import Trainer, TrainingArguments
+ from optimum.habana import GaudiTrainer, GaudiTrainingArguments

# Define the training arguments
- training_args = TrainingArguments(
+ training_args = GaudiTrainingArguments(
+   use_habana=True,
+   use_lazy_mode=True,
+   gaudi_config_name=gaudi_config_name,
  ...
)

# Initialize our Trainer
- trainer = Trainer(
+ trainer = GaudiTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset
    ... # other arguments
)
```

where `gaudi_config_name` is the name of a model from the [Hub](https://huggingface.co/Habana) (Gaudi configurations are stored in model repositories) or a path to a local Gaudi configuration file (you can see [here](https://huggingface.co/docs/optimum/habana/package_reference/gaudi_config) how to write your own).

### Stable Diffusion

🌍 Optimum Habana also features HPU-optimized support for the 🌍 Diffusers library. Thus, you can easily deploy Stable Diffusion on Gaudi for performing text-to-image generation.

Here is how to use it and the differences with the 🌍 Diffusers library:

Copied

```
- from diffusers import DDIMScheduler, StableDiffusionPipeline
+ from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline


model_name = "runwayml/stable-diffusion-v1-5"

- scheduler = DDIMScheduler.from_pretrained(model_name, subfolder="scheduler")
+ scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler")

- pipeline = StableDiffusionPipeline.from_pretrained(
+ pipeline = GaudiStableDiffusionPipeline.from_pretrained(
    model_name,
    scheduler=scheduler,
+   use_habana=True,
+   use_hpu_graphs=True,
+   gaudi_config="Habana/stable-diffusion",
)

outputs = pipeline(
    ["An image of a squirrel in Picasso style"],
    num_images_per_prompt=16,
+   batch_size=4,
)
```

### Ready-to-Use Examples

Here are examples for various modalities and tasks that can be used out of the box:

* Text
  * [text classification](https://github.com/huggingface/optimum-habana/tree/main/examples/text-classification),
  * [question answering](https://github.com/huggingface/optimum-habana/tree/main/examples/question-answering),
  * [language modeling](https://github.com/huggingface/optimum-habana/tree/main/examples/language-modeling),
  * [text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation),
  * [summarization](https://github.com/huggingface/optimum-habana/tree/main/examples/summarization),
  * [translation](https://github.com/huggingface/optimum-habana/tree/main/examples/translation),
  * [protein folding](https://github.com/huggingface/optimum-habana/tree/main/examples/protein-folding)
* Images
  * [image classification](https://github.com/huggingface/optimum-habana/tree/main/examples/image-classification)
* Audio
  * [audio classification](https://github.com/huggingface/optimum-habana/tree/main/examples/audio-classification),
  * [speech recognition](https://github.com/huggingface/optimum-habana/tree/main/examples/speech-recognition)
* Text and images
  * [text-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion),
  * [contrastive image-text training](https://github.com/huggingface/optimum-habana/tree/main/examples/contrastive-image-text).