9 posts tagged with "jupyter"

We're excited to announce Jupyter Embed, the simplest way to bring the full power of Jupyter to any website, blog, or documentation page.

Want to see Jupyter Embed in action before copying any code?

The demo showcases all component types - code cells, notebooks, terminals, consoles, and viewers - running live in your browser. It works seamlessly across:

• ✅ Desktop browsers: Chrome, Firefox, Safari, Edge
• ✅ Mobile browsers: iOS Safari, Android Chrome
• ✅ Tablets: iPad, Android tablets

No installation required. Just open the link and start exploring interactive Jupyter components.

I recently had the privilege of presenting at JupyterCon 2025, where I shared our work on the Jupyter MCP Server that aims to standardize how AI agents interact with Jupyter notebooks through the Model Context Protocol (MCP).

Discover how Datalayer is enhancing Jupyter for remote code execution.

We're excited to announce a significant enhancement to the Datalayer ecosystem with the release of jupyter-mimetypes, a Python package that provides a simple interface for variable exchange between Jupyter kernels and client applications. This new package has become a foundational dependency of jupyter-kernel-client, enabling a more elegant and simplified Python SDK API in the Datalayer core package.

We are thrilled to announce our collaboration with Anaconda, a leader in Data Science and AI platforms. This partnership marks a step forward in our mission to democratize access to high-performance computing resources for Data Scientists and AI Engineers.

Anaconda offers Anaconda Notebooks, a cloud-based service that allows data scientists to use Jupyter Notebooks without the hassle of local environment setup. Through our collaboration, we are enhancing this platform with Datalayer's Remote Runtime technology, bringing seamless GPU access directly to Anaconda Notebooks users.

We are super excited to announce that Datalayer is entering Private Beta! After months of development, we are inviting today those who signed up on our waiting list to experience our solution first-hand.

In the realm of AI, data science, and machine learning, Jupyter Notebooks are highly valued for their interactive capabilities, enabling users to develop with immediate feedback and iterative experimentation.

However, as models grow in complexity and datasets expand, the need for powerful computational resources becomes critical. Traditional setups often require significant adjustments or sacrifices, such as migrating code to different platforms or dealing with cumbersome configurations to access GPUs. Additionally, often only a small portion of the code requires GPU acceleration, while the rest can run efficiently on local resources.

What if you could selectively run resource-intensive cells on powerful remote GPUs while keeping the rest of your workflow local? That's exactly what Datalayer Cell Kernels feature enables. Datalayer works as an extension of the Jupyter ecosystem. With this innovative approach, you can optimize your cost without disrupting your established processes.

We're excited to show you how it works.

We are thrilled to announce the 0.0.6 release of Datalayer. This release improves the data analytics user and developer experience with Jupyter React, a javascript library to ensure React.js is a first-class citizen in the Jupyter ecosystem.

Jupyter React is built on top of JupyterLab which aims to be the next default notebook for Python data scientists and is actively developed. However, some users sill prefer the classic notebook and JupyterLab is not yet mainstream... The following points can be the identified as the source of the shadow:

1. The user interface is intimidating and quite complicated. An initiative to strip-down the user interface has been taken with Retrolab, but the result still looks pretty much like JupyterLab without visible value compared to the classic notebook. Users will even loose some beloved features like their preferred keyboard shortcuts, VIM mode, performance...
2. The extensions ecosystem is rich but breaking changes in the core of JupyterLab have made the overall ecosystem fragile and subject to failures on installation.
3. The overall performance (startup time, load large notebook, switch tabs...) is know to be degraded on JupyterLab.
4. The recently merged realtime collaboration feature is solely not usable with a real multi-user authentication and authorization system.
5. As developer, the Lumino widget toolkit which backs JupyterLab user interface is hard to use and looks pretty much like a Qt toolkit rather than like a modern javascript e.g. React.js, Vue.js, Svelte...

All Data Scientists know that story... Install the well-known Jupyter Classic or JupyterLab Notebook on their local PC/laptop, pip install some python libraries like pandas..., download some datasets and finally start analysing with a notebook in isolation. There are a few pain points there:

1. Setting up the tools is hard and time consuming. You have to install Python, Jupyter and add the libraries you need. Conda environments or Docker containers can help mitigate the pain at some point, but finally these are yet additional tools to setup and manage.
2. At some point, they want to collaborate with teammates, or want to share some results. The Data Scientist is just on his island and has no easy way to break the silo. The recent Realtime collaboration features have been merged into JupyterLab but it is just the permises and miss fundamental building blocks like identity, authorization...
3. The analysis is not easily reproducible. The setup you have done on a particular Windows platform is completely different from the setup another collaborator may have done on macOS.

More Cloud-native​

There comes the need for an better solution. At Datalayer we think that a more Cloud-native Jupyter can help remove those pain points. In other words, we embrasse the infrastructure provided by cloud providers like GCloud, AWS, Azure... and build on top to provide more power to the Data Scientist.

Cloud native computing is an approach in software development that utilizes cloud computing to "build and run scalable applications in modern, dynamic environments such as public, private, and hybrid clouds.