yigitgenc / lesara-clv

Customer Lifetime Value Project

lesara-clv

Dockerized Lesara Customer Value Project

Installation

• Running on your computer
  • Installing
  • Running
  • Debugging
  • Logging
  • Tests

Installing

Get Docker first:

• Docker CE for Mac
• Docker CE for Windows
• Docker CE for CentOS
• Docker CE for Debian
• Docker CE for Fedora
• Docker CE for Ubuntu

And clone the repo by using git or download the master tarball to your computer:

$ cd lesara-clv/

Apply the following commands respectively.

$ docker-compose build

Building predict service may take longer than expected, please be patient. (See here)

Running

$ docker-compose up -d

To stop project; use docker-compose stop command.

Check processes by doing docker ps. You should see something like this:

CONTAINER ID        IMAGE                COMMAND                  CREATED                  STATUS              PORTS                    NAMES
083e8970d241        lesara-clv_predict   "python main.py"         Less than a second ago   Up 5 seconds                                 lesara-clv_predict_1
25907e5f28b6        lesara-clv_api       "python api.py"          Less than a second ago   Up About a minute   0.0.0.0:5000->5000/tcp   lesara-clv_api_1
fa1a95628353        redis:4.0.9-alpine   "docker-entrypoint.s…"   7 minutes ago            Up 9 minutes        6379/tcp                 lesara-clv_redis_1

Check again your docker processes for a while later. You will see the predict container disappeared (completed).

CONTAINER ID        IMAGE                COMMAND                  CREATED             STATUS              PORTS                    NAMES
25907e5f28b6        lesara-clv_api       "python api.py"          40 seconds ago      Up 2 minutes        0.0.0.0:5000->5000/tcp   lesara-clv_api_1
fa1a95628353        redis:4.0.9-alpine   "docker-entrypoint.s…"   8 minutes ago       Up 10 minutes       6379/tcp                 lesara-clv_redis_1

If you want to re-run predict service again; just type $ docker-compose up -d predict. Generated scores will be updated.

Now you are ready to access predicted scores through the API.

$ curl http://localhost:5000/api/customers/0867716461bb557156b6f22ae2ee8122

You should see something like this:

{
    "customer_id": "0867716461bb557156b6f22ae2ee8122",
    "predicted_clv": "292.951564"
}

Tests

$ docker-compose run --rm predict pytest --verbosity=3

Cheers! 🍻