1.8 KiB
(serve-tensorflow-tutorial)=
Keras and Tensorflow Tutorial
In this guide, we will train and deploy a simple Tensorflow neural net. In particular, we show:
- How to load the model from file system in your Ray Serve definition
- How to parse the JSON request and evaluated in Tensorflow
Please see the Key Concepts to learn more general information about Ray Serve.
Ray Serve is framework agnostic -- you can use any version of Tensorflow. However, for this tutorial, we use Tensorflow 2 and Keras. Please make sure you have Tensorflow 2 installed.
pip install "tensorflow>=2.0"
Let's import Ray Serve and some other helpers.
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We will train a simple MNIST model using Keras.
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Services are just defined as normal classes with __init__ and __call__ methods.
The __call__ method will be invoked per request.
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Now that we've defined our services, let's deploy the model to Ray Serve. We will define a Serve deployment that will be exposed over an HTTP route.
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Let's query it!
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