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Convenient all-in-one technology stack for deep learning prototyping - allows you to rapidly iterate over new models, datasets and tasks on different hardware accelerators like CPUs, multi-GPUs or TPUs. A collection of best practices for efficient workflow and reproducibility. Thoroughly commented - you can use this repo as a reference and educational resource. Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that depend on each other. PyTorch Lightning, a lightweight PyTorch wrapper for high-performance AI research. Think of it as a framework for organizing your PyTorch code. Hydra, a framework for elegantly configuring complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line.
Features
- Predefined Structure is clean and scalable
- Generic, easy-to-adapt tests for speeding up the development
- Automatically test your repo with Github Actions
- All logs (checkpoints, configs, etc.) are stored in a dynamically generated folder structure
- Comes down to 4 simple steps
- Override chosen hyperparameters
Project Samples
