If the above steps do not resolve the issues, it might be helpful to provide more specific information such as error messages or code examples so that further assistance can be provided. Complex models or large datasets might require more memory or processing power, which could cause cells to take longer to execute. Check the resource usage of your system. That will use the pip associated with the kernel in use. Make sure there are no infinite loops or other errors in the code that could prevent the cell from finishing. If you are installing spacy from inside the jupyter notebook, use the pip syntax. Verify that your code is correct and properly written. Ensure that you have the necessary dependencies installed. Check if you have installed the correct version of PyTorch compatible with your system and environment. If you are experiencing issues with PyTorch cells not finishing, it could be due to a few reasons: If your answer to 1 is yes, can you also look at your console log in VSCode. When using that same environment outside of vscode (from the command like) can you run jupyter notebook and get a valid server 2. If the issue persists, try restarting DataSpell itself. First off, you are launching jupyter from your selected python environment in VSCode when you use the interactive window. Restart the Jupyter server and try opening the notebook again. Check if the notebook file is located in the correct directory and accessible by the Jupyter server. Make sure the Jupyter server is running properly on localhost:8888. You can try the following steps to resolve this: The message you see over the notebook might be an indication that the notebook is not fully loaded or there is an issue with the connection to the Jupyter server.
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