ferneutron / tfserving

This repository contains code to train, export and serve a Tensorflow model with TFServing. Additionally, this repository provides the installation and configuration of TFServing through a Docker image.

Model Deployment with TFServing

This repository contains code to train, export and serve a Tensorflow model with TFServing. Additionally, this repository provides the installation and configuration of TFServing through a Docker image.

How to use it?

It is recommended to follow the step by step described below. However, you can adapt the code to your needs.

Step 1.

Build the docker image as follows:

$ docker build -t tfserving:v1 .

Step 2.

Run the container and access it through the shell.

Note: It is recommended that you mount the current directory so that you have access to the python scripts. If you want to skip mounting the current directory, you will have to modify Dockerfile to add COPY commands to move scripts from the image build.

$ docker run -it -v $PWD:/home/app/ tfserving:v1 /bin/bash

Step 3.

Once you are inside the container, generate the train.csv and test.csv files, running the generate_data.py script.

$ python -B generate_data.py

Step 4.

Train and export the model.

$ python -B train.py

Step 5.

Launch TFServing server.

$ tensorflow_model_server --port=8500 --rest_api_port=8501 --model_name=saved_model --model_base_path="/tmp"

Note: Port 8500 will be used for gRPC calls while port 8501 will be used for REST requests.

Step 6.

REST and gRPC requests.

$ python -B inference_rest.py

$ python -B inference_grpc.py