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Update Orca QuickStart links in README (#7136)

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* update link in README

* update ray link
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Kai Huang 2022-12-30 16:41:41 +08:00 committed by GitHub
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README.md
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@ -110,7 +110,7 @@ flowchart TD;
</details> </details>
*See Orca [user guide](https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/orca.html), as well as [TensorFlow](https://bigdl.readthedocs.io/en/latest/doc/Orca/QuickStart/orca-tf-quickstart.html) and [PyTorch](https://bigdl.readthedocs.io/en/latest/doc/Orca/QuickStart/orca-pytorch-quickstart.html) quickstart, for more details.* *See Orca [user guide](https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/orca.html), as well as [TensorFlow](https://bigdl.readthedocs.io/en/latest/doc/Orca/Howto/tf2keras-quickstart.html) and [PyTorch](https://bigdl.readthedocs.io/en/latest/doc/Orca/Howto/pytorch-quickstart.html) quickstarts, for more details.*
- In addition, you can also run standard **Ray** programs on Spark cluster using _**RayOnSpark**_ in Orca. - In addition, you can also run standard **Ray** programs on Spark cluster using _**RayOnSpark**_ in Orca.
@ -147,7 +147,7 @@ flowchart TD;
</details> </details>
*See RayOnSpark [user guide](https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/ray.html) and [quickstart](https://bigdl.readthedocs.io/en/latest/doc/Orca/QuickStart/ray-quickstart.html) for more details.* *See RayOnSpark [user guide](https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/ray.html) and [quickstart](https://bigdl.readthedocs.io/en/latest/doc/Orca/Howto/ray-quickstart.html) for more details.*
### Nano ### Nano
You can transparently accelerate your TensorFlow or PyTorch programs on your laptop or server using *Nano*. With minimum code changes, *Nano* automatically applies modern CPU optimizations (e.g., SIMD, multiprocessing, low precision, etc.) to standard TensorFlow and PyTorch code, with up-to 10x speedup. You can transparently accelerate your TensorFlow or PyTorch programs on your laptop or server using *Nano*. With minimum code changes, *Nano* automatically applies modern CPU optimizations (e.g., SIMD, multiprocessing, low precision, etc.) to standard TensorFlow and PyTorch code, with up-to 10x speedup.