Deploy Jupyter Notebook on Azure on Northflank

If you're working with GPU-intensive tasks—such as training deep learning models with TensorFlow, processing massive datasets, or performing real-time simulations—a Jupyter Notebook pre-configured with TensorFlow can dramatically streamline your workflow. This setup accelerates computation, shortens development cycles, and enables rapid experimentation directly within a familiar, browser-based interface.

When deployed through Northflank's BYOC (Bring Your Own Cloud, you gain the full flexibility of Jupyter's interactive environment along with the raw performance of NVIDIA GPU acceleration. It’s an ideal solution for AI development, scientific computing, and any high-throughput workload that demands efficient, scalable compute resources.

• An Azure BYOC cluster with 1 CPU nodepool and 1 GPU nodepool with spot instances (NVIDIA H100)
• 1 Jupyter Notebook with Tensorflow deployment
• 1 persistent volume to store data

• An Azure account

1. Create an account on Northflank
2. Click deploy Jupyter Notebook now
3. Select your Azure account, or integrate your Azure account with Northflank
4. Click deploy stack to save and run the Jupyter Notebook template
5. Select the jupyter service when the template run has finished
6. From the service dashboard, click the shell button on your running container
7. Run the command jupyter server list and the token returned as part of the URL
8. Open the code.run domain for your Jupyter service, shown in the service header
9. Paste the token into the Jupyter web interface to create a new password and log in

You can now use your Jupyter Notebook with Tensorflow with GPU acceleration. Try running the following command, and you should see a GPU device listed:

import tensorflow as tf 
tf.config.list_physical_devices('GPU')