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update doc for perf_train_cpu_many, add intel mpi introduction (#18576)

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* update doc for perf_train_cpu_many, add mpi introduction

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* Update docs/source/en/perf_train_cpu_many.mdx

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update docs/source/en/perf_train_cpu_many.mdx

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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Wang, Yi
2022-08-12 20:36:27 +08:00
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parent 46d09410eb
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docs/source/en/perf_train_cpu_many.mdx
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@@ -36,8 +36,22 @@ pip install oneccl_bind_pt=={pytorch_version} -f https://software.intel.com/ipex
```
where `{pytorch_version}` should be your PyTorch version, for instance 1.12.0.
Check more approaches for [oneccl_bind_pt installation](https://github.com/intel/torch-ccl).
Versions of oneCCL and PyTorch must match.
### Usage in Trainer
## Intel® MPI library
Use this standards-based MPI implementation to deliver flexible, efficient, scalable cluster messaging on Intel® architecture. This component is part of the Intel® oneAPI HPC Toolkit.
It can be installed via [MPI](https://www.intel.com/content/www/us/en/developer/articles/tool/oneapi-standalone-components.html#mpi).
Please set the environment by following command before using it.
```
source /opt/intel/oneapi/setvars.sh
```
The following "Usage in Trainer" takes mpirun in Intel® MPI library as an example.
## Usage in Trainer
To enable multi CPU distributed training in the Trainer with the ccl backend, users should add **`--xpu_backend ccl`** in the command arguments.
Let's see an example with the [question-answering example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering)