#PyTorch Lightning
pytorch-lightning
This framework facilitates AI model workflows by providing streamlined pretraining, finetuning, and deployment for flexible and scalable use, especially among PyTorch users. It integrates smoothly with LitServe for model serving, ensuring efficient workflow management. Designed with practicality in mind, it effortlessly handles classification, segmentation, and summarization tasks. Supported by comprehensive documentation and community-driven examples, it serves as a vital resource for various deep learning projects.
terratorch
TerraTorch enhances geospatial data modeling by utilizing pre-trained model backbones like Prithvi, SatMAE, and ScaleMAE, supporting tasks including segmentation, classification, and regression. Its flexible setups through pip or conda and adaptable configuration options facilitate simple integration for developers extending geospatial functionality.
solo-learn
Discover self-supervised methods for unsupervised visual representation using PyTorch Lightning. Solo-learn offers a variety of models useful beyond its environment to enhance visual data processing. It includes features like configuration updates, data processing enhancements, and flexibility in training approaches. The platform emphasizes evaluation, feature visualization, and efficient model logging to support model implementation and assessment.
pytorch-forecasting
PyTorch Forecasting uses PyTorch's deep learning architectures to simplify time series forecasting for professionals and beginners. Built on PyTorch Lightning, it supports training on both CPUs and GPUs. Key features include a versatile time series dataset class, a robust model training framework with visualization tools, and various neural network architectures optimized for real-world applications. Featuring multi-horizon metrics and hyperparameter tuning using Optuna, this package supports accurate predictions in diverse scenarios. Explore a range of tutorials and resources to enhance forecasting capabilities.
yet-another-lightning-hydra-template
Built on PyTorch Lightning and Hydra, this template optimizes machine learning workflows for efficient prototyping and model comparison across diverse hardware, such as CPUs, multi-GPUs, and TPUs. Focusing on efficiency, reproducibility, and clarity, it provides extensive documentation and best practices, making it ideal for basic and complex machine learning tasks like classification and segmentation. It enhances workflow efficiency and fosters confidence and transparency in project results.
lightning-bolts
Lightning-Bolts extends PyTorch Lightning with additional components such as callbacks and datasets, facilitating applied research and production. It includes features like the Torch ORT Callback for enhanced training and inference speeds on NVIDIA or AMD GPUs. The SparseMLCallback further introduces sparsity in model fine-tuning using the DeepSparse engine. Encouraging community contributions, Lightning-Bolts evolves to support diverse machine learning needs in different domains without exaggeration.
tutorials
Explore the Lightning Library for a curated set of PyTorch Lightning tutorials that prioritize efficiency and reproducibility. Utilizing rich script formats, this repository ensures easy collaboration and environment consistency by supporting edits through Python scripts and conversion tools like jupytext. It offers a structured approach to contribution, dataset management, and valuable development insights, making it perfect for those looking to seamlessly create and manage tutorials in PyTorch Lightning.
lightning-hydra-template
The template efficiently initiates deep learning projects using PyTorch Lightning and Hydra, facilitating smooth configuration management. It supports multi-GPU training, mixed precision, and rapid experimentation, reducing boilerplate while allowing exploration of diverse models and datasets. Although focused on model prototyping, it offers extensive MLOps tools for educational and research purposes. Engage in experimentation with minimal setup to enhance your deep learning skills.
OpenSTL
OpenSTL offers a robust framework for spatio-temporal predictive learning with a variety of methods and use cases. Its modular design facilitates integration and customization for fields like video prediction, weather forecasting, and traffic analysis. The project provides implementations in PyTorch Lightning and naive PyTorch, addressing various technical preferences. Key features include adaptable code design, established benchmarks, a wide array of datasets, and necessary dependencies. The platform's comprehensive documentation, model zoo, and visualization tools enable researchers to effectively engage with and enhance the OpenSTL framework.
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