Census Model Deployment to Heroku Using FastAPI

In this project, a simple census dataset is used to create an inference pipeline, which is trained and deployed to Heroku and AWS ECS using FastAPI. The dataset consists of 32,561 entries of different people, each with 14 features (age, education, etc.) and the model infers the salary range of an entry. You can try the API here (you might need to wait a bit the first time until the app awakens):

https://census-salary-model.herokuapp.com

I forked the starter code for this project from a Udacity exercise/demo repository and modified it to the present form, which deviates significantly from the original form.

The focus of this project doesn't lie so much on the data processing, but on the techniques and technologies used for model/pipeline deployment. A list of the most important MLOps methods and tools used is the following:

This project serves also as a blueprint for similar inference pipelines that need to be deployed using CI and CD techniques. Therefore, implementation details have been carefully collected.

Dataset

The dataset was obtained from the this Udacity repository.

As specified in the config.yaml configuration file, we have 14 features, which are divided in numerical and categorical:

numerical:
- "age"
- "fnlgt"
- "education_num"
- "capital_gain"
- "capital_loss"
- "hours_per_week"
categorical:
- "workclass"
- "education"
- "marital_status"
- "occupation"
- "relationship"
- "race"
- "sex"
- "native_country"

The target variable is the salary value, which is binary: <=50K or >50K.

How to Use This Project

The directory of the project consists of the following files:

.
├── .slugignore                         # Heroku app ignore file
├── .flake8                             # Flake8 linting ignore file
├── .dockerignore                       # Docker ignore file
├── .github/                            # CI workflows (Github Actions)
├── Dockerfile                          # Docker image file
├── Instructions.md                     # Original Udacity instructions
├── ModelCard.md                        # MODEL CARD
├── Procfile                            # Heroku launch command
├── README.md                           # This file
├── api                                 # FastAPI definition
│   ├── __init__.py
│   ├── README.md                       # Explanation of the API structure
│   ├── app.py
│   └── schemas.py
├── assets/                             # Images & related
├── census_notebook.ipynb               # Main research notebook
├── census_salary                       # Package which contains the ML library
│   ├── __init__.py
│   ├── README.md                       # Explanation of the library structure
│   ├── census_library.py
│   ├── core
│   │   ├── __init__.py
│   │   ├── config_example.yaml
│   │   └── core.py
│   ├── data
│   │   ├── __init__.py
│   │   └── dataset.py
│   └── ml
│       ├── __init__.py
│       ├── data_processing.py
│       └── model.py
├── conda.yaml                          # Conda research and development environment
├── config.yaml                         # Project configuration file
├── data                                # Original dataset
│   └── census.csv
├── docker-compose.yaml                 # Docker compose YAML script
├── exported_artifacts
│   ├── evaluation_report.txt           # Model evaluation with the test split
│   ├── model.pickle                    # Model artifact
│   ├── processing_parameters.pickle    # Data processing pipeline artifact
│   └── slice_output.txt                # Data slicing results
├── live_api_example.py                 # Script which connects to the deployed API
├── logs                                # Logs (of the library/package)
│   └── census_pipeline.log
├── main.py                             # Library usage example file
├── push_to_ecr.sh                      # Script to build image and push to AWS ECR
├── push_to_heroku.sh                   # Script to build image and push to Heroku registry
├── requirements.txt                    # API app dependencies (for CI and CD)
├── run.sh                              # Python version for Heroku
├── runtime.txt                         # Run command for docker container
├── screenshots                         # Final API screenshots (Heroku)
│   ├── continuous_deployment.png
│   ├── continuous_integration.png
│   ├── example.png
│   ├── live_doc.png
│   ├── live_get.png
│   └── live_post.png
├── setup.py                            # Python package setup for census_salary
├── starter/                            # Original starter code from Udacity
└── tests                               # Tests: for the library/package and the API app
    ├── __init__.py
    ├── README.md                       # Explanation of the test folder structure
    ├── conftest.py
    ├── test_api.py
    └── test_census_library.py

