From af11ec079a0ac39475f013d1a73bafb6e78b989b Mon Sep 17 00:00:00 2001 From: Eric Liang Date: Thu, 10 Jun 2021 16:23:38 -0700 Subject: [PATCH] update serve verbiage (#16360) --- README.rst | 4 ++-- doc/source/serve/index.rst | 3 +-- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/README.rst b/README.rst index f00b29689..d452a9fc5 100644 --- a/README.rst +++ b/README.rst @@ -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 diff --git a/doc/source/serve/index.rst b/doc/source/serve/index.rst index 310b09c99..e684daf37 100644 --- a/doc/source/serve/index.rst +++ b/doc/source/serve/index.rst @@ -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 `, :ref:`Tensorflow, and Keras `, to :ref:`Scikit-Learn ` 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.