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TensorFlow Serving Examples via Containers

This repository is a set of Dockerfiles and a Makefile to create the necessary artifacts and containers from the TensorFlow Serving Basic Tutorial. At some point, I'll work through the advanced tutorial as well.

Building

To create the necessary containers (including a trained model of MNIST), just simply:

make

this does assume you at least have Docker installed and an active connection to pull down dependencies.

Running Local Tests

In order to run a test on a system that uses Docker-Machine (e.g. Mac OS X), run:

run-test-docker-machine.sh

The above script will run a mnist_inference server, setup an SSH tunnel and then run the mnist_client.

Running self-contained Tests

If you want to just run the full tutorial within the container, just run make test

Additional Considerations

Notable Mentions

  • If you are using Docker Machine, you'll probably want to ensure you have a decently big VM for this build, something like: docker-machine create -d virtualbox --virtualbox-memory "10000" --virtualbox-disk-size "100000" --virtualbox-cpu-count "4" bfvm
  • See tensorflow/serving Issue 114 - which was really due to the grpc/grpc Issue 7133, I had to download the master branch of gRPC to pick up a recent bug fix.
  • Because of the above, gRPC is installed from scratch on the client Docker image
  • You can change the docker image prefix just by updating image-name.config

Types of Dockerfiles

There are four Dockerfiles in the repository.

  • Dockerfile.BUILD - sets up all build components, run the necessary bazel builds and then tar the assets so they can be copied out of the container
  • Dockerfile.EXPORT - executes mnist_export script and tar the generated model
  • Dockerfile.INFERENCE - executes mnist_inference but needs a model. The current implementation assumes artifacts/mnist_model.tar.gz exists
  • Dockerfile.CLIENT - executes mnist_client for a given server address (specified via --server) and outputs the error rate

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