IBM AI Enterprise Workflow - Capstone Project

Overview of Contents

• app.py: flask app to predict, train, and access logfiles
• data: directory containing data files
• Dockerfile: commands to build the Docker image
• logs: directory for storing training and prediction logs
• models: directory for storing models
• notebooks: contains notebook for exploratory data analysis and visualizations
• requirements.txt: file containing the packages used in this repo
• rununittests.py: python script to run unit tests
• src: directory containing python scripts for training and prediction
• templates: simple templates for rendering flask app
• unittests: directory containing scripts for unit tests

Testing in Python

To test the flask app:

~$ python app.py

Go to http://0.0.0.0:8080 to see a basic website for this project.

To run the all of the unit tests, including model, logging, and API tests:

~$ python rununittests.py

To run only the model tests:

~$ python unittests/ModelTests.py

To run only the logging tests:

~$ python unittests/LoggerTests.py

To run only the API tests:

~$ python unittests/ApiTests.py

To test the training and prediction of the models:

~$ python src/model.py

Testing in Docker

To build the Docker container:

~$ sudo docker build -t capstone .

To verify that the image is there:

~$ sudo docker image ls

To run the container:

~$ sudo docker run -p 4000:8080 capstone

Go to http://0.0.0.0:4000/ to verify that the app is running.

To quit, press CTRL+C.

To verify the container is there:

~$ sudo docker ps -a

To remove the container using the container name or id from above step:

~$ sudo docker rm [container_name_or_id]

To remove the image:

~$ sudo docker image rm capstone