optimum 2.1.0
Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality.
License: Apache
Requires Python: >=3.9.0
Keywords: transformers, quantization, pruning, optimization, training, inference, onnx, onnx runtime, intel, habana, graphcore, neural compressor, ipu, hpu

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<h1 align="center"><p>🤗 Optimum</p></h1>

<p align="center">
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</p>

<p align="center">
Optimum is an extension of Transformers 🤖 Diffusers 🧨 TIMM 🖼️ and Sentence-Transformers 🤗, providing a set of optimization tools and enabling maximum efficiency to train and run models on targeted hardware, while keeping things easy to use.
</p>

## Installation

Optimum can be installed using `pip` as follows:

```bash
python -m pip install optimum
```

If you'd like to use the accelerator-specific features of Optimum, you can check the documentation and install the required dependencies according to the table below:

| Accelerator                                                                         | Installation                                                                |
| :---------------------------------------------------------------------------------- | :-------------------------------------------------------------------------- |
| [ONNX](https://huggingface.co/docs/optimum-onnx/en/index)                           | `pip install --upgrade --upgrade-strategy eager optimum[onnx]`              |
| [ONNX Runtime](https://huggingface.co/docs/optimum-onnx/onnxruntime/overview)       | `pip install --upgrade --upgrade-strategy eager optimum[onnxruntime]`       |
| [ONNX Runtime GPU](https://huggingface.co/docs/optimum-onnx/onnxruntime/overview)   | `pip install --upgrade --upgrade-strategy eager optimum[onnxruntime-gpu]`   |
| [Intel Neural Compressor](https://huggingface.co/docs/optimum/intel/index)          | `pip install --upgrade --upgrade-strategy eager optimum[neural-compressor]` |
| [OpenVINO](https://huggingface.co/docs/optimum/intel/index)                         | `pip install --upgrade --upgrade-strategy eager optimum[openvino]`          |
| [IPEX](https://huggingface.co/docs/optimum/intel/index)                             | `pip install --upgrade --upgrade-strategy eager optimum[ipex]`              |
| [NVIDIA TensorRT-LLM](https://huggingface.co/docs/optimum/main/en/nvidia_overview)  | `docker run -it --gpus all --ipc host huggingface/optimum-nvidia`           |
| [AMD Instinct GPUs and Ryzen AI NPU](https://huggingface.co/docs/optimum/amd/index) | `pip install --upgrade --upgrade-strategy eager optimum[amd]`               |
| [AWS Trainum & Inferentia](https://huggingface.co/docs/optimum-neuron/index)        | `pip install --upgrade --upgrade-strategy eager optimum[neuronx]`           |
| [Intel Gaudi Accelerators (HPU)](https://huggingface.co/docs/optimum/habana/index)  | `pip install --upgrade --upgrade-strategy eager optimum[habana]`            |
| [FuriosaAI](https://huggingface.co/docs/optimum/furiosa/index)                      | `pip install --upgrade --upgrade-strategy eager optimum[furiosa]`           |

The `--upgrade --upgrade-strategy eager` option is needed to ensure the different packages are upgraded to the latest possible version.

To install from source:

```bash
python -m pip install git+https://github.com/huggingface/optimum.git
```

For the accelerator-specific features, append `optimum[accelerator_type]` to the above command:

```bash
python -m pip install optimum[onnxruntime]@git+https://github.com/huggingface/optimum.git
```

## Accelerated Inference

Optimum provides multiple tools to export and run optimized models on various ecosystems:

- [ONNX](https://huggingface.co/docs/optimum-onnx/en/onnx/usage_guides/export_a_model) / [ONNX Runtime](https://huggingface.co/docs/optimum-onnx/en/onnxruntime/usage_guides/models), one of the most popular open formats for model export, and a high-performance inference engine for deployment.
- [OpenVINO](https://huggingface.co/docs/optimum/intel/inference), a toolkit for optimizing, quantizing and deploying deep learning models on Intel hardware.
- [ExecuTorch](https://huggingface.co/docs/optimum-executorch/guides/export), PyTorch’s native solution for on-device inference across mobile and edge devices.
- [Intel Gaudi Accelerators](https://huggingface.co/docs/optimum/main/en/habana/usage_guides/accelerate_inference) enabling optimal performance on first-gen Gaudi, Gaudi2 and Gaudi3.
- [AWS Inferentia](https://huggingface.co/docs/optimum-neuron/en/guides/models) for accelerated inference on Inf2 and Inf1 instances.
- [NVIDIA TensorRT-LLM](https://huggingface.co/blog/optimum-nvidia).

