endpoint using WSGI specification, which works with Python servers using WSGI-interface.
name: name of the deployment endpoint, this will be the final part of the URL
image: the Docker image that will be used as the deployment environment
wsgi: specifies the WSGI application to serve, specify the module (e.g.
package.app) or the module and the WSGI callable (e.g.
description: (optional) more detailed human-readable description of the endpoint
files: (optional) files that will be loaded into the image, for example, the trained model. The files will be in the same directory as your code, modified by the
- endpoint: name: wsgi-endpoint description: predict digits from image inputs image: tensorflow/tensorflow:1.3.0-py3 wsgi: predict_wsgi:predict_wsgi files: - name: model description: Model output file from TensorFlow path: model.pb
Read more about WSGI on their website.
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