ML-in-containers: A Jupyter Notebook repository from opeyemibami

Machine learning and Tensorflow app development in containers

This project utilizes docker for machine learning experimentation, research and deployment as microservice. You can use docker swarm or kubernetes to orchestrate the sercives declared in the docker compose file

Model architecture

Steps to get the app running:

clone the repository git clone https://github.com/opeyemibami/ML-in-containers.git
cd ML-in-containers
install docker on you host machine if you don't have it install docker installation

Walk through the notebook

To start the notebook, run this command in your terminal

docker-compose up tensorflow
copy the web url and paste in any browser to access the notebook

Build custom tensorflow serving docker image

run this command in your terminal

docker-compose build tf_model_serving

After custom image has been built, with docker swarm, we can start up the services listed in the docker compose file using the commands below in terminal:

docker swarm init
docker stack deploy -c docker-compose.yml tf

With this, docker will create a virtual network which allows all containers to communicate with each other by name.

To check the logs each containers, we can run the command below which is a general format of docker service logs

Tensorflow serving: docker service logs tf_tf_model_serving
Jupyter notebook: docker service logs tf_tensorflow

With the jupyter logs, you can copy the web url to access the serving notebook and run the predict function to send requests to the server.

opeyemibami/ML-in-containers