---
title: "Use AI Labs as a learner"
description: "Find your AI Labs courses in TIR and work in them with instances, datasets, model repositories, endpoints, the Playground, integrations, and tokens."
content_type: "how-to"
product: "ai-labs"
platform: "TIR"
url: "/tir/ai-labs/how-to/use-ai-labs-as-a-learner/"
topic: ["learners"]
---

# Use AI Labs as a learner

Work in the courses an Admin adds you to.

## Before you begin

Make sure that an Admin has added you to an AI Labs organization and to the course you want to work in.

## View your courses and course material

Open your courses to read the material your Professor shared and download the files you need.

- Accept the invitation in the email you received. You see the organizations you belong to, and in each one only the
  courses you were added to.
- Sign in to AI Labs, which is available in Delhi.

### Check your dashboard

Your **Dashboard** gives an overview of your active resources, notifications, and courses:

- **Services Running** has your nodes, inferences, and datasets, with how many are running and the total.
- **Courses Overview** has the courses you were added to, each with its instructor and a short
  description. Select a course to open it.

### Open a course

1. In the left menu, select **Courses**.
2. Select a course. Its **Courseware** page lists the modules and study material.
3. Open a module and download its files, such as lecture PDFs, slides, or notes.

You can view and download course material. Your Admin or Professor uploads and changes it.

For example, in the course **Intro to Cloud Computing**, open Module 1 and download materials such as
`Module 1/06.pdf`.

Practise what you learn: [launch an instance](/tir/ai-labs/how-to/use-ai-labs-as-a-learner/#launch-an-instance). If a course is missing,
see [Troubleshooting AI Labs](/tir/ai-labs/troubleshooting/ai-labs/).

## Launch an instance

Launch an instance to get a ready-to-use workspace for AI and machine learning work, with containers, JupyterLab,
and popular AI frameworks included.

- Make sure that your group gives you **Create** access to **Node**, the service for instances.
- Check that the organization is running. You can create and start instances only while it runs.
- Check that you are within your instance limit for the plan you want. Your group sets how many instances you can
  run on each plan.
- Create or choose a dataset if you want one on the instance. See [Create a dataset](/tir/ai-labs/how-to/use-ai-labs-as-a-learner/#create-a-dataset).

### Launch the instance

1. In the left menu, select **Instances**, then **Create Instance**.
2. Choose an image. AI Labs instances use ready-made images.
3. Choose a plan with the GPU memory, RAM, and CPU you need, from the plans your groups allow. To narrow the choice,
   filter by CPU, RAM, or GPU memory.
4. Choose the workspace size, and choose an existing dataset or create a new one if you want it.
5. Enter a name for the instance and continue.
6. Review the summary and select **Launch**.

The instance appears in your **Instances** list and starts.

### If the instance does not launch

- **You have reached your allowed instance quota and cannot launch any additional resources at this time.** means you have
  used the limit your group gives you on this plan. Delete an instance you are finished with, or ask
  an Admin to raise the limit.
- **This AI Labs organization is currently stopped, so you cannot start this resource.** means an Admin stopped the
  organization. Ask the Admin to start it.

For more, see [Troubleshooting AI Labs](/tir/ai-labs/troubleshooting/ai-labs/).

Add data with [Create a dataset](/tir/ai-labs/how-to/use-ai-labs-as-a-learner/#create-a-dataset) or deploy a model with
[Create a model repository](/tir/ai-labs/how-to/use-ai-labs-as-a-learner/#create-a-model-repository).

## Create a dataset

Create a dataset to keep your data in one place and open it from your instances and training code.

- Make sure that your group gives you **Create** access to **Storage**.
- Decide the storage type. A dataset is backed by object storage or by disk storage.

You can create datasets even when an Admin has stopped the organization. Datasets that you create are yours, and they stay
hidden from other learners.

### Create the dataset

1. In the left menu, select **Dataset**, then **Launch Dataset**.
2. Enter a **dataset name** and choose the **storage type**.
3. Select **Create**.

The dataset appears in your list and is ready to mount on an instance.

