2 posts tagged with "release"

We're excited to announce the release of Datalayer 1.2.0 — Black Snake — a major release packed with new features and enhancements that significantly improve performance and user experience.

The Datalayer Black Snake release is named as a tribute to the Python opensource community as to the companies like Anaconda and Quansight for their support in driving innovation in the open.

New Features​

Cell-Specific Kernels: Execute specific cells with different kernels, optimizing costs by leveraging local resources for data preparation and remote resources for intensive computations.

Cell Kernel execution

The remote GPU Kernel is utilized only for the duration of the cell computation, minimizing costs.

CLI Execution: Execute code remotely from your local terminal.

CLI Remote Execution

Sharing State between Notebook and CLI

When using the same Kernel, variables defined in a notebook can be used in the CLI and vice versa. This holds also true when using multiple notebooks connected to the same kernel, for example.

User storage: Users can now persist data across kernels sessions. Read more about it in this blog post.

Bug Fixes and Stability Improvements​

Improved Kernel Stability: Addressed several kernel stability issues that users encountered when running long-running processes.

Resolved Environment Variable Conflicts: Fixed issues related to environment variable management in GPU-accelerated environments, ensuring smoother integration with external services and data sources.

Security Enhancements: Continued improvements in secret management and encryption to ensure safe data handling when accessing external data sources and services.

How to Get Started​

Existing Users: Existing users can update their environments to Datalayer 1.2.0.

pip install datalayer --upgrade

New Users: New users can ask for an invitation to the beta and get started with Datalayer by following the documentation:

• Use it with JupyterLab: Read the documentation on https://docs.datalayer.io/jupyterlab
• Use it with Visual Studio Code: Read the documentation on https://docs.datalayer.io/vs-code
• Use it with a Command Line Interface: Read the documentation on https://docs.datalayer.io/cli

Upcoming Features​

Storage Management: Enhanced storage management capabilities will be introduced in the next release, allowing users to manage their data more efficiently.

Expanded Data Source Support: More integrations with popular data sources will soon be available, further simplifying cloud data access.

User Environment: Users will be able to create their custom environments, allowing them to install specific packages and libraries.

Collaboration: Stay tuned for collaborative features coming in the next release, allowing multiple users to work together with the same kernel.

Install Datalayer​

Datalayer is a JupyterLab extension. To install it, just run the following command in your terminal.

pip install datalayer

You will need python>=3.9 and pip available on your machine.

JupyterLab Launcher​

Launch JupyterLab as usual.

jupyter lab

JupyterLab users are used to go the their launcher which present typical tiles to create a Notebook and launch a Kernel.

Datalayer introduces a new element at the top of the JupyterLab launcher.

Account​

The first step is to authenticate.

If this is your fist contact with Datalayer, you will need an account. Just fill in a few details and check your mailbox for the confirmation code.

Serverless​

Once authenticated, Datalayer takes care of the rest and will create the needed services for you in its own infrastructure.

You don't have to worry on anything, just wait on the green light that should appear on your Home page.

Kernels​

Once the services are available, it may take a bit of time to have your kernels up-and-running. For now, we offer you 3 differents Remote Kernels.

The Home page also list your local machine Kernels, and will offer in next releases the ability to create local browser Kernels.

Remote Kernels​

Remote Kernels creates for now predefined Remote Kernels from your local JupyterLab.

Notebooks​

To ease the onboarding, you can create example of Notebooks clicking on the Example buttons.

This step is of course completely optional and you are welcome to directly use your own Notebooks.

You can use the Kernels from the standard JupyterLab kernel picker.

Click on the top-right picker of the Notebook, and assign a Kernel to Notebook (the Remote Kernels are listed at the top).

Local Files​

To mount your Local Files to the Remote Kernel, a SSH Server must be running on your local machine (on port 22) and you must be able to connect without password prompt from your local terminal.

# Has to connect without password prompt.
ssh localhost
# ...

Kernel Lifecycle​

You can delete a Kernel.

We will support the start as pause of the Kernel.

Need Help?​

Contact us for support, we are here to help.