mlpotion 1.2.0

Atomic Steps for ML Pipelines, framework agnostic, modular

MLPotion: Brew Your ML Magic! 🧪✨

Modular Components for Machine Learning Pipelines

Provided and maintained by 🦄 UnicoLab

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Welcome, fellow alchemist! 🧙‍♂️ Ready to brew some machine learning magic without getting locked in a cauldron?

MLPotion is your chest of modular, mix-and-match ML building blocks that work across Keras, TensorFlow, and PyTorch. Think of it as LEGO® for ML pipelines, but with fewer foot injuries and more flexibility!

Why MLPotion? 🤔

Ever felt trapped by a framework that forces you to do things "their way"? We've been there. That's why we created MLPotion:

• 🎯 Framework Agnostic: Write once, run anywhere (well, on Keras, TensorFlow, or PyTorch).
• 🧱 Modular by Design: Pick the pieces you need, leave the rest in the box.
• 🔬 Type-Safe: Python 3.10+ typing that actually helps you (mypy approved!).
• 🚀 Production Ready: Built for the real world, not just notebooks.
• 🎨 Orchestration Flexible: Works standalone OR with ZenML, Prefect, Airflow - your choice!
• 📦 Install What You Need: Core package works without any ML frameworks (you only install what you need)!
• 🤝 Community-Driven: Missing something? Contribute it back - we love community additions!

What's in the Potion? 🧪

⚗️ Core Ingredients

• Type-safe protocols for all components
• Framework-agnostic result types
• Consistent error handling
• Zero-dependency core package

🔧 Framework Support

• Keras 3.0+ - The friendly one
• TensorFlow 2.15+ - The production workhorse
• PyTorch 2.0+ - The researcher's favorite

📊 Data Processing

• CSV loaders for all frameworks
• Dataset optimization utilities
• Data transformers
• Preprocessing pipelines

🎓 Training & Evaluation

• Unified training interface
• Comprehensive evaluation tools
• Rich result objects
• Training history tracking

💾 Model Management

• Save/load model checkpoints
• Export to production formats
• Model inspection utilities
• Multiple export formats

🔄 Orchestration Integration

• ZenML integration built-in
• Extensible to Prefect, Airflow, etc.
• Works standalone (no orchestration needed!)

The MLPotion Philosophy 🎭

"A good potion doesn't force you to drink it a certain way. It just... works."

— Ancient ML Alchemist Proverb (we just made that up)

We believe in:

1. Flexibility > Convention: Your project, your rules
2. Simplicity > Complexity: If it's hard to use, we failed
3. Type Safety > Runtime Surprises: Catch errors before they bite
4. Modularity > Monoliths: Use what you need, ignore the rest
5. Consistency > Chaos: Same patterns across all frameworks
6. Community > Corporate: Built by the community, for the community

📦 Installation

Install only what you need to keep your environment clean.

Core (Base Protocols)

Perfect for defining custom implementations or lightweight usage.

poetry add mlpotion

For Framework Users

# TensorFlow / Keras
poetry add mlpotion -E tensorflow

# PyTorch
poetry add mlpotion -E pytorch

For MLOps (ZenML)

Combine your framework with ZenML capabilities.

poetry add mlpotion -E tensorflow -E zenml
# OR
poetry add mlpotion -E pytorch -E zenml

The Full Lab

poetry add mlpotion -E all

⚡ Quickstart

Here is a simple example of defining a training configuration and running a trainer using Keras components.

from mlpotion.frameworks.keras.training import ModelTrainer
from mlpotion.frameworks.keras.config import ModelTrainingConfig

# 1. Define your configuration 📝
# Strongly typed configs ensure you never miss a parameter
config = ModelTrainingConfig(
    epochs=10,
    batch_size=32,
    optimizer="adam",
    loss="sparse_categorical_crossentropy"
)

# 2. Initialize the Trainer 🧪
trainer = ModelTrainer(config=config)

# 3. Run the pipeline 🚀
# (Assuming 'my_model' and datasets are ready)
history = trainer.train(
    model=my_model,
    train_data=train_ds,
    val_data=val_ds
)

print(f"✅ Training complete! Final accuracy: {history.history['accuracy'][-1]:.4f}")

ZenML User? Drop this logic directly into a pipeline step:

from mlpotion.integrations.zenml.tensorflow.steps import train_model

Feature Comparison 📊

Feature	MLPotion	Framework-Only	All-in-One Solutions
Multi-framework	✅ Yes	❌ No	⚠️ Limited
Type Safety	✅ Full	⚠️ Partial	⚠️ Partial
Modular Install	✅ Yes	❌ No	❌ No
ZenML Native	✅ Yes	❌ Manual	⚠️ Adapters
Learning Curve	📈 Gentle	📈 Framework-specific	📈 Steep
Production Ready	✅ Yes	⚠️ DIY	✅ Yes
Flexibility	🌟🌟🌟🌟🌟	🌟🌟🌟🌟🌟	🌟🌟

Who's This For? 🎯

You'll love MLPotion if you:

• Switch between frameworks and hate rewriting everything
• Value heavily tested code that you can reuse
• Value type safety and IDE autocomplete (who doesn't?)
• Want production-ready code without enterprise bloat
• Believe ML pipelines should be composable and testable

You might want something else if you:

• Do not like modularity
• Do not like reusability
• Are too lazy to contribute something that you can't already find here

Community & Support 🤝

• GitHub: Star, fork, contribute!
• Issues: Report bugs, request features.
• UnicoLab: Enterprise AI solutions.

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Ready to brew some ML magic? Let's get started! 🧪✨
Built with ❤️ for the ML community by 🦄 UnicoLab