Updates the distributed CPU training documentation to add instructions for running on a Kubernetes cluster
Author: dmsuehir
Pull request: yes

# What does this PR do?

This PR updates the distributed CPU training documentation to include instructions for using multiple CPU nodes from a Kubernetes cluster. The instructions are kept pretty simple, assuming that the user already has access to a Kubernetes cluster and understands the basics of how to use it. The Kubernetes instructions build upon the existing distributed CPU example by using the same [question answering script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) with [Kubeflow's PyTorch training operator](https://www.kubeflow.org/docs/components/training/pytorch/), while explaining along the way how to use Intel software optimizations and set the CPU/memory limits/requests in the specification file.

CC: @sywangyi @ashahba

## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

@stevhliu and @MKhalusova

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- text models: @ArthurZucker and @younesbelkada
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- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @pacman100

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc and @younesbelkada

Documentation: @stevhliu and @MKhalusova

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Comments:
Thanks for adding practice in Kubernetes cluster.

@stevhliu Thank you for the review, I made the updates that you suggested.