squid-ml 0.1.2

An easy-to-use, no boilerplate ML experiment tracker.

Squid ML

A no-boilerplate, ease-to-use AI/ML experiment tracker.

Do you find yourself spending more time setting up MLflow and the related infrastructure, compared to actually building models and data pipelines? Squid ML is here to help - log model training runs, artifacts, metrics, and more, using just 2 lines of code!

Features

1. Quickly set up the tracking infrastructure: This repo uses MLfLow for logging experiments, MinIO as an artifact store, and PostgreSQL as the backend store for MLflow.
2. Easily log experiments, runs, and artifacts: Use decorators to wrap the pipeline, which can then log the model training and model evaluation metrics. Scikit-learn, PyTorch, and TensorFlow are supported as of Feb 4, 2025.

Installation

1. Ensure that Docker and Docker Compose V2 are installed and working on your machine.
2. Use pip to install the package.

pip install squid-ml

3. Building the package from source (optional).

git clone https://github.com/ar-bansal/squid-ml.git

cd squid-ml
python -m build 
pip install dist/squid_ml-0.1.1-py3-none-any.whl

Usage

1. Start the tracking server: If you already have a tracking server set up, just call mlflow.set_tracking_uri(...) with your tracking URI.

from squid import Server

# Default project_name is the current working directory's basename
tracking_server = Server(project_name="my-project")     

tracking_server.start(
    quiet=False,                # Setting it to False will print logs on the terminal/cell
    use_current_env=True        # Automatically fetches Python and MLflow versions from your currently active environment's version for the best compatibility.
    )      

(OR)

# Python and mlflow version need to be specified the first time.
tracking_server.start(
    quiet=False,                # Setting it to False will print logs on the terminal/cell
    python_version="3.10",      # Match with your environment's version for the best compatibility
    mlflow_version="2.18.0"     # Match with your environment's version for the best compatibility
    )      

2. Use the logging decorators: While wrapping your pipeline, add *args and **kwargs as parameters. While calling the function, pass experiment_name as a keyword argument.

from squid import SklearnLogger
from sklearn.linear_model import LinearRegression


def train_model(model, X_train, y_train):
    ...

def evaluate_model(model, X_test, y_test):
    ...


# Default logging_kwargs={}
# Refer to mlflow.sklearn.autolog's documentation for more logging_kwargs
sklearn_logger = SklearnLogger(
    logging_kwargs={
        "serialization_format": mlflow.sklearn.SERIALIZATION_FORMAT_PICKLE
    }
)

@sklearn_logger.log
def run_pipeline(X_train, X_test, y_train, y_test, model, *args, **kwargs):
    # call train_model to get the model and training metrics
    # call evaluate_model to get the validation/test metrics

    # create a single dictionary with all the metrics that need to be logged

    # return model and metrics to use the decorator to log the model and metrics
    return model, metrics


def main():
    model = LinearRegression()
    X_train, X_test, y_train, y_test = ...

    model, metrics = run_pipeline(
        X_train, 
        X_test, 
        y_train, 
        y_test, 
        model, 
        experiment_name="my-experiment"
    )

Notes

By default, the following values are used as username and passwords for the PostgreSQL and MinIO containers respectively:

DB_USERNAME=dbuser
DB_PASSWORD=dbpassword

ARTIFACT_STORE_ACCESS_KEY=storeuser
ARTIFACT_STORE_SECRET_KEY=storepassword

If you'd like to use different credentials for them, simply set the environment variables using os.

import os 

os.environ["DB_USERNAME"] = mynewuser
os.environ["DB_PASSWORD"] = mynewpassword

os.environ["ARTIFACT_STORE_ACCESS_KEY"] = mynewuser
os.environ["ARTIFACT_STORE_SECRET_KEY"] = mynewpassword