The project contains the following central files or components:

The research notebook census_notebook.ipynb , in which the dataset data/census.csv is explored and modeled.
The research code is packaged into a production library in census_salary; the library has a README.md file which explains its structure, if you're interested. The package reads the config.yaml configuration file and is able to train the inference pipeline, producing the corresponding artifacts to exported_artifacts. Then, we can load these and perform inference on new data; an usage example is provided in main.py.
That package is used in the FastAPI app developed in api.
Both packages (the inference library and the API) are tested in tests using Pytest.

Installing Dependencies for Custom (Local) Environments

If you'd like to run the project locally, you need to create a custom environment and install the required dependencies. A quick recipe which sets everything up with conda is the following:

# Create environment with YAML, incl. packages
conda env create -f conda.yaml
conda activate census

Note that the requirements.txt file contains the minimum necessary packages for deployment, whereas conda.yaml is for setting up the local development environment (e.g., jupyter notebooks is included).

Running the Packages Locally

As introduced in this section, we have 4 central files or components:

The notebook: census_notebook.ipynb .
The census model library: census_salary.
The API: api.
The tests (for 2 and 3): tests.

In the following instruction to run those components locally are provided; it is assumed that the appropriate environment was installed as explained in the dedicated subsection.

The Notebook

We can open and run the research notebook in at least two ways:

In created custom environment:

conda activate census
jupyter lab census_notebook.ipynb

In Google Colab. For that, simply click on the following link:

In any case, the notebook performs the complete data exploration and modeling which is transferred to the library. Additionally, extra research work and tests are carried out in it.

The Model Library

The file main.py contains the most important usage commands from the census_salary package/library. We can run it with

python main.py

This will produce:

Inference artifacts in exported_artifacts: the trained model and the data processing pipeline, as well as all evaluation reports (general and data slicing).
Logs in logs/census_pipeline.log.

The API

After the inference artifacts have been created (e.g., executing main.py), we can start the FastAPI app implemented in api using the Uvicorn web server:

uvicorn api.app:app --reload

This will spin up a REST API on http://127.0.0.1:8000, which we can access with the browser. If we open that URL, we'll get the welcome page with a link to the documentation. The documentation page contains all defined endpoints and we can test them from there:

http://127.0.0.1:8000: GET, index/welcome page delivered.
http://127.0.0.1:8000/health: GET, JSON returned with API and model version.
http://127.0.0.1:8000/predict: POST, we provide a JSON with features and an inference JSON is returned.
http://127.0.0.1:8000/docs: default documentation interface.

One of the advantages of FastAPI is that it creates the documentation interface automatically.

Once the API is running, we can interact with it using any tool, such as curl or requests:

import requests
import json

# Input JSON/dict with features
# Feature names and values as after data processing:
# _ instead of -, no blank spaces
d = {
    "inputs": [
        {
            "age": 39,
            "workclass": "State-gov",
            "fnlgt": 77516,
            "education": "Bachelors",
            "education_num": 13,
            "marital_status": "Never-married",
            "occupation": "Adm-clerical",
            "relationship": "Not-in-family",
            "race": "White",
            "sex": "Male",
            "capital_gain": 2174,
            "capital_loss": 0,
            "hours_per_week": 40,
            "native_country": "United-States"
        }
    ]
}

# POST input JSON to API and wait or response
r = requests.post("http://127.0.0.1:8000/predict", data=json.dumps(d))
print(r.status_code) # 200
print(r.json())
# {'model_lib_version': '0.0.1', 'timestamp': '2023-01-23 17:07:10.403670', 'predictions': ['<=50K']}
print(r.json()['predictions']) # ['<=50K']

The Tests

Tests are implemented in tests; in that folder, conftest.py defines the pytest fixtures and the rest of the files contain all the test cases for the library and the API. To run the tests:

pytest tests

The Github Action/Workflow python-app.yml explained in the Continuous Integration section below executes that command every time we push to the Github repository.

