This repo contains all the resources for my demo explaining how to use DVC along with other interesting tools & frameworks like PyCaret & FastAPI for data & model versioning, experimentation with ML models & finally deploying these models quickly for inferencing.

This demo was presented at the DVC Office Hours on 20th Jan 2022.

Note: We will use Azure Blob Storage as our remote storage for this demo. To follow along, it is advised to either create an Azure account or use a different remote for storage.

Create a virtual environment named dvc-demo & install required packages

python3 -m venv dvc-demo
source dvc-demo/bin/activate
pip install dvc[azure] pycaret fastapi uvicorn python-multipart

Initialize the repo with DVC tracking & create a data/ folder

mkdir dvc-pycaret-fastapi-demo
cd dvc-pycaret-fastapi-demo
git init
dvc init
git remote add origin https://github.com/tezansahu/dvc-pycaret-fastapi-demo.git
mkdir data

We use the Heart Failure Prediction Dataset for this demo.

First, we download the heart.csv file & retain ~800 rows from this file in the data/ folder. (We will use the file with all the rows later - this is to simulate the change/increase in data that an ML workflow sees during its lifetime)

Track this data/heart.csv using DVC

dvc add data/heart.csv
git add data/heart.csv.dvc
git commit -m "add data - phase 1"

• Go to the Azure Portal & create a Storage Account (here, we name it dvcdemo) Creating a Storage Account on Azure

• Within the storage account, create a Container (here, we name it demo20jan2022)

• Obtain the Connection String from the storage account as follows: Obtaining the Connection String for a Storage Account on Azure

• Install the Azure CLI from here & log into Azure from within the terminal using az login

Now, we store the tracked data in Azure:

dvc remote add -d storage azure://demo20jan2022/dvcstore
dvc remote modify --local storage connection_string <connection-string>
dvc push
git push origin main

Create the notebooks/ folders using mkdir notebook & download the notebooks/experimentation_with_pycaret.ipynb notebook from this repo into this notebooks/ folder.

Track this notebook with Git:

git add notebooks/
git commit -m "add ml training notebook"

Run all the cells mentioned under Phase 1 in the notebook. This involves basics of PyCaret:

• Setting up a vanilla experiment with setup()
• Comparing various classification models with compare_models()
• Evaluating the preformance a model with evaluate_model()
• Making predictions on the held-out eval data using predict_model()
• Finalizing the model by training on the full training + eval data using finalize_model()
• Saving the model pipeline using save_model()

This will create a model.pkl file in the models/ folder

Now, we track the ML model using DVC & store it in our remote storage

dvc add models/model.pkl
git add models/model.pkl.dvc
git commit -m "add model - phase 1"
dvc push
git push origin main

First, delete the .dvc/cache/ & models/model.pkl (simulate production env). Then, pull the changes from the DVC remote storage.

dvc pull

Check that the model.pkl file is now present in models/ folder.

Now, create a server/ folder & place the main.py file in it after downloaidng the server/main.py file from this repo. This RESTful API server has 2 POST endpoints:

• Inferencing on an individual record
• Batch inferencing on a CSV file

We commit this to our repo:

git add server/
git commit -m "create basic fastapi server"

Now, we can run our local server on port 8000

cd server
uvicorn main:app --port=8000

Go to http://localhost:8000/docs & play with the endpoints present in the interactive documentation.

Swagger Interactive API Documentation for our Server

For the individual inference, you could use teh following data:

{
 "Age": 61,
 "Sex": "M",
 "ChestPainType": "ASY",
 "RestingBP": 148,
 "Cholesterol": 203,
 "FastingBS": 0,
 "RestingECG": "Normal",
 "MaxHR": 161,
 "ExerciseAngina": "N",
 "Oldpeak": 0,
 "ST_Slope": "Up"
}

Now, we use the full heart.csv file to simulate the arrival of new data with time. We place it within data/ folder & upload it to DVC remote.

dvc add data/heart.csv
git add data/heart.csv.dvc
git commit -m "add data - phase 2"
dvc push
git push origin main

Now, we run the experiment in Phase 2 of the notebooks/experimentation_with_pycaret.ipynb notebook. This involves:

• Feature engineering while setting up teh experient
• Fine-tuning of models with tune_model()
• Creating an ensemble of models with blend_models()

The blended model is saved as models/modl.pkl

We upload it to our DVC remote.

dvc add models/model.pkl
git add models/model.pkl.dvc
git commit -m "add model - phase 2"
dvc push
git push origin main

Now, we again start the server (no code changes required, because the model file has same name) & perform inference.

cd server
uvicorn main:app --port=8000

With this, we demonstrate how DVC can be used in conjunction with PyCaret & FastAPI for iterating & experimenting efficiently with ML models & deploying them with minimal effort.

• Fundamentals of MLOps: A 4-blog series
  • A Gentle Introduction to MLOps
  • Data & Model Management with DVC
  • ML Experimentation using PyCaret
  • Tracking with MLFlow & Deployment with FastAPI
• DVC Documentation
• PyCaret Documentation
• FastAPI Documentation

Created with ❤️ by Tezan Sahu