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update serve verbiage (#16360)

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Eric Liang 2021-06-10 16:23:38 -07:00 • committed by GitHub
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README.rst
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@ -206,8 +206,8 @@ Ray Serve Quick Start
- Framework Agnostic: Use the same toolkit to serve everything from deep
learning models built with frameworks like PyTorch or Tensorflow & Keras
to Scikit-Learn models or arbitrary business logic.
- Python First: Configure your model serving with pure Python code - no more
YAMLs or JSON configs.
- Python First: Configure your model serving declaratively in pure Python,
without needing YAMLs or JSON configs.
- Performance Oriented: Turn on batching, pipelining, and GPU acceleration to
increase the throughput of your model.
- Composition Native: Allow you to create "model pipelines" by composing multiple

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doc/source/serve/index.rst
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@ -25,8 +25,7 @@ Ray Serve is an easy-to-use scalable model serving library built on Ray. Ray Se
- **Framework-agnostic**: Use a single toolkit to serve everything from deep learning models
built with frameworks like :ref:`PyTorch <serve-pytorch-tutorial>`,
:ref:`Tensorflow, and Keras <serve-tensorflow-tutorial>`, to :ref:`Scikit-Learn <serve-sklearn-tutorial>` models, to arbitrary Python business logic.
- **Python-first**: Configure your model serving with pure Python code---no more YAML or
JSON configs.
- **Python-first**: Configure your model serving declaratively in pure Python, without needing YAML or JSON configs.
Since Ray Serve is built on Ray, it allows you to easily scale to many machines, both in your datacenter and in the cloud.