dockerfiles/tensorflow-serving

Clear Linux* OS tensorflow-serving container image

What is this image?

clearlinux/tensorflow-serving is a Docker image with tensorflow-serving running on top of the official clearlinux base image.

Tensorflow-serving is a flexible, high-performance serving system for machine learning models.

For other Clear Linux* OS based container images, see: https://hub.docker.com/u/clearlinux

Why use a clearlinux based image?

Clear Linux* OS is an open source, rolling release Linux distribution optimized for performance and security, from the Cloud to the Edge, designed for customization, and manageability.

Clear Linux* OS based container images use:

• Optimized libraries that are compiled with latest compiler versions and flags.
• Software packages that follow upstream source closely and update frequently.
• An aggressive security model and best practices for CVE patching.
• A multi-staged build approach to keep a reduced container image size.
• The same container syntax as the official images to make getting started easy.

To learn more about Clear Linux* OS, visit: https://clearlinux.org.

Deployment:

Deploy with Docker

The easiest way to get started with this image is by simply pulling it from Docker Hub.

*Note: This container uses the same syntax as the official tensorflow-serving image.

1. Pull the image from Docker Hub:

   docker pull clearlinux/tensorflow-serving
   

2. Download a tensorflow-serving repo and setup the location of demo models

   git clone https://github.com/tensorflow/serving
   TESTDATA="$(pwd)/serving/tensorflow_serving/servables/tensorflow/testdata"
   

3. Start a container using the examples below:

   docker run -t --rm -p 8501:8501 \
   -v "$TESTDATA/saved_model_half_plus_two_cpu:/models/half_plus_two" \
   -e MODEL_NAME=half_plus_two \
   clearlinux/tensorflow-serving &
   

4. Query the model using the predict API and the return => { "predictions": [2.5, 3.0, 4.5] }

   curl -d '{"instances": [1.0, 2.0, 5.0]}' \
   -X POST http://localhost:8501/v1/models/half_plus_two:predict
   

Deploy with Kubernetes

This image can also be deployed on a Kubernetes cluster, such as minikube.The following example YAML files are provided in the repository as reference for Kubernetes deployment:

• tensorflow-serving-deployment.yaml: example to create a basic tensorflow-serving service.

Steps to deploy tensorflow-serving on a Kubernetes cluster:

1. Download a tensorflow-serving repo.

   cd /var/tmp
   git clone https://github.com/tensorflow/serving
   

2. Deploy tensorflow-serving-deployment.yaml .

   kubectl create -f tensorflow-serving-deployment.yaml
   

3. Query the model using the predict API and the return => { "predictions": [2.5, 3.0, 4.5] }, where 30001 is the port number defined in your service.

   curl -d '{"instances": [1.0, 2.0, 5.0]}' \
   -X POST http://<nodeIP>:30001/v1/models/half_plus_two:predict
   

Build and modify:

The Dockerfiles for all Clear Linux* OS based container images are available at https://github.com/clearlinux/dockerfiles. These can be used to build and modify the container images.

1. Clone the clearlinux/dockerfiles repository.

   git clone https://github.com/clearlinux/dockerfiles.git
   

2. Change to the directory of the application:

   cd tensorflow-serving/
   

3. Build the container image:

   docker build -t clearlinux/tensorflow-serving .
   

   Refer to the Docker documentation for default build arguments. Additionally:

   • swupd_args - specifies arguments to pass to the Clear Linux* OS software manager. See the swupd man pages for more information.

Licenses

All licenses for the Clear Linux* Project and distributed software can be found at https://clearlinux.org/terms-and-policies