Skip to main content

Learner

Dashboard​

The Dashboard provides learners with an overview of their active resources, notifications, and courses.

Dashboard

Services Running​

Learners can view the status of services associated with their account, including:

  • Nodes
  • Inferences
  • Datasets

Each service displays the count of active and total resources.

Courses Overview​

All available courses under the learner’s Organization are displayed here.
Each course card includes:

  • Course title
  • Instructor’s name
  • Short description

Learners can click on any course to explore the detailed course modules and materials.

Courses​

Learners can access their enrolled courses by clicking on the Courses option from the left-side navigation menu.

Courses

Each course card displays:

  • Course Name
  • Instructor Name
  • Course Description

Learners can click on the course card to open detailed course content.

Courseware and Modules​

After selecting a course, learners are directed to the Courseware page, which displays all course modules and study materials.

Courseware

Each course is divided into modules, and every module may contain downloadable files such as PDFs, slides, or notes.

Learners can:

  • View all available Modules
  • Download attached learning materials (e.g., lecture PDFs)

Example​

For the course Intro to Cloud Computing, learners can access Module 1 and download materials like Module 1/06.pdf.

Access Permissions​

Note
  • Learners can view and download course materials shared by the instructor.
  • Learners cannot upload or modify course content.
  • All courses and materials are managed and updated by the Admin or Professor.

Platform Services​

Instances (Nodes)​

Instances provide collaborative, ready-to-use environments for AI and ML development. Each instance brings together containers, JupyterLab, and popular AI frameworks—giving you and your team a powerful workspace that works out of the box.

Steps to create an instance​

Navigate to Instances section from the sidebar and then click on Create Instance button.

Instance Dashboard

Select the desired image and then click on Next button.

Select Image

Select the plan according to your need and then click on Next button.

Select Plan

Select the workspace size based on your needs, and either choose an existing dataset or create a new one as required.

Select Storage

Enter the desired name for your instance and then click on Next button.

Name Node

Click on Launch button to launch the new instance.

Launch Node

Datasets​

Datasets allow you to organize, share, and easily access your data directly within your notebooks and training code. Currently, TIR supports datasets backed by EOS (Object Storage) and PVC-backed datasets with Disk Storage.

Steps to create dataset​

Navigate to Dataset section from the sidebar and then click on Launch Dataset button.

Dataset Dashboard

Enter the dataset name, select the storage type, and then click the Create button.

Dataset Create

Inference​

Model Repository​

TIR Model Repositories are designed to store model weights and configuration files. These repositories can be backed by either E2E Object Storage (EOS) or PVC storage within a Kubernetes environment.

Steps to create Model Repository​

Navigate to Inference section from the sidebar and then select Model Repository. Then click on Create Repository button.

Model Repository Dashboard

Enter the repository name, select the model type and storage type, and then click the Create button.

Model Repository Dashboard

Model Endpoints​

Model endpoints are dedicated API interfaces that allow applications to send data to a deployed AI model and receive inference results in real time. They serve as the bridge between your trained model and external systems, enabling seamless integration of AI predictions into your applications or services.

Steps to create Model Endpoints​

Navigate to Inference section from the sidebar and then select Model Endpoints. Then click on Create Endpoint button.

Model Endpoint Dashboard

Choose the desired framework.

Model Endpoint frameworks

Click on Link with Model Repository and select from Model Repository.

Model Endpoint Download

Select the desired plan and then click on Next button.

Model Endpoint Plans

Configure the LLM settings if you want.

Model Endpoint LLM Settings

Select the Active Workers and Max Workers count according to your need. Then click on Next button.

Model Endpoint Worker Settings

Add environment variables if you want. Then click on Next button.

Model Endpoint Env variables

You can view the endpoint summary on the summary page, and if needed, modify any section of the inference creation process by clicking the edit button. Click on Launch button.

Model Endpoint Summary

Playground​

You can interact with our chat models directly via the Playground. Select a model, configure the parameters, and start generating responses.

Playground

Integrations​

E2E Cloud supports integration with popular ML platforms like Hugging Face and Weights & Biases, enabling users to easily connect external tools for model training, tracking, and deployment. These integrations help streamline AI workflows, improve collaboration, and enhance experiment management on GPU-powered E2E instances.

Steps to create Integrations​

Navigate to Integrations section from the sidebar and then click on Create Integration button.

Integration Dashboard

Select the Integration Type from the given list.

Integration type list

Enter the Hugging face token and then click on Create button.

Integration create

Personal Access Tokens​

Steps to create Token​

Navigate to Personal Access Tokens section from the sidebar and then click on Create Token button.

PAT dashboard

Enter the token name and then click on Create button.

PAT create

Summary​

Through the Learner View, students can:

  • Access their Dashboard for quick insights
  • Explore assigned Courses
  • View and download Course Materials