Research Playground

Inspired by ashleve/lightning-hydra-template and NVIDIA/NeMo

It's a library I created for efficient ML/DL research on various tasks.

Key features:

• 🚀 Production-ready training pipelines
• 🧠 Actual model implementations
• ⚡️ Easy configuration management with Hydra
• 📊 Experiment tracking with Weights & Biases
• 🔧 Modular architecture for quick prototyping
• 🐳 Docker support for reproducible environments

Getting Started

1. Install asdf to manage different tools' runtime versions.

2. Update .env.example to your needs.

3. Setup your training Hydra config in configs/experiments/ folder.

4. Choose between local development outside or inside Docker container.

   • Outside of Docker (not recommended):

     make init-local
     poetry shell && python3 src train --experiment <experiment_name>

   • Inside Docker:

     make init && make build && make run
     python3 src train --experiment <experiment_name>

Notes

• Use make help to see all available commands.

• Use python3 src --help to see all available CLI arguments.