Setting Environment

Before discussing into the implementation, we need to set up our working environment properly. This includes creating a virtual environment, installing dependencies, and configuring essential filees

1. Creating a Virtual Environment

Using a virtual environment ensures that dependencies remain isolated and do not interfere with other projects. To create and activate a virtual environment using Conda:

# Create a conda environment
conda create --name venv python=3.12 -y

# Activate the environment
conda activate venv

2. Installing Dependencies

Predict-Pipe relies on various machine learning, MLOps, and DevOps libraries. Install them using the requirements.txt file:

pip install -r requirements.txt

This installs all necessary packages, including:

• pandas, numpy, scikit-learn (for data processing and model training)

• MLflow (for experiment tracking)

• notebook (for Jupyter Notebook support)

• matplotlib (for data visualization)

• python-box (for structured configuration handling)

• tqdm (for progress tracking)

• ensure==1.0.2 (for type safety)

• joblib (for model serialization)

• types-pyYAML (for YAML parsing and type safety)

• Flask & Flask-Cors (for API deployment)

3. Configuring Environment Variables

Certain tools, such as MLflow and DagsHub, require authentication credentials. Set up environment variables in a .env file:

# .env file
MLFLOW_TRACKING_URI=<your-mlflow-uri>
MLFLOW_TRACKING_USERNAME=<your-username>
MLFLOW_TRACKING_PASSWORD=<your-password>

Note: here MLFLOW_TRACKING_USERNAME, MLFLOW_TRACKING_PASSWORD are dagshub username and access key

Then, load the .env file in your script for chevking using:

from dotenv import load_dotenv
import os

load_dotenv()
MLFLOW_TRACKING_URI = os.getenv("MLFLOW_TRACKING_URI")

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Now, our environment is ready, and we can start implementing the pipeline components.