DagsHub client libraries

A repo of models for Auto-labeling with Label Studio ML backend. Requires working with https://github.com/DagsHub/ls-configurable-model

An end-to-end LLM hello world tutorial. Follow along to build a RAG Chatbot to provide support on DagsHub questions. Using best practices in RAG, model selection, prompt engineering, LLM fine-tuning (PEFT), and LLM evaluation. All in one!

A hello world project that covers dataset curation, annotation, experiment tracking and model management. All in one place

A project for onboarding materials for DagsHub. Mainly used in the DagsHub Docs

Easily create projects with the datasets and models you want to use, versioned, and ready to roll.

This project provides a comprehensive guide on using DagsHub to manage machine learning datasets and models. It includes steps for setting up a DagsHub project, connecting data buckets, creating datasets, annotating data, fine-tuning models, tracking experiments, registering models, and logging predictions. The guide is supplemented with visual aids to help users follow along easily.

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A project combining DagsHub and Google Colab – Showcasing DagsHub as an ML-first GDrive Alternative

Sample of image data with segmentation annotations in COCO format.

Automates migration of ML experiment data from Weights & Biases to DagsHub using MLflow for unified experiment tracking. Facilitates easy sharing, collaboration, and version control of ML projects.

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We're excited to introduce interactive Jupyter Notebook Outputs 🚀 This project has a few examples of working with interactive notebook outputs for data profiling and embedded experiment tracking on DagsHub

DPT is a QA-bot designed to help answer questions about DagsHub. It is a fork of the brilliant buster project.

This repository hold open source datasets for various machine learning domains with a link to download and use them

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