# Learner

## Dashboard

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

![Dashboard](images/learner_view.png)

### 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](images/learner_course_view.png)

Each course card displays:
- **Course Name**  
- **Instructor Name**  
- **Course Description**

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

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### Courseware and Modules

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

![Courseware](images/learner_module_view.png)

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

:::info 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**.
:::

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## 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](images/create_instance_01.png)

Select the desired **image** and then click on **Next** button.

![Select Image](images/create_instance_02.png)

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

![Select Plan](images/create_instance_03.png)

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

![Select Storage](images/create_instance_04.png)

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

![Name Node](images/create_instance_05.png)

Click on **Launch** button to launch the new instance.

![Launch Node](images/create_instance_06.png)


### 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](images/create_dataset_01.png)

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

![Dataset Create](images/create_dataset_02.png)


### 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](images/create_model_repo_01.png)

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

![Model Repository Dashboard](images/create_model_repo_02.png)

#### 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](images/create_model_endpoint_01.png)

Choose the desired **framework**.

![Model Endpoint frameworks](images/create_model_endpoint_02.png)

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

![Model Endpoint Download](images/create_model_endpoint_03.png)

Select the desired plan and then click on **Next** button.

![Model Endpoint Plans](images/create_model_endpoint_04.png)

Configure the LLM settings if you want.

![Model Endpoint LLM Settings](images/create_model_endpoint_05.png)

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

![Model Endpoint Worker Settings](images/create_model_endpoint_06.png)

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

![Model Endpoint Env variables](images/create_model_endpoint_07.png)

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](images/create_model_endpoint_08.png)

#### Playground

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

![Playground](images/playground.png)

### 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](images/create_integrations_01.png)

Select the **Integration Type** from the given list.

![Integration type list](images/create_integrations_02.png)

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

![Integration create](images/create_integrations_03.png)

### 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](images/create_pat_01.png)

Enter the token name and then click on **Create** button.

![PAT create](images/create_pat_02.png)


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## Summary

Through the **Learner View**, students can:
- Access their **Dashboard** for quick insights  
- Explore assigned **Courses**  
- View and download **Course Materials**   





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