Mount the dataset when you [launch an instance](/tir/ai-labs/how-to/use-ai-labs-as-a-learner/#launch-an-instance). For messages, see
[Troubleshooting AI Labs](/tir/ai-labs/troubleshooting/ai-labs/).

## Create a model repository

Create a model repository to store model weights and configuration files, so an endpoint can serve the model.

Make sure that your group gives you **Create** access to **Inference**.

### Create the repository

1. In the left menu, select **Inference**, then **Model Repository**.
2. Select **Create Repository**.
3. Enter a **repository name**, choose the model type, and choose the storage type: object storage or disk
   storage. If you choose an existing object storage bucket, AI Labs shows **Invalid storage type**.
4. Select **Create**.

Upload your model files, then [create a model endpoint](/tir/ai-labs/how-to/use-ai-labs-as-a-learner/#create-a-model-endpoint) that serves
them. For messages, see [Troubleshooting AI Labs](/tir/ai-labs/troubleshooting/ai-labs/).

## Create a model endpoint

Create a model endpoint to give your applications an API that sends data to your deployed model and gets inference
results back in real time.

- Make sure that your group gives you **Create** access to **Inference**.
- [Create a model repository](/tir/ai-labs/how-to/use-ai-labs-as-a-learner/#create-a-model-repository) that holds the model.
- Check that the organization is running and that you are within your instance limit for the plan you choose. An
  endpoint counts each replica against your limit.

### Create the endpoint

1. In the left menu, select **Inference**, then **Model Endpoints**.
2. Select **Create Endpoint**.
3. Choose the framework.
4. Select **Link with Model Repository** and choose your repository.
5. Choose a plan and continue.
6. (Optional) Set the LLM settings for a language model.
7. Set **Active Workers** and **Max Workers**.
8. (Optional) Add environment variables, then continue.
9. Check the summary. To change a step, select its edit button.
10. Select **Launch**.

An endpoint in AI Labs cannot use a shared file system or a private container.

When the replicas exceed your limit, AI Labs shows **You cannot select more than ... replicas as
you have reached your allowed instance quota**. Choose a smaller number.

Try the model with [the Playground](/tir/ai-labs/how-to/use-ai-labs-as-a-learner/#chat-with-a-model-in-the-playground). For messages, see
[Troubleshooting AI Labs](/tir/ai-labs/troubleshooting/ai-labs/).

## Chat with a model in the Playground

Chat with a deployed chat model in the Playground to try it before you connect an application to it.

[Create a model endpoint](/tir/ai-labs/how-to/use-ai-labs-as-a-learner/#create-a-model-endpoint) for a chat model and wait for it to run.

### Chat with the model

1. In the left menu, select **Inference**, then **Playground**.
2. Select a model.
3. Set the parameters you want to use.
4. Start generating responses.

To change how the model runs, see [Create a model endpoint](/tir/ai-labs/how-to/use-ai-labs-as-a-learner/#create-a-model-endpoint). If a message appears, see [Troubleshooting AI Labs](/tir/ai-labs/troubleshooting/ai-labs/).

## Create an integration

Create an integration to connect an external platform such as Hugging Face or Weights & Biases. Use it for
model training, tracking, and deployment on your AI Labs instances.

- Make sure that your group gives you **Create** access to **External Integration**.
- Have the token for the platform, for example a Hugging Face token.

### Create the integration

1. In the left menu, select **Integrations**, then **Create Integration**.
2. Choose the **Integration Type** from the list.
3. Enter your token, for example the Hugging Face token.
4. Select **Create**.

The integration is ready to use in your AI Labs work. If a message appears, see [Troubleshooting AI Labs](/tir/ai-labs/troubleshooting/ai-labs/).

## Create a personal access token

Create a personal access token from the **Personal Access Tokens** page.

Make sure that your group gives you **Create** access to **API Token**.

### Create the token

1. In the left menu, select **Personal Access Tokens**, then **Create Token**.
2. Enter a token name.
3. Select **Create**.

The token appears in your list. Only you see the tokens you create.

Keep the token private. If a message appears, see [Troubleshooting AI Labs](/tir/ai-labs/troubleshooting/ai-labs/).