Modern ML Cookiecutter

🚀 A modality-aware, end-to-end template for modern machine learning projects covering NLP, Speech, and Vision with best-in-class models and researcher-friendly configuration.

✨ Features

• 🎯 Multi-Modal Support: Choose from NLP (DistilBERT), Speech (Whisper), or Vision (ViT) with optimized configurations
• ⚡ Fast Dependency Management: Uses uv for lightning-fast package management
• 🧠 ML-Centric Configuration: Researcher-friendly parameter names (epochs not num_train_epochs)
• 🖥️ Mac MPS Support: Optimized for Apple Silicon with Metal Performance Shaders
• ☁️ Local & Cloud Training: Seamless training with Hugging Face Accelerate locally or SkyPilot in the cloud
• 🚀 Production-Ready Serving: High-performance model serving with LitServe
• 📊 Experiment Tracking: Optional integration with tracelet
• 🔧 Type-Safe Configuration: Pydantic-based settings with modality-aware validation

🎯 Supported Modalities

Modality	Task	Model	Dataset	Key Libraries
NLP	Text Classification	DistilBERT	IMDB	transformers, datasets
Speech	ASR (Speech-to-Text)	Whisper	Common Voice	transformers, librosa, torchaudio
Speech	TTS (Text-to-Speech)	CSM (Sesame)	Conversational	transformers, CSM
Vision	Image Classification	Vision Transformer	CIFAR-10	torchvision, PIL, opencv

🚀 Quick Start

Generate a new project with a single command:

# Using cruft (recommended for template updates)
uvx cruft create https://github.com/prassanna-ravishankar/cookiecutter-modern-ml

# Using cookiecutter directly
cookiecutter https://github.com/prassanna-ravishankar/cookiecutter-modern-ml

Choose your modality (NLP, Speech, Vision) and start training with uv run task train.

🔧 What’s Included

Pre-configured Tools

• uv: Ultra-fast Python package management
• Transformers: State-of-the-art models for all modalities
• Accelerate: Multi-device training (CUDA/MPS/CPU)
• LitServe: High-performance model serving
• Polars: Fast data processing (not pandas)
• Pydantic: Type-safe configuration management
• Ruff: Fast Python linter and formatter
• Pytest: Testing framework

Design Philosophy

• Simplicity over Features: Avoid over-engineering, focus on researcher needs
• ML-Centric: Parameter names and structure match ML research conventions
• Modality-Aware: Each domain (NLP/Speech/Vision) has optimized defaults
• Modern Tooling: Latest best practices (uv, Polars, LitServe, Pydantic)
• Mac-First: Optimized for Apple Silicon development

The template automatically optimizes for your hardware (Mac MPS, CUDA, or CPU) and includes cloud training support with SkyPilot for easy scaling to production workloads.