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The DVC foreach feature is a game changer when you have repetitive tasks in your pipeline. For example, you might want to train several different types of models on the same task, and then compare the different models' performance in one final evaulation step. Or you might want to perform the same preprocessing step on different subsets of the data. In each of those cases, in the past you would have to manually define copies of repetitive steps in your DVC pipeline.No more!DAGsHub now supports the new format, and you can bring your pipeline automation to the next level - Don't Repeat Yourself.
It's sometimes a hassle to list all your data files or find a notebook or a model from within your repository. With the smart tab presentation, you can now filter data files, models files, DVC files, notebooks and easily find the files you're looking for.
Smart tabsWe've now made a huge step forward with page responsiveness. It's easy to browse your project on mobile (or if you have a really narrow screen), so if that's your thing, you should be pretty happy.
If you've signed up with GitHub and want to migrate a project, we made it significantly easier, and more awesome to do. We'll scan your GitHub projects, show you their information, and let you choose which ones to import. Check it out in the connect screen.
You can now sign in to DAGsHub using your Google account. Yay!
Each user now gets a default access token, we also made it easier to create, copy and delete access tokens! You can use a token instead of your DAGsHub password for DAGsHub Storage, hosted MLflow, and pushing to Git. Find your token here
DAGsHub now supports MLflow experiment tracking. This means that every project you create comes with a hosted MLflow server. All you need to do to see your MLflow experiments is to add the URL, and your credentials. To read more on how to configure your local MLflow setup – read the doc.
Yep. You heard right. You can now DIFF tabular data. Comparing tabular data will show you an interactive view with rows changed, added, and deleted. It's pretty awesome if we say so ourselves.
Some users asked to flip the order of comparison between different versions because visualizing changes was more intuitive that way. For example switching between comparing master<>new_feature to new_feature<>master. This is now possible by clicking the arrow button between the comparison selectors.
You can now invite new users to a project or organization. Within the project settings you'll find the invite button in the Collaboration tab, and you can create an invite with a copyable link, or send an email directly to your collaborator.
You'll see the same window in an organization team pages, as well as the organization member page.
You can now interactively view your .csv and .tsv files, enabling you to sort, filter and make sense of your data/metric files.
You can now have multiple DVC pipelines in one project, and they will all be shown in the data pipeline window. Each will be visualized as a separate connected component within the same view.
This is a big one! Setting up a DVC remote can be a hassle, with credit cards, cloud permissions and IAM. But not anymore. With DAGsHub storage, we reduce configuration to 0. You can just dvc remote add origin https://dagshub.com/<user>/<repo-name>.dvc and push. All the benefits of DAGsHub work out of the box. Read more about it in our launch blog post.
Getting started should be as fun, easy and accessible as possible. We felt like our tutorial could do better on some of those fronts, so we've updated it. Check out our new Basic Tutorial, which covers DVC basics. We'll cover data exploration, data versioning (in a much simpler fashion), experiment tracking, and more, all while creating a project which is much closer to real-life data science. Many users don't necessarily need the more advanced DVC pipelines, so we've separated them from the original tutorial into the Pipeline Tutorial, which, as it's name suggests, focuses more on pipelines.
Data science has many unique requirements, and handling folders with a TON of changed files is one of them. This means that looking at file-by-file comparisons are less informative, and that's exactly why we created directory comparisons. In every file or folder view, we've added a dropdown to select a branch, tag, or commit to compare to. By selecting a Git branch, tag, or commit, you can compare the project filesystem between these two states, and easily make sense of what changed.
You now have a natural browsing experience of your working directory. DAGsHub can read directory dependencies and show their content in the file tree. In addition to directories, files can be displayed naturally, the same way we display git-tracked files.
You can now login or sign up to DAGsHub using your Github user, with just a click or two!
Major feature update. Data Science Pull Requests (DS PRs) are the natural extension of Pull Requests (PRs) to the realm of data science. You can now discuss, compare, and review changes to experiments, data, and models in your pull requests. After you finish your review, you can merge your data, models, and artifacts automatically. Read more in the dedicated doc page.
You can now diff your notebooks in a meaningful way, making it easy to understand what inputs and outputs changed.
 Notebook diffingWe've updated the Experiment Tab with a ton of cool features. You can now filter and search experiments with much more granularity, drag and drop to reorder or hide columns and search for the columns you need in the column selection panel.
Many users have told us they'd like to change their experiments' names. We're happy to announce this is now possible, just click the edit (pencil) button in the single experiment view to edit.
DAGsHub now supports DVC 1.0. You can safely upgrade and enjoy all the things you love about DAGsHub. If you need help with the upgrade, we also created an automatic migration script.
If you always wanted to connect your GitHub repository to DAGsHub so that you can get the best of both worlds, this one's for you! You can now connect existing repos by clicking the + Create button in the navigation and choosing New Connection. DAGsHub supports GitHub, GitLab and Bitbucket.
You can now view and download your DVC managed files and folders not only in the pipeline view but also in the file tree. Remote files have a blueish tint to their file tree row.
[](assets/dvc_file_tree.png)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.
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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.
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