KladML

Build ML pipelines with pluggable backends. Simple. Modular. Yours.

What is KladML?

KladML is a lightweight, modular SDK for building production-ready machine learning pipelines. Unlike heavy MLOps platforms, KladML gives you:

Interface-based architecture - Swap backends without changing code
Local-first - No servers required, works offline with SQLite
Framework-agnostic - Works with PyTorch, TensorFlow, scikit-learn, or any ML library
CLI included - Initialize projects, run experiments from terminal

Quick Install

pip install kladml

Quick Start

# Create a project
kladml init my-project
cd my-project

# Run training
kladml run native train.py

Why KladML?

Feature	KladML	MLflow	ClearML
Interface-based	✅ Pluggable	❌ Hardcoded	❌ Hardcoded
Server required	❌ No	⚠️ Optional	✅ Yes
Local-first	✅ SQLite default	✅ Yes	❌ No
Learning curve	🟢 Minutes	🟡 Days	🔴 Weeks
Custom backends	✅ Easy	⚠️ Complex	❌ No

Documentation

🚀 Getting Started — Install, configure, and run your first experiment
🧠 Core Concepts — Understand interfaces, runners, and the architecture
🏗️ Architecture — Deep dive into model contracts and design patterns
📦 CLI Reference — All available commands and options

Example

from kladml import TimeSeriesModel, ExperimentRunner

class MyForecaster(TimeSeriesModel):
    def train(self, X_train, y_train=None, **kwargs):
        # Your training logic
        return {"loss": 0.1}

    def predict(self, X, **kwargs):
        return self.model.predict(X)

    def evaluate(self, X_test, y_test=None, **kwargs):
        return {"mae": 0.5}

    def save(self, path: str):
        # Save model
        pass

    def load(self, path: str):
        # Load model
        pass

# Run with tracking
runner = ExperimentRunner()
runner.run(model_class=MyForecaster, train_data=data)