The [export](https://huggingface.co/docs/optimum/exporters/overview) and optimizations can be done both programmatically and with a command line.

### ONNX + ONNX Runtime

🚨🚨🚨 ONNX integration was moved to [`optimum-onnx`](https://github.com/huggingface/optimum-onnx) so make sure to follow the installation instructions 🚨🚨🚨

Before you begin, make sure you have all the necessary libraries installed :

```bash
pip install --upgrade --upgrade-strategy eager optimum[onnx]
```

It is possible to export Transformers, Diffusers, Sentence Transformers and Timm models to the [ONNX](https://onnx.ai/) format and perform graph optimization as well as quantization easily.

For more information on the ONNX export, please check the [documentation](https://huggingface.co/docs/optimum-onnx/en/onnx/usage_guides/export_a_model).

Once the model is exported to the ONNX format, we provide Python classes enabling you to run the exported ONNX model in a seamless manner using [ONNX Runtime](https://onnxruntime.ai/) in the backend.

For this make sure you have ONNX Runtime installed, fore more information check out the [installation instructions](https://onnxruntime.ai/docs/install/).

More details on how to run ONNX models with `ORTModelForXXX` classes [here](https://huggingface.co/docs/optimum-onnx/en/onnxruntime/usage_guides/models).

### Intel (OpenVINO + Neural Compressor + IPEX)

Before you begin, make sure you have all the necessary [libraries installed](https://huggingface.co/docs/optimum/main/en/intel/installation).

You can find more information on the different integration in our [documentation](https://huggingface.co/docs/optimum/main/en/intel/index) and in the examples of [`optimum-intel`](https://github.com/huggingface/optimum-intel).

### ExecuTorch

Before you begin, make sure you have all the necessary libraries installed :

```bash
pip install optimum-executorch@git+https://github.com/huggingface/optimum-executorch.git
```

Users can export Transformers models to [ExecuTorch](https://github.com/pytorch/executorch) and run inference on edge devices within PyTorch's ecosystem.

For more information about export Transformers to ExecuTorch, please check the doc for [Optimum-ExecuTorch](https://huggingface.co/docs/optimum-executorch/guides/export).

### Quanto

[Quanto](https://github.com/huggingface/optimum-quanto) is a pytorch quantization backend which allows you to quantize a model either using the python API or the `optimum-cli`.

You can see more details and [examples](https://github.com/huggingface/optimum-quanto/tree/main/examples) in the [Quanto](https://github.com/huggingface/optimum-quanto) repository.

## Accelerated training

Optimum provides wrappers around the original Transformers [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) to enable training on powerful hardware easily.
We support many providers:

- [Intel Gaudi Accelerators (HPU)](https://huggingface.co/docs/optimum/main/en/habana/usage_guides/accelerate_training) enabling optimal performance on first-gen Gaudi, Gaudi2 and Gaudi3.
- [AWS Trainium](https://huggingface.co/docs/optimum-neuron/training_tutorials/sft_lora_finetune_llm) for accelerated training on Trn1 and Trn1n instances.
- ONNX Runtime (optimized for GPUs).

### Intel Gaudi Accelerators

Before you begin, make sure you have all the necessary libraries installed :

```bash
pip install --upgrade --upgrade-strategy eager optimum[habana]
```

You can find examples in the [documentation](https://huggingface.co/docs/optimum/habana/quickstart) and in the [examples](https://github.com/huggingface/optimum-habana/tree/main/examples).

### AWS Trainium

Before you begin, make sure you have all the necessary libraries installed :

```bash
pip install --upgrade --upgrade-strategy eager optimum[neuronx]
```

You can find examples in the [documentation](https://huggingface.co/docs/optimum-neuron/index) and in the [tutorials](https://huggingface.co/docs/optimum-neuron/tutorials/fine_tune_bert).