Example Project Template: Serve a Scikit-learn Model via a Flask API

Example Project Template: Serve a Scikit-learn Model via a Flask API

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Example Project Template: Serve a Scikit-learn Model via a Flask API
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Just looking for the project? Grab the code here: queirozfcom/flask-sklearn-seed

This is a full template for building a simple flask-based API and server that serves a trained Scikit-learn model.

It is not meant for production, just for development purposes

  • Includes:

    • API and code-level tests
    • Logging
    • Error handling
    • CLI for training the model
    • Input validation using JSON Schema

Quickstart

  • Clone the project

    $ git clone git@github.com:queirozfcom/flask-sklearn-seed.git
Cloning into 'flask-sklearn-seed'...

  • create Python 3 virtualenv, activate virtualenv

    $ cd flask-sklearn-seed
$ virtualenv -p python3 venv3
$ source venv3/bin/activate

  • install requirements-dev

    $ pip install -r requirements-dev.txt

  • train the model using the dummy data:

    $ python -m app.models.train_model data/raw/training.csv v0

Will train model v0 using the file at: /home/felipe/flask-sklearn-seed/data/raw/training.csv 

training set has 7500 rows
validation set has 2500 rows
0.985957111012551
Successfully saved model at /home/felipe/flask-sklearn-seed/trained-models/trained-model-v0.p

  • start the server

    $ python -m app.app
* Serving Flask app "app" (lazy loading)
* Environment: production
    WARNING: Do not use the development server in a production environment.
    Use a production WSGI server instead.
* Debug mode: off
* Running on http://0.0.0.0:8080/ (Press CTRL+C to quit)

Using the app

  • Training via the CLI

    • To train a model: $ python -m app.models.train_model <path/to/training_set.csv> <version-number>

  • Tests

    • To run utils tests: $ python -m tests.utils_tests
    • To run API tests: $ python -m tests.web_tests

  • Starting the server

    $ python -m app.app
 * Running on http://0.0.0.0:8080/ (Press CTRL+C to quit)

Code Organization

This is how this project's code is structured.

Loosely based on Queirozf.com: How to Structure Software Projects: Python Examples and Cookie Cutter Data Science

.
│
├── README.md                       <----- this file
│
├── app
│   ├── app.py                      <----- main project file. contains routes and initialization code
│   │
│   ├── settings.py
│   │
│   ├── helpers                     <----- helpers contain helper code that is SPECIFIC to this application
│   │   ├── features.py                              they are placed here so as not to overly pollute the business logic
│   │   ├── files.py                                 with scaffolding code.
│   │   └── validation.py
│   │
│   ├── models                      <----- code for training models
│   │   └── train_model.py
│   │
│   └── utils                       <----- utils contain helper code that is NOT SPECIFIC to this application,
│       └── files.py                                i.e. it could be extracted and used elsewhere
│
├── data                            <----- data files, intermediate representation, if needed.
│   ├── interim
│   ├── processed
│   └── raw
│       └── training_set.csv
│
├── logs                            <----- logs folder
│   └─ application.log
│
├── notebooks                       <----- jupyter notebooks for data exploration and analyses
│   └── view-data.ipynb
│
├── requirements-dev.txt            <----- packages required to DEVELOP this project (train model, notebooks, tests, CLI commands)
├── requirements-prod.txt           <----- packages required to DEPLOY this project (only serves the API)
│
├── tests                           <----- test code
│   ├── utils_tests.py
│   └── web_tests.py
│
├── trained-models                  <------ trained models (serialized) are kept here
│   ├── trained-model-v0.p
│   ├── trained-model-v1.p
│   └── ...
│
└── venv3                           <------ python virtualenv

API Docs

Healthcheck

A simple healthcheck, to be used for monitoring (e.g. in AWS Elastic Beanstalk) a given model version.

Example: Correct Request, valid version

REQUEST
GET /v0/healthcheck
RESPONSE 200
OK

Example: Correct Request, invalid version

REQUEST
GET /v31254/healthcheck
RESPONSE 200
Not OK

Predict

Returns a prediction, calculated by a previously trained model, whose version is <version>.

Example: Correct Request

REQUEST
POST /v0/predict
{
    "id": "2",
    "x_1": -2.0,
    "x_2": -0.414120,
    "x_3": 0.2131,
    "x_4": -1.2
}
RESPONSE 200
{
    "id": "2",
    "prediction": 0.8077
}

Example: Model version not found

REQUEST
POST /v43287/predict
{
    "id": "19826478126",
    "x_1": 1.0,
    "x_2": -0.414120,
    "x_3": 0.2131,
    "x_4": -1.2
}
RESPONSE 404
{
    "message": "Trained model version 'v43287' was not found."
}

Example: Invalid request arguments

REQUEST
POST /v0/predict
{
    "id": "126",
    "x_1": 1.0,
    "x_2": -2.2
}
RESPONSE 400
{
    "message": "Missing keys: 'x_3', 'x_4'"
}

Logging

Logging is needed to keep track of how people use your app (collect usage metrics) and to help diagnose errors in case something goes wrong.

I've used an external package (concurrent-log-handler) because the default RotatingFileHandler does not support compression of old log files. This is to make sure logging itself doesn't cause problems due to lack of disk space.

Caching

There are a couple of caching mechanisms for flask (e.g. https://github.com/sh4nks/flask-caching) but, since Logistic Regression is an eager learning method (i.e. inference is quite fast because most of the work is done at training time), it didn't seem to be worth the extra complexity.

Maybe if you are using lazy methods (such as k-NN), caching would be more useful.

flask scikit-learn

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