<h3>About DVCLive</h3>

We have a <a href="https://github.com/iterative/dvclive">new project DVCLive</a>, not DVC so that might be better for students. DVCLive is newer (easy to contribute) and more ML specific (it might be a better fit for the students).&nbsp;

DVCLive is an open-source library for monitoring the progress of metrics during training of machine learning models. It&#39;s built with Git and MLOps principles in mind:

<ul>
	<li>Codification of data&nbsp; 
	Tracked metrics are stored in readable text files that can be versioned by Git or other version control tools.</li>
	<li>Distributed&nbsp; 
	No services or servers are required. Metrics are stored in a Git repository as text files, or pointers to files in DVC storage.</li>
	<li>GitOps API&nbsp; 
	Plots are generated through DVC using Git commit SHAs or branch names, e.g.:&nbsp;dvc plots diff --target logs master.</li>
	<li>Automation 
	DVCLive metrics are easy to use by any automation, DevOps, or MLOps tool such as CI/CD (including CML), custom scripts, or ML platforms.</li>
</ul>

<h3>Key Summer of Code task</h3>

Help us with the ongoing active issues of our library. We have ML-specific DVCLive with the set of good-first-issue as well as DVC with a set of good-first-issue. We decide down the road what is the best for the ODS Summer of Code participants.

We have already started to mark some issues as&nbsp;good first issues, and in case of dvclive&nbsp;<a href="https://github.com/iterative/dvclive/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">all marked</a> as such should be fine to take by newcomers.&nbsp;

We will be on the lookout for this kind of issues, and will also be going through dvc&#39;s open issues and marking them too.

<h3>Call for integration collaboration&nbsp;</h3>

Today only Python is supported (while DVC is language agnostic), with a minimum number of integrations with ML frameworks:

<ul>
	<li>Tensorflow/Keras</li>
	<li>XGBoost</li>
	<li>MMCV</li>
</ul>

The DVCLive team is happy to extend the functionality as needed. Please create an issue to start a discussion!

<h3>Contacts and other useful materials&nbsp;</h3>

<ul>
	<li>ODS slack channel <a href="https://opendatascience.slack.com/archives/CGGLZJ119">#tool_dvc</a></li>
	<li><a href="https://github.com/iterative/dvclive">Library GitHub</a>&nbsp;</li>
	<li><a href="https://github.com/iterative/dvclive/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22 ">Good first issues</a>&nbsp;</li>
</ul>

We invite ODS Summer of Code participants to help us on our new DVC-based library DVCLive.

What is DVC?

🔗 <a href="https://dvc.org/doc/user-guide/what-is-dvc">Git for Data</a> (What is DVC?)

Data Version Control (DVC) is an open-source tool for data science and machine learning teams to manage datasets, ML models, and experiments in Git. Key parts include:

<ul>
	<li>&ldquo;Git for Data and Models&rdquo; - DVC extends Git versioning to large files like datasets and ML models for rigorous project management.&nbsp; Use your regular Git workflow for ML projects, and share project materials with a Git repository URL.</li>
	<li>&quot;Makefiles for data and ML projects&quot; - DVC pipelines are makefiles for ML, with optimizations and human-readable formatting ideal for organizing ML projects. Pipelines connect scripts and dependencies, like datasets and models, for reproducibility and efficiency.</li>
	<li>&ldquo;Experiment tracking via Git&rdquo; - Compare model metrics, hyperparameters, and plots across commits, branches, and releases..</li>
</ul>

&nbsp;

Short History:

DVC was created in 2017 to address gaps in ML tools, and has evolved into a successful open source project with 150+ contributors and thousands of users.

Some interesting highlights from the community:

<ul>
	<li><a href="https://pythonbytes.fm/episodes/show/191/live-from-the-manning-python-conference">PythonBytes podcast with makers of spaCy, an NLP library, on integrating with DVC</a></li>
	<li><a href="https://martinfowler.com/articles/cd4ml.html">Martin Fowler blog: automating the end-to-end lifecycle of ML applications</a></li>
	<li><a href="https://medium.com/qonto-engineering/using-dvc-to-create-an-efficient-version-control-system-for-data-projects-96efd94355fe">How Qonto Engineering is using DVC</a>&nbsp;&nbsp;</li>
</ul>

If you want to learn more about DVC and its journey, check out an interview with DVC creator Dmitry Petrov on Podcast.init. Listen <a href="https://www.pythonpodcast.com/data-version-control-episode-206/">HERE</a> or read the transcript <a href="https://towardsdatascience.com/data-version-control-with-dvc-what-do-the-authors-have-to-say-3c3b10f27ee">HERE</a>.

Recently, version 1.0 was released. DVC 1.0 is inspired by discussions and contributions from our community of data scientists, ML engineers, developers and software engineers. Read up on new features, like data visualization and data transfer optimizations, in our release <a href="https://dvc.org/blog/dvc-1-0-release">blog post</a>.

Today, the project remains under active development.&nbsp; Now that the data management layer has reached a stable form, the DVC team is focusing on the data scientist&rsquo;s experience. Our goal is to become&nbsp; Git for ML - a holistic tool to capture the ML experiments lifecycle following a Git-like philosophy. That means, no complicated infrastructure, databases, or dependencies on third-party external APIs.

&nbsp;

Contributor&rsquo;s Guide:

🔗 <a href="https://dvc.org/doc/user-guide/contributing/core">DVC Contributor&rsquo;s Guide</a>

We welcome contributors from different backgrounds and levels of experience! We&rsquo;ll be happy to guide and help with the contribution - either to the core project, documentation, tutorials, or blog. We&rsquo;ve participated in the programs like Google Season of Docs (similar to Google Summer of Code) and have substantial experience guiding and mentoring folks who do their first contributions. As well as we have an established community of experienced contributors.

DVC is written in Python. It&rsquo;s a command line tool that deals with large files and Git internals (among other things) but is built for ML engineers and data scientists in mind as end users. You may be a great match for us if you want to see and learn software engineering best practices in a mature project, and if you&rsquo;re curious how ML teams operationalize their workflow.

The best way to start is to check the project&rsquo;s <a href="https://github.com/iterative/dvc/issues">issue tracker</a> (or <a href="https://github.com/iterative/dvc.org/issues">this one</a> for the website and docs). There are usually plenty of low hanging fruits, which are tagged with &ldquo;<a href="https://github.com/iterative/dvc/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22">first good issue</a>&rdquo; and &ldquo;<a href="https://github.com/iterative/dvc/issues?q=is%3Aopen+is%3Aissue+label%3Ahacktoberfest">hacktoberfest</a>&rdquo; labels. We recommend&nbsp; getting in touch with us about the issue (to confirm the details, get help, etc.)&nbsp; in one of the <a href="https://dvc.org/support">support channels</a> or in the <a href="https://opendatascience.slack.com/archives/CGGLZJ119">#tool_dvc</a> channel in ODS.

Dmitry Petrov

Ivan Shcheklein

Ruslan Kuprieiev

Data Version Control (DVC)

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DVC @ Summer of Code

About DVCLive

Key Summer of Code task

Call for integration collaboration

Contacts and other useful materials