Deploying the Project: Running the Packages on the Cloud

The goal of such an API is to be available to several services; if we spin up the API as explained in The API and our computer IP is accessible to other machines in our intranet, we would be done at the moment. However, the goal is often to publish the API to the internet. To that end, two possible deployments are discussed in dedicated sections:

Continuous Deployment to Heroku
Deployment to AWS ECS

In any case, the API interaction remains the same for the user and all instructions in The API section hold, except the URL needs to be changed. The script live_api_example.py shows how we would make the calls; if the API deployed on Heroku is up, we can run that script and get results:

python live_api_example.py

To spin up the remote API, we need to wake it by opening the URL in the browser; the dyno goes to sleep after 30 minutes of inactivity:

https://census-salary-model.herokuapp.com

More Implementation Details

This section summarizes details beyond the usage of the project. The goal is to provide enough background to re-use the structure of this repository for similar ML applications.

Model Card

The model card, which contains a summary of the model properties, is in the co-located file ModelCard.md.

In there, the following topics are covered:

Dataset
Model: training and final performance
Evaluation metrics, data slicing
Bias and ethical issues (morel fairness)

FastAPI Application: API

The API is defined in api, where there is a README.md with endpoint usage examples.

If you're interested in more details on how to build such APIs, check my notes in MLOpsND_Deployment.md.

Census Model Library

The census modeling is implemented in an isolated package for modular and easier use. In the package folder census_salary, there is a README.md file which explains the package structure and provides with usage exanples.

Testing with Pytest

Both the census library and the API are tested using Pytest. The folder tests contains the implementation modules and a README.md with further explanations.

Continuous Integration with Github Actions

The project uses Github Actions from Github to implement Continuous Integration. In summary, every time we push a new version to Github (or perform a pull request), the package is checked in a custom container running on Github servers:

Dependencies are installed, including the package census_salary.
Linting is performed on the code using flake8.
pytest is run on the project.

To create the CI Github Action, we simply choose the Python Application pre-configured workflow on the Github web interface:

Github repository > Actions > New Workflow > Python application: configure

The python-app.yml workflow YAML file needs to be modified, as well as other files in the repository. Here's a list of things to pay attention to:

Since we have a package, we need to install it in the container (see the YAML below).
Check which Python version we are using.
Add missing/required packages to requirements.txt: flake8, pytest, pyyaml, setuptools, etc.
Add a .flake8 file to the repository to configure flake8, i.e., which folders/files we want to exclude form linting.
If the tests are in another folder than test, modify the pytest command.
If the tests trigger logging, create the logging file in the container (see YAML).
Make sure all commands defined in the YAML work on our local environment.

Every time we push, the the build job will be run on the Github servers (see YAML); we can check the logs by clicking on the Github web interface:

Github repository > Actions > All workflows: Python application >
    Select desired run > Jobs: build

The final python-app.yml YAML is stored and committed to .github/workflows, and its content is the following:

# This workflow will install Python dependencies, run tests and lint with a single version of Python
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python

name: Python application

on:
  push:
    branches: [ "master" ]
  pull_request:
    branches: [ "master" ]

permissions:
  contents: read

jobs:
  build:

    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v3
    - name: Set up Python 3.10
      uses: actions/setup-python@v3
      with:
        python-version: "3.10"
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install flake8 pytest
        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
        pip install .
    - name: Lint with flake8
      run: |
        # stop the build if there are Python syntax errors or undefined names
        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
    - name: Test with pytest
      run: |
        # Create a logs file
        #mkdir logs
        #touch logs/census_pipeline.log
        # Test
        pytest tests

Continuous Deployment to Heroku

The app deployed on Heroku can be accessed at

https://census-salary-model.herokuapp.com

Heroku is a cloud platform which enables several deployment methods; here two are implemented:

Github connection with continuous deployment.
Container registry with Heroku CLI.

