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DagsHub Client


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What is DagsHub?

DagsHub is a platform where machine learning and data science teams can build, manage, and collaborate on their projects. With DagsHub you can:

  1. Version code, data, and models in one place. Use the free provided DagsHub storage or connect it to your cloud storage
  2. Track Experiments using Git, DVC or MLflow, to provide a fully reproducible environment
  3. Visualize pipelines, data, and notebooks in and interactive, diff-able, and dynamic way
  4. Label your data directly on the platform using Label Studio
  5. Share your work with your team members
  6. Stream and upload your data in an intuitive and easy way, while preserving versioning and structure.

DagsHub is built firmly around open, standard formats for your project. In particular:

Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.

DagsHub Client API & CLI

This client library is meant to help you get started quickly with DagsHub. It is made up of Experiment tracking and Direct Data Access (DDA), a component to let you stream and upload your data.

For more details on the different functions of the client, check out the docs segments:

  1. Installation & Setup
  2. Data Streaming
  3. Data Upload
  4. Experiment Tracking
    1. Autologging
  5. Data Engine

Some functionality is supported only in Python.

To read about some of the awesome use cases for Direct Data Access, check out the relevant doc page.

Installation

pip install dagshub

Direct Data Access (DDA) functionality requires authentication, which you can easily do by running the following command in your terminal:

dagshub login

Quickstart for Data Streaming

The easiest way to start using DagsHub is via the Python Hooks method. To do this:

  1. Your DagsHub project,
  2. Copy the following 2 lines of code into your Python code which accesses your data:
    from dagshub.streaming import install_hooks
    install_hooks()
  3. That’s it! You now have streaming access to all your project files.

🀩 Check out this colab to see an example of this Data Streaming work end to end:

Open In Colab

Next Steps

You can dive into the expanded documentation, to learn more about data streaming, data upload and experiment tracking with DagsHub


Analytics

To improve your experience, we collect analytics on client usage. If you want to disable analytics collection, set the DAGSHUB_DISABLE_ANALYTICS environment variable to any value.

Made with 🐢 by DagsHub.

DagsHub's Projects

cvat icon cvat

Powerful and efficient Computer Vision Annotation Tool (CVAT)

fds icon fds

Fast Data Science, AKA fds, is a CLI for Data Scientists to version control data and code at once, by conveniently wrapping git and dvc

gitea icon gitea

Git with a cup of tea! Painless self-hosted all-in-one software development service, including Git hosting, code review, team collaboration, package registry and CI/CD

gochimp3 icon gochimp3

πŸ’ Golang client for MailChimp API 3.0.

hacktoberfest-issues icon hacktoberfest-issues

A repository that holds all the issues on DagsHub that take part in Hacktoberfest 2023 for contribution management

label-studio icon label-studio

Label Studio is a multi-type data labeling and annotation tool with standardized output format

ml-workspace icon ml-workspace

πŸ›  All-in-one web-based IDE specialized for machine learning and data science.

open-source-ml-datasets icon open-source-ml-datasets

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

open-source-ml-models icon open-source-ml-models

This repository holds open-source machine learning models for various domains ready to download and use

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