Introduction
Simplifying Machine Learning (ML) workflows can be a daunting task, especially when dealing with chaotic iterations and multiple experiments. With the increasing complexity of ML projects, it’s essential to have a reliable and efficient way to manage and track experiments. In this article, we’ll explore how the DVC (Data Version Control) extension for VS Code can help you streamline your ML workflows and make your life easier.
Need an easy way to run and track your experiments?
Install the DVC extension from the VS Code marketplace. Then, run experiments, visualize deep learning metrics in real-time, compare experiments, and save the ones you like – all from your IDE.
Run a Python file and see results
Want to simplify your chaotic ML iterations?
With the DVC extension, you can run reproducible workflows directly from VS Code.
Run a new experiment directly from VS Code
Live plots let you visualize metrics from these runs in real-time.
View plots in real-time
To make it easy for you to create the workflows, the extension even auto-generates code snippets.
Auto-generate pipeline specifications
Tired of context switching throughout the day?
The integration of DVC with VS Code empowers you to do everything from within your IDE. No more jumping from notebooks to the terminal to IDE to web browsers to Git.
DVC has helped individual ML developers and teams in companies like UBS, DeGould, Exscientia, Kibsi and many more to standardize their ML workflows on top of their cloud resources and Git repositories.
Visual Studio Code (VS Code) is, by far, the most popular IDE for all developers, including ML engineers.
The DVC extension makes VS Code even more useful for you by providing you a VS Code-native environment for managing your ML projects.
You get the power of DVC with capabilities beyond what’s available in the terminal!
With over 34 thousand installs, the extension is proven to help you solve the challenges of creating and managing your Machine Learning workflows.
To install the extension, open VS Code and search for “DVC” in the Extensions view.
Or install the extension from the VS Code marketplace.
Now, create a DVC repository for your machine learning project and start experimenting!
Here’s how to do this:
To see all the DVC commands supported by the extension, open the DVC Command Palette using F1 or ⇧⌃P on Windows/Linux or ⇧⌘P on macOS and typing DVC.
Over the last one year, we’ve made several enhancements in the DVC extension for VS Code.
For some of the interesting stuff you can do with it, watch the videos here.
As a mark of the extension reaching a new level of maturity, today we have launched it in Product Hunt. It would be awesome if you check it out and leave us some feedback and support!
Conclusion
Simplifying ML workflows can be a complex task, but with the DVC extension for VS Code, you can streamline your experiments and save time. With its ability to auto-generate code snippets, visualize metrics in real-time, and provide a VS Code-native environment for managing your ML projects, DVC is the perfect tool for any ML engineer.
Frequently Asked Questions
Q1: What is DVC?
DVC is a data version control system that helps you manage and track your machine learning experiments.
Q2: How does DVC work with VS Code?
The DVC extension for VS Code provides a VS Code-native environment for managing your ML projects, allowing you to run experiments, visualize metrics in real-time, and auto-generate code snippets.
Q3: What are the benefits of using DVC?
DVC helps you simplify your ML workflows by providing a reliable and efficient way to manage and track your experiments. It also allows you to visualize metrics in real-time and auto-generate code snippets.
Q4: How do I install the DVC extension for VS Code?
You can install the DVC extension for VS Code by opening the Extensions view in VS Code and searching for “DVC”. Alternatively, you can install it from the VS Code marketplace.
Q5: What are some of the interesting features of the DVC extension for VS Code?
Some of the interesting features of the DVC extension for VS Code include its ability to auto-generate code snippets, visualize metrics in real-time, and provide a VS Code-native environment for managing your ML projects.