The Github connection method is the easiest one: we create the app, e.g., using the Heroku web interface, and connect our Github repository to it. Then, we need to check two options in the automatic deploys tab:

Wait for CI to pass before deploy: with this option, the tests defined in python-app.yml will need to pass.
Enable Automatic Deploys: when the tests pass, the app will be deployed and accessible on Heroku.

With that, we have an app which follows the CI/CD methodology!

Note: Since the Heroku app (aka. slug) is limited in terms or memory and disk space, we can specify in the file .sligignore which files don't need to be deployed (e.g., notebooks, datasets, etc.).

The Container registry method is more complex but allows for more flexibility. However, continuous deployment is not as straightforward. To use it we need to:

Select the Container registry method when creating the app on Heroku.
Create a docker image, as explained in the section Docker Container.
Push the docker image to Heroku using the Heroku CLI and release the app. For this last step, the required commands are summarized in push_to_heroku.sh, which can be run if all the requirements are met (i.e., docker engine and Heroku CLI installed):
```
$ heroku login
$ ./push_to_heroku.sh
```

Note: The docker build commands in push_to_heroku.sh are optimized for Mac computers with an Apple silicon (M1/M2).

For more information on how to deploy to Heroku, check my notes in MLOpsND_Deployment.md

Data and Model Versioning

The current version of this project does not use any data or model versioning. The dataset is included in the repository and the inference artifacts are not uploaded anywhere. However, in a professional setting and with increasingly larger artifacts, that is not viable. Two easy options to apply versioning to any kind of artifacts are the following:

DVC: We can define remote storage folders (e.g., in GDrive, S3, etc.) and upload large datasets to them while ignoring them in git. DVC requires a buildpack for Heroku, as explained in the starter folder. Check my notes on DVC for more information.
Weights and Biases: we can create model and dataset artifacts and upload/download them from the W&B servers. An example of mine where W&B functionalities are used extensible is the following: music_genre_classification.

Docker Container

The app docker image is defined in Dockerfile; additionally, we need to consider the .dockerignore file, which is the equivalent to [.gitignore], but when building images. The reason is because image sizes increase fast, and cloud space can become expensive.

To build the image and and run the container, we can follow these commands:

# Build the Dockerfile to create the image
# docker build -t <image_name[:version]> <path/to/Dockerfile>
docker build -t census_model_api:latest .
 
# Check the image is there: watch the size (e.g., ~1GB)
docker image ls

# Run the container locally from a built image
# Recall to: forward ports (-p) and pass PORT env variable (-e), because run.sh expects it!
# Optional: 
# -d to detach/get the shell back,
# --name if we want to choose conatiner name (else, one randomly chosen)
# --rm: automatically remove container after finishing (irrelevant in our case, but...)
docker run -d --rm -p 8001:8001 -e PORT=8001 --name census_model_app census_model_api:latest

# Check the API locally: open the browser
#   http://localhost:8001
#   Use the web API
 
# Check the running containers: check the name/id of our container,
# e.g., census_model_app
docker container ls
docker ps

# Get a terminal into the container: in general, BAD practice
# docker exec -it <id|name> sh
docker exec -it census_model_app sh
# (we get inside)
cd /opt/census_model
ls
cat logs/census_pipeline.log
exit

# Stop container and remove it (erase all files in it, etc.)
# docker stop <id/name>
# docker rm <id/name>
docker stop census_model_app
docker rm census_model_app

We can document and automate the build and run process using docker-compose; to that end, we need to define the docker-compose.yaml file and run it as follows:

# Run contaner(s), detached; local docker-compose.yaml is used
docker-compose up -d

# Check containers, logs
docker-compose ps
docker-compose logs

# Stop containers
docker-compose down

Note: The deployment to Heroku pr AWS ECR is automated in the files push_to_heroku.sh and push_to_ecr.sh, respectively. Those build the images in a slightly different manner, targeting the Mac architecture and the cloud platform.

For more information on how to dockerize an application, check my notes in MLOpsND_Deployment.md.

Deployment to AWS ECS

The deployment of a dockerized application to AWS ECR and using Fargate is essentially not complex, but requires to cover many details; therefore, I suggest checking my notes in MLOpsND_Deployment.md.

In summary, we need to:

Create an AWS account and install the AWS CLI.
Create a dockerized application, as explained in Docker Container.
Configure the app and its components in AWS.

Run the commands specified in push_to_ecr.sh:

$ aws configure # enter the access key and the secret/pass
# chmod a+x push_to_ecr.sh
$ ./push_to_ecr.sh

Results and Conclusions

In this project, a simple census dataset is used to create an inference pipeline, which is trained and deployed in the form of a FastAPI app. The dataset consists of 32,561 entries of different people, each with 14 features (age, education, etc.) and the model infers the salary range of an entry.

The focus of this project doesn't lie so much on the data processing, but on the techniques and technologies used for model/pipeline deployment, which are listed in the introduction.

All in all, the repository achieves two goals:

Show how an ML pipeline can be deployed with modern tools.
Collect all necessary background information to use this project as a template for similar deployments.

In the following, some possible improvements are outlined and related links are provided.

Next Steps, Improvements

Implement Docker containerization.
Implement authentication.
Create a simple front-end for the API (e.g., a form).
Use DVC: Data and model version control. A similar repository where this is done: theyorubayesian/cliffhanger.
Deployment to AWS ECS using the Docker image.
Implement experiment tracking, e.g., with Weights and Biases.
Use Pydantic alias_generator to parse field names as desired; see: parsing field names.

Interesting Links

My notes:

My guide on CI/DC: cicd_guide
My boilerplate for reproducible ML pipelines using MLflow and Weights & Biases: music_genre_classification.
My personal notes on the Udacity MLOps nanodegree: mlops_udacity; example and exercise repository related to this project: mlops-udacity-deployment-demos.
A very simple Heroku deployment with the Iris dataset and using Flask as API engine.
Notes on how to transform research code into production-level packages: customer_churn_production.
My summary of data processing and modeling techniques: eda_fe_summary.
My notes on the Udemy course Deployment of Machine Learning Models by Soledad Galli & Christopher Samiullah: deploying-machine-learning-models.

Authorship

Mikel Sagardia, 2023.
No guarantees.

If you find this repository useful, you're free to use it, but please link back to the original source.

Note that this repository was originally forked from a Udacity exercise/demo; as such, I kept the original LICENSE.txt. However, the modifications done to the project are significant. I would say that basically the starter folder and the dataset are the only things that I have kept unchanged.

Name		Name	Last commit message	Last commit date
Latest commit History 233 Commits
.github/workflows		.github/workflows
api		api
assets		assets
census_salary		census_salary
data		data
exported_artifacts		exported_artifacts
logs		logs
screenshots		screenshots
starter		starter
tests		tests
.dockerignore		.dockerignore
.flake8		.flake8
.gitignore		.gitignore
.slugignore		.slugignore
CODEOWNERS		CODEOWNERS
Dockerfile		Dockerfile
Instructions.md		Instructions.md
LICENSE.txt		LICENSE.txt
ModelCard.md		ModelCard.md
Procfile		Procfile
README.md		README.md
census_notebook.ipynb		census_notebook.ipynb
conda.yaml		conda.yaml
config.yaml		config.yaml
docker-compose.yaml		docker-compose.yaml
live_api_example.py		live_api_example.py
main.py		main.py
push_to_ecr.sh		push_to_ecr.sh
push_to_heroku.sh		push_to_heroku.sh
requirements.txt		requirements.txt
run.sh		run.sh
runtime.txt		runtime.txt
setup.py		setup.py

License

mxagar/census_model_deployment_fastapi

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