π€ Optimum Habana
π€ Optimum Habana is the interface between the π€ Transformers and π€ Diffusers libraries and Habanaβs Gaudi processor (HPU). It provides a set of tools that enable easy model loading, training and inference on single- and multi-HPU settings for various downstream tasks as shown in the table below.
HPUs offer fast model training and inference as well as a great price-performance ratio. Check out this blog post about BERT pre-training and this article benchmarking Habana Gaudi2 versus Nvidia A100 GPUs for concrete examples. If you are not familiar with HPUs, we recommend you take a look at our conceptual guide.
The following model architectures, tasks and device distributions have been validated for π€ Optimum Habana:
In the tables below, β means single-card, multi-card and DeepSpeed have all been validated.
- Transformers
- Diffusers
Architecture | Training | Inference | <center>Tasks</center> |
---|---|---|---|
Stable Diffusion | β | ||
LDM3D | β |
Other models and tasks supported by the π€ Transformers and π€ Diffusers library may also work. You can refer to this section for using them with π€ Optimum Habana. Besides, this page explains how to modify any example from the π€ Transformers library to make it work with π€ Optimum Habana.
Learn the basics and become familiar with training transformers on HPUs with π€ Optimum. Start here if you are using π€ Optimum Habana for the first time!
Practical guides to help you achieve a specific goal. Take a look at these guides to learn how to use π€ Optimum Habana to solve real-world problems.
High-level explanations for building a better understanding of important topics such as HPUs.
Technical descriptions of how the Habana classes and methods of π€ Optimum Habana work.