lluunn and k8s-ci-robot Enable tf serving prometheus metrics (#1911)
* Enable tf serving prometheus metrics

* formatting

* add unit test
Latest commit 4c1bfb2 Nov 6, 2018

README.md

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tf-serving

TensorFlow serving is a server for TensorFlow models.

Quickstart

The following commands use the io.ksonnet.pkg.tf-serving prototype to generate Kubernetes YAML for tf-serving, and then deploys it to your Kubernetes cluster.

First, create a cluster and install the ksonnet CLI (see root-level README.md).

If you haven't yet created a ksonnet application, do so using ks init <app-name>.

Finally, in the ksonnet application directory, run the following:

# Expand prototype as a Jsonnet file, place in a file in the
# `components/` directory. (YAML and JSON are also available.)
$ ks prototype use io.ksonnet.pkg.tf-serving tf-serving \
  --name tf-serving \
  --namespace default

# Apply to server.
$ ks apply -f tf-serving.jsonnet

Using the library

The library files for tf-serving define a set of relevant parts (e.g., deployments, services, secrets, and so on) that can be combined to configure tf-serving for a wide variety of scenarios. For example, a database like Redis may need a secret to hold the user password, or it may have no password if it's acting as a cache.

This library provides a set of pre-fabricated "flavors" (or "distributions") of tf-serving, each of which is configured for a different use case. These are captured as ksonnet prototypes, which allow users to interactively customize these distributions for their specific needs.

These prototypes, as well as how to use them, are enumerated below.

io.ksonnet.pkg.tf-serving

TensorFlow serving

Example

# Expand prototype as a Jsonnet file, place in a file in the
# `components/` directory. (YAML and JSON are also available.)
$ ks prototype use io.ksonnet.pkg.tf-serving tf-serving \
  --name YOUR_NAME_HERE \
  --model_path YOUR_MODEL_PATH_HERE

Parameters

The available options to pass prototype are:

  • --name=<name>: Name to give to each of the components [string]
  • --model_path=<model_path>: Path to the model. This can be a GCS path. [string]