Introducing Hugging Face Optimum
Hugging Face Optimum is a versatile extension designed to work seamlessly with Hugging Face’s popular tools, Transformers and Diffusers. This extension aims to optimize models for both training and inference on various hardware platforms, offering users maximum efficiency without complexity.
Getting Started with Installation
To get started with Optimum, users can easily install it using Python's package manager pip with the following command:
python -m pip install optimum
Additionally, Optimum provides specialized features to enhance performance on different accelerators. These features require specific dependencies, which can be installed as per the accelerator being used. For instance, using an ONNX Runtime accelerator requires:
pip install --upgrade --upgrade-strategy eager optimum[onnxruntime]
Each accelerator has its own installation command, which ensures that users can tailor the Optimum installation to suit their specific hardware needs.
Accelerated Inference Capabilities
Optimum enables users to quickly export and run models that are optimized across several ecosystems. It supports various technologies such as:
- ONNX and ONNX Runtime
- TensorFlow Lite
- OpenVINO
- Habana Gaudi processors, and others
The library offers users the ability to perform graph optimizations and apply quantizations, enhancing model performance across platforms. A notable feature is the ability to seamlessly switch model classes for using specific runtimes, facilitating easy integration in different environments.
Exploring Key Features
Optimum brings a host of optimization features to the table which include:
- Graph optimization to streamline model computations
- Post-training dynamic and static quantization
- Quantization Aware Training (QAT)
- FP16 precision for efficiency without significant accuracy loss
- Model pruning and knowledge distillation to compress models
These features are selectively supported across various platforms, giving users the flexibility to choose optimizations that best meet their requirements.
OpenVINO Integration
For those looking to use OpenVINO, Optimum provides straightforward steps to export models to the OpenVINO format. It allows users to quantize both weights and activations to improve performance while maintaining the model's accuracy.
By altering a few lines of code, users can migrate to the OpenVINO environment when running inference. This ease of transition underlines Optimum's focus on user-friendly operations.
Utilizing Neural Compressor and ONNX Runtime
Optimum also integrates with Intel’s Neural Compressor and ONNX Runtime for boosting model efficiency. Users can apply dynamic quantization to their models or use programmatic commands to export them into the ONNX format and optimize them appropriately.
Once models are exported and optimized, Optimum offers Python classes to run these models using ONNX Runtime, enabling smooth and efficient operations.
TensorFlow Lite and Accelerated Training Support
Models can also be exported and quantized to TensorFlow Lite format, adhering to robust yet simple guidelines provided by Optimum.
For training purposes, Optimum provides wrappers around the Hugging Face Transformers’ Trainer for efficient training on advanced hardware. It supports Habana Gaudi processors and AWS Trainium instances, allowing users to easily harness the power of these hardware accelerators. The ONNX Runtime is also available for optimized GPU training.
Quantization with Quanto
Quanto is Optimum's own PyTorch quantization backend that allows for efficient model quantization. It supports model quantization through intuitive commands, reducing model size and inference latency, while maintaining performance.
Users can quantize their models using either Optimum's Python API or command-line interface, making it accessible for users with varied technical expertise.
Ease of Integration and User-Centric Design
Overall, Hugging Face Optimum is a powerful toolkit that caters to a wide range of machine learning needs, making advanced optimizations accessible to users. Through its efficient hardware utilization and simplified processes, it paves the way for faster, more economical model deployments across diverse platforms.