Introduction
https://dvc.org/doc/install DVC 3.0 empowers you to experiment with ease, from notebook exploration to model management, leveraging cloud/remote storage for seamless data versioning.
Experiment Tracking and Beyond
In DVC 2.0, we introduced experiments, providing a way to track them as lightweight Git commits, enabling you to manage experiments alongside your code. With the DVC extension for VS Code, you now have a workbench for tracking experiments without any servers or logins. Your experiments are also accessible in our collaboration hub, Studio, and connected to your Git repo, allowing for easy sharing, reviewing, and merging.
Model Management
The Studio Model Registry enables you to manage the entire model lifecycle inside your Git workflow, from creating the model to deploying it in any deployment system. Our approach to model management is consistent with everything else we do – it’s all about integrating with your existing stack and tools and empowering you to tie your workflows around GitOps principles and automation.
Cloud Experiments (Alpha Release)
When we released DVC 2.0, we also introduced the cml runner command to run continuous integration (CI) on your own cloud instances. Cloud experiments build on this technology without CI, offering less setup (configure directly in Studio). With the alpha release of Studio Cloud Experiments, you can run DVC experiments on your own cloud infrastructure with just a few clicks, including support for GPU and spot instance.
Hyperparameter Optimization
DVC can also help with hyperparameter optimization by integrating with other tools. You can queue an entire grid search of experiments, configure multiple complex model architectures with Hydra integration, and track your Optuna studies.
Smarter Cloud/Remote Storage
Sometimes you need faster performance, especially for large data downloads and uploads. We’ve focused on improving performance where it matters most. For example, pushing data to S3 is 2.5x faster in DVC 3.0 than in early versions of DVC 2.x.
Faster Performance
Our constant interaction with the DVC community gives us feedback on what should be improved. We heard from you that the ML landscape is already complex and you want to keep your tools simple. That’s why many of the new features are improvements to existing functionality, and why we are building this stack of tools to make DVC easier, more flexible, and the solid choice for your MLOps workflows.
Conclusion
DVC 3.0 provides a seamless experience for tracking experiments, managing models, and leveraging cloud/remote storage for data versioning. With its new features and improvements, DVC is now more accessible, flexible, and efficient. Get started with DVC 3.0 today and experience the power of MLOps!
Frequently Asked Questions
Q1: How do I get started with DVC 3.0?
A1: You can start by reading the DVC documentation or watching the tutorials on the DVC website.
Q2: Can I use DVC with my existing Git workflow?
A2: Yes, DVC integrates seamlessly with your existing Git workflow, enabling you to manage your data and models alongside your code.
Q3: How do I track experiments with DVC?
A3: You can use the DVC extension for VS Code or configure the cml runner command to track your experiments.
Q4: Can I use DVC with my existing cloud storage?
A4: Yes, DVC supports cloud storage providers like AWS S3, Google Cloud Storage, and Azure Blob Storage.
Q5: How do I get help with DVC?
A5: You can seek help from the DVC community, read the DVC documentation, or reach out to the DVC support team.
