Added Intel Gaudi to Accelerator Setup Guide (#6543)

Added Intel Gaudi to the list of accelerators in the setup guide.

Co-authored-by: sakell <sakell@habana.ai>
Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
This commit is contained in:
ShifaAbu 2024-09-17 01:24:45 +03:00 коммит произвёл GitHub
Родитель 170b46e8b1
Коммит 2a56f53395
Не найден ключ, соответствующий данной подписи
Идентификатор ключа GPG: B5690EEEBB952194
1 изменённых файлов: 8 добавлений и 0 удалений

Просмотреть файл

@ -9,6 +9,7 @@ tags: getting-started training accelerator
- [Intel Architecture (IA) CPU](#intel-architecture-ia-cpu)
- [Intel XPU](#intel-xpu)
- [Huawei Ascend NPU](#huawei-ascend-npu)
- [Intel Gaudi](#intel-gaudi)
# Introduction
DeepSpeed supports different accelerators from different companies. Setup steps to run DeepSpeed on certain accelerators might be different. This guide allows user to lookup setup instructions for the accelerator family and hardware they are using.
@ -246,3 +247,10 @@ accelerator: npu
## Multi-card parallel training using Huawei Ascend NPU
To perform model training across multiple Huawei Ascend NPU cards using DeepSpeed, see the examples provided in [DeepSpeed Examples](https://github.com/microsoft/DeepSpeedExamples/blob/master/training/cifar/cifar10_deepspeed.py).
# Intel Gaudi
PyTorch models can be run on Intel® Gaudi® AI accelerator using DeepSpeed. Refer to the following user guides to start using DeepSpeed with Intel Gaudi:
* [Getting Started with DeepSpeed](https://docs.habana.ai/en/latest/PyTorch/DeepSpeed/Getting_Started_with_DeepSpeed/Getting_Started_with_DeepSpeed.html#getting-started-with-deepspeed)
* [DeepSpeed User Guide for Training](https://docs.habana.ai/en/latest/PyTorch/DeepSpeed/DeepSpeed_User_Guide/DeepSpeed_User_Guide.html#deepspeed-user-guide)
* [Optimizing Large Language Models](https://docs.habana.ai/en/latest/PyTorch/DeepSpeed/Optimizing_LLM.html#llms-opt)
* [Inference Using DeepSpeed](https://docs.habana.ai/en/latest/PyTorch/DeepSpeed/Inference_Using_DeepSpeed.html#deepspeed-inference-user-guide)