How to deploy a Jupyter Notebook pre-installed with TensorFlow?

Spheron allows you to deploy a password protected Jupyter Notebook instance pre-installed with TensorFlow, all set up and ready to use on the Akash Network. To deploy a TensorFlow GPU:

1. Click "New Cluster" on the top right corner.
2. Choose "Accelerate" to leverage GPU-powered computing for enhanced performance.
3. Select Start from Marketplace App.
4. Pick TensorFlow GPU from the marketplace.
5. Select your preferred Region, if any. If you do not add a region, the container will be deployed in any region for Spot, or in the eu-east region for On Demand. Click here to know more.
6. Spheron will automatically select the recommended plan for the specific template. If you intend to move forward with the recommended plan, just Click "Deploy" to initiate deployment.
7. Select the instance plan that suits your needs. Our offerings include a range of GPU plans, though certain GPU plans may be out of stock and require reaching out to our team (opens in a new tab) for access.
8. Configure Storage (SSD) plan for your instance. Use the "Add Persistent Storage" toggle to add persistent storage for your instance.
9. You can add advanced configuration if required. Click here to know more.
10. Click "Deploy" to initiate deployment.

Deploy Your Own

Deploy your own Jupyter Notebook pre-installed with TensorFlow with Spheron:

(opens in a new tab)

Verify Installation

The Jupyter Notebook dashboard can be accessed only after the Compute Instance is provisioned. Thus, you need to wait for the installation to complete before you can start using it.

How to connect to Jupyter Notebook?

• Use the Connection URL provided by Spheron under Port Policy Info to access Jupyter Notebook.
• Use the token from the instance logs to unlock the dashboard.

For more information, refer to the TensorFlow docs (opens in a new tab).