Sometimes you don't feel like using the command line.
Or you made a typo in one of the file names and want to fix it quickly.
You can now edit your DVC data pipeline in DAGsHub's visual interface.
The home for data science collaboration
A community oriented data science platform for collaboration.
Based on Open Source tools and open formats.
Made with 🐕 for data scientists.
Get started. Fast.
Build your own ML project:
Start the tutorial
Frequently asked questions:
Learn more
See your new superpowers:
Example repo
Latest updates
Experiment tracking
Track your experiment parameters and metrics using simple, readable and open formats.
Gain insights into your research using smart visualizations. Find the experiment you need, easily.
New
Tutorial for experiment tracking
A new tutorial, walking you through a realistic data science project and showing how to use DAGsHub's
experiment tracking and visualization features
to organize and share your research.
"No one can whistle a symphony. It takes a whole orchestra to play it."
When people work smarter, and work together, they can build awesome things.
Data scientists are struggling with this.
The basis for frictionless collaboration is the ability to understand what others have done, and the ability to pick up where they left off.
DAGsHub is a web platform based on open source tools, optimized for data science and oriented towards the open source community.
It is a central location where projects can be hosted, discovered, and collaborated on.
Superpowers you get with DAGsHub
Collaborate effectively
Leverage the same best practices used in software engineering to get more done. DAGsHub helps
automate your workflow,...
so you can focus on work instead of coordination.
Share intermediate results from your pipeline with any collaborator, instead of re-running all the pre-processing code, and re-run only parts of the pipeline that you changed.
Ease of use also means it takes less time to get new team members and collaborators caught up and contributing.
Share intermediate results from your pipeline with any collaborator, instead of re-running all the pre-processing code, and re-run only parts of the pipeline that you changed.
Ease of use also means it takes less time to get new team members and collaborators caught up and contributing.
Reproduce results easily
Reproduce any experiment from previous versions of your project. It’s as simple as switching a branch and running a single command....
Experiment with different hyper parameters in parallel. Each run will automagically save its results in an organized and easy to access location.
Data & pipeline versioning
Maintaining giant spreadsheets of hyper parameters and inconsistent naming conventions are a thing of the past....
DVC tracks data & code versions. Every time you run a stage in your pipeline, your data is saved
automatically, so you don’t need to worry about accidentally ruining your data.
You can visualize your pipeline (as DAGs) and see how it changes over time, and get any version of your model with a click.
You can visualize your pipeline (as DAGs) and see how it changes over time, and get any version of your model with a click.
Simplicity with Open Source & open formats