Missing Jupyter package prompt during notebook cell execution

Resolving Missing Jupyter Package Prompts

To resolve the prompt for missing jupyter packages in DataSpell, you must install the Jupyter stack into the specific Python interpreter or Conda environment currently selected for your project. DataSpell requires these packages locally to initialize its managed server.

Resolution Steps

1. Automatic Installation: Click the install prompt provided by the IDE. DataSpell will attempt to inject the required dependencies into the active environment.
2. Manual Installation: If the prompt fails, open the terminal associated with your environment and run:

   pip install jupyter

   Note: For Conda environments, use conda install jupyter.
3. Verification: Verify the installation by running pip list or conda list to ensure jupyter and ipykernel are present.

Managed vs. External Jupyter Servers

DataSpell offers two primary ways to handle the notebook backend. Choosing between them depends on your need for environment stability versus convenience.

Managed Server (Default)

The IDE handles the server lifecycle. While convenient, this approach can be fragile because DataSpell's internal server components may have specific version requirements that conflict with your project's pinned dependencies. Pinning your environment's Jupyter stack does not guarantee stability across IDE updates, as the managed server's internal logic may change.

External Server URL

Bypassing the managed server in favor of a configured external URL is the preferred architectural decision in the following scenarios:

• Strict Version Control: When your project requires a specific, immutable version of the Jupyter stack that conflicts with DataSpell's requirements.
• Remote Execution: When the compute resources (GPU/RAM) are located on a remote server rather than your local machine.
• Organizational Compliance: When security policies require Jupyter to run within a specific container or audited environment.

Troubleshooting Kernel Mismatches

If packages are installed but the kernel fails to start (common after migrating .ipynb files), there may be a mismatch between the kernel specification in the notebook metadata and the registered kernels in your environment. You can verify registered kernels by running:

jupyter kernelspec list

Diagnostic Detail Needed: Are you seeing a specific "Kernel Error" message, or is the IDE simply failing to trigger the installation prompt despite the packages being missing?