# Run Your First Standard Training

This article provides a step-by-step walkthrough for running a standard training workload.

A training workload contains the setup and configuration needed for building your model, including the container, images, data sets, and resource requests, as well as the required tools for the research, all in a single place.

## Prerequisites

Before you start, make sure:

* You have created a [project](https://run-ai-docs.nvidia.com/self-hosted/2.20/platform-management/aiinitiatives/organization/projects) or have one created for you.
* The project has an assigned quota of at least 1 GPU.

## Step 1: Logging In

{% tabs %}
{% tab title="UI" %}
Browse to the provided NVIDIA Run:ai user interface and log in with your credentials.
{% endtab %}

{% tab title="CLI v2" %}
Log in using the following command. You will be prompted to enter your username and password:

```sh
runai login --help
```

{% endtab %}

{% tab title="CLI v1 (Deprecated)" %}
Log in using the following command. You will be prompted to enter your username and password:

```sh
runai login
```

{% endtab %}

{% