🤗 Optimum is an extension of 🤗 Transformers and Diffusers, providing a set of optimization tools enabling maximum efficiency to train and run models on targeted hardware, while keeping things easy to use.
🤗 Optimum can be installed using pip as follows:
python -m pip install optimumIf you'd like to use the accelerator-specific features of 🤗 Optimum, you can install the required dependencies according to the table below:
| Accelerator | Installation |
|---|---|
| ONNX Runtime | pip install --upgrade --upgrade-strategy eager optimum[onnxruntime] |
| Intel Neural Compressor | pip install --upgrade --upgrade-strategy eager optimum[neural-compressor] |
| 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] |
| Habana Gaudi Processor (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.gitFor 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🤗 Optimum provides multiple tools to export and run optimized models on various ecosystems:
- ONNX / ONNX Runtime
- TensorFlow Lite
- OpenVINO
- Habana first-gen Gaudi / Gaudi2, more details here
- AWS Inferentia 2 / Inferentia 1, more details here
- NVIDIA TensorRT-LLM , more details here
The export and optimizations can be done both programmatically and with a command line.
Before you begin, make sure you have all the necessary libraries installed :
pip install optimum[exporters,onnxruntime]It is possible to export 🤗 Transformers and Diffusers 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 seemless manner using ONNX Runtime in the backend.
More details on how to run ONNX models with ORTModelForXXX classes here.
Before you begin, make sure you have all the necessary libraries installed :
pip install optimum[exporters-tf]Just as for ONNX, it is possible to export models to TensorFlow Lite and quantize them. You can find more information in our documentation.
Before you begin, make sure you have all the necessary libraries installed.
You can find more information on the different integration in our documentation and in the examples of optimum-intel.
Quanto is a pytorch quantization backenb which allowss 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.
🤗 Optimum provides wrappers around the original 🤗 Transformers Trainer to enable training on powerful hardware easily. We support many providers:
- Habana's Gaudi processors
- AWS Trainium instances, check here
- ONNX Runtime (optimized for GPUs)
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.
Before you begin, make sure you have all the necessary libraries installed :
pip install optimum[onnxruntime-training]You can find examples in the documentation and in the examples.
