Iris API

This is an HTTP service that provides functions to detect Iris target from lists. It includes:

Iris Predictor

For understanding the APIs and resources please refer to the folder resources or the explanation here

Folders

bin: executable file creating the necessary folders and copying models before building the docker
models: sklearn KNN model
resources: resources for different APIs

Docker

In this part we explain how to build and run the docker.

Build a Docker container

sudo bin/docker_build_context.sh
sudo docker build --tag=iris_api:0.0.1 build/docker

Run Docker container

Just CPU:

sudo docker run -d  -p 8000:80 iris_api:0.0.1

After starting the container the service should listen on 127.0.0.1 port 8000.

The number of Gunicorn workers can be configured by setting the NUM_WORKER environment variable when running the container, e.g. -e NUM_WORKER=2.

Start service manually

For debugging it can be helpful to start the service manually. Run the container but overwrite the entrypoint with a Bash shell (you need to modify the version manually instead of 0.0.1):

docker run -it -p 8000:80 --entrypoint=/bin/bash iris_api:0.0.1

This starts the container and opens a shell but does not start the service. Start the service manually:

cd /app
gunicorn --workers 1 --worker-class gevent --bind 0.0.0.0:80 main:app

Login to the Docker container

Lookup container ID:

    docker container ps
    CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS              PORTS                  NAMES
    7932a3814453        friendlyhello       "python app.py"     16 seconds ago      Up 15 seconds       0.0.0.0:4000->80/tcp   musing_robinson

Open a shell on the container:

docker exec -it c1de50a17e8d /bin/bash

Cleanup

Remove all containers and images:

sudo docker rm $(sudo docker ps -a -q)
sudo docker rmi $(sudo docker images -q)

Gunicorn

The Gunicorn configuration is described in Gunicorn settings.

The most important Gunicorn configuration parameters are:

--reload - Restart workers when code changes. This should only be used during development
--workers - The number of worker processes for handling requests
--worker-class - The type of workers to use
--bind - The socket and port to bind
--access-logfile - Path of the access log file
--error-logfile - Path of the error log file
--daemon - Daemonize the Gunicorn process

aliechoes/iris-api