optimum 2.2.0

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.

🤗 Optimum

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.

Installation

Optimum can be installed using pip as follows:

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	pip install --upgrade --upgrade-strategy eager optimum[onnx]
ONNX Runtime	pip install --upgrade --upgrade-strategy eager optimum[onnxruntime]
ONNX Runtime GPU	pip install --upgrade --upgrade-strategy eager optimum[onnxruntime-gpu]
OpenVINO	pip install --upgrade --upgrade-strategy eager optimum[openvino]
NVIDIA TensorRT-LLM	docker run -it --gpus all --ipc host huggingface/optimum-nvidia
AMD Instinct GPUs and Ryzen AI NPU	pip install --upgrade --upgrade-strategy eager optimum[amd]
AWS Trainum & Inferentia	pip install --upgrade --upgrade-strategy eager optimum[neuronx]
Intel Gaudi Accelerators (HPU)	pip install --upgrade --upgrade-strategy eager optimum[habana]
FuriosaAI	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:

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

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

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 / ONNX Runtime, one of the most popular open formats for model export, and a high-performance inference engine for deployment.
• OpenVINO, a toolkit for optimizing, quantizing and deploying deep learning models on Intel hardware.
• ExecuTorch, PyTorch’s native solution for on-device inference across mobile and edge devices.
• Intel Gaudi Accelerators enabling optimal performance on first-gen Gaudi, Gaudi2 and Gaudi3.
• AWS Inferentia for accelerated inference on Inf2 and Inf1 instances.
• NVIDIA TensorRT-LLM.

The export and optimizations can be done both programmatically and with a command line.

ONNX + ONNX Runtime

🚨🚨🚨 ONNX integration was moved to optimum-onnx so make sure to follow the installation instructions 🚨🚨🚨

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

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

It is possible to export Transformers, Diffusers, Sentence Transformers and Timm models to the ONNX format and perform graph optimization as well as quantization easily.

For more information on the ONNX export, please check the documentation.

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 in the backend.

For this make sure you have ONNX Runtime installed, fore more information check out the installation instructions.

More details on how to run ONNX models with ORTModelForXXX classes here.

Intel (OpenVINO + NNCF)

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

pip install --upgrade --upgrade-strategy eager optimum[openvino]

You can find more information on the different integration in our documentation and in the examples of optimum-intel.

ExecuTorch

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

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

Users can export Transformers models to 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.

Quanto

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 in the Quanto repository.

Accelerated training

Optimum provides wrappers around the original Transformers Trainer to enable training on powerful hardware easily. We support many providers:

• Intel Gaudi Accelerators (HPU) enabling optimal performance on first-gen Gaudi, Gaudi2 and Gaudi3.
• AWS Trainium 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 :

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

You can find examples in the documentation and in the examples.

AWS Trainium

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

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

You can find examples in the documentation and in the tutorials.