Overview
Typical end users of GPU PaaS are data scientists, ML researchers or developers. Once the Org Admin provides the user with the appropriate role, they will have the ability to login into the self service portal, create and manage GPU workspaces, request for GPUs, compute, deploy and operate Jupyter notebooks and other services made available by the administrator or service provider. One of the goals of GPU PaaS service is to enable IT/Operations or Platform teams to empower these users with an On Demand experience to get access to a fully operational and ready to use environment where they can perform their tasks efficiently.
In the example below, the "data scientist" and the "developer are different users in the organization. Both have access to the same "Self Service Portal". Note that the data scientist has done the following:
- Has created one workspace
- Has launched two GPU powered "compute instances" in this workspace
- Has deployed a LLM fine tuning service into one instance
- Has deployed a Jupyter notebook into the other instance for model development
Access Developer Hub¶
To access the Developer Hub from the console, click Developer Hub, as shown in the image below.
The Developer Hub page provides a unified interface for end users to manage AI/ML workflows efficiently. Users can organize resources using Workspaces, launch and manage Compute Instances, develop models in Notebooks, deploy them via Inference Endpoints, and track tasks under AI/ML Jobs. Additionally, they can define and integrate Custom Services for specific project needs, enabling seamless collaboration, resource optimization, and streamlined deployment within a project-oriented structure.
