Deepnote, hailed as the Jupyter Notebook successor,
is now open-sourced after 7 years running as a sole cloud offering.

The main pain point that Deepnote addresses is the limitation of the single-player notebook. Jupyter's age means it was created in a pre-cloud, pre-collaboration and pre-AI era. The times now require reactive and collaborative tools that integrate into your existing stack.

In contrast to the old horse Jupyter, Deepnote sports built-in AI capabilities such as a native AI agent, code completion and seamless integration with the popular coding tools like VS Code or Windsurf. It also integrates with Git, can share worksheets with a click, supports native database and API connections, as well as offering interactive inputs, charts, KPIs, buttons plus hundreds of data integrations. That said, it's also compatible with the Jupyter format as you can export to .ipynb from your notebook.

Deepcode, however is not just the application, it's also a format, .deepnote which introduces a few key features in comparison to its Jupyter counterpart:

• Human-readable YAML
• Support for securely connecting to data sources and other integrations
• Multiple notebooks per project, with shared dependencies and integrations
• Extensible block types, Deepnote already has 23 of them (SQL as first-class citizen, charts, inputs, KPIs, text)
• Verifiable schema, ensuring metadata integrity and forward compatibility
• Language-agnostic, supporting Python, SQL, TypeScript, and sometimes even in the same notebook.

To get started say from VScode, head over to the marketplace to get and install the extension. Then:

Open a folder containing .deepnote project files or convert an existing Jupyter notebook to deepnote format with:

npx @deepnote/convert notebook.ipynb

Then

1. Find the Deepnote icon in the Activity Bar (sidebar)
2. Click on a notebook in the Deepnote Explorer to open it
3. Select a Python kernel when prompted
4. Start coding

While the project is open sourced and can be found up on Github, as yet you can't:

• Take the UI you're used to from Deepnote Cloud and run it locally
• Edit notebooks with a local AI agent
• Bring your own keys for AI services
• Run your own compute

But these faciliities will be added will soon.

Watch the repo for that to happen, or join the managed cloud offering.

More Information

Deepnote is a data notebook for the AI era  

Related Articles

Learn Python With Dan The Machine Learning Engineer  

To be informed about new articles on I Programmer, sign up for our weekly newsletter, subscribe to the RSS feed and follow us on Twitter, Facebook or Linkedin.

Comments

Make a Comment or View Existing Comments Using Disqus

or email your comment to: comments@i-programmer.info