You can try reinstall Jupyter,

pip uninstall jupyter
pip install jupyter

and try again, which works in my case. I had this problem when I upgrade to Ubuntu 18.04.

Also check this thread. https://github.com/jupyter/notebook/issues/1892

If you are using serious memory or running jobs that take time, get out of jupyter and get into a terminal. Jupyter notebooks are for exploration and data analysis. They're not meant to be where you train deep models or do heavy processing tasks.

There are a few potential reasons for the issues you're experiencing with your Jupyter notebook on AWS Braket. Let's address them one by one: 1. Kernel dying: This is likely due to memory constraints. When working with large datasets or complex models, it's common to run out of memory, causing the kernel to crash. To resolve this: - Restart the kernel: Navigate to the "Kernel" menu in your Jupyter Notebook and select "Restart Kernel". - Check resource utilization: Monitor your instance's memory usage using system commands or CloudWatch metrics. - Increase instance size: If you're consistently running out of memory, consider using a larger instance type with more RAM. 2. ModuleNotFound errors: These errors occur when Python can't find the required packages. To address this: - Ensure proper installation: Make sure you've correctly installed the required packages (scikit-learn, tensorflow, etc.) in your environment. - Verify environment activation: Double-check that you're using the correct environment where these packages are installed. - Install missing packages: If packages are missing, install them using pip or conda within your Jupyter notebook or terminal. 3. Environment issues: Creating a new environment is a good approach, but ensure you're activating it correctly: - Create a new conda environment with necessary packages. - Register this environment with Jupyter: ``` python -m ipykernel install --user --name=my_notebook_env ``` - Select the new kernel in Jupyter: Click "Kernel" > "Change kernel" and choose your new environment. 4. AWS Braket specific considerations: - Check AWS Braket documentation for any specific package versions or compatibility issues. - Ensure you have the necessary permissions to install packages and modify environments on your instance. If problems persist after trying these solutions, consider reaching out to AWS support for platform-specific troubleshooting. Remember to save your work frequently and consider using smaller subsets of your data for initial development to avoid memory issues. **Sources** Running Jupyter Notebook Tutorials - AWS Deep Learning AMIs Jupyter Notebook QuickStart — AWS Neuron Documentation Building multi-tenant JupyterHub Platforms on Amazon EKS | Containers

Any suggestions to fix this issue? :slight_smile:

Have you checked the docker logs to see what the logs show? It might have some helpful information. docker logs [OPTIONS] CONTAINER

@PNSmith actually this is less a newbie question than you might think. When a kernel dies without any warning, this often means that your hardware is not supported or misconfigured. This happened for me when I was working in a virtual machine what I have discussed with the developers on github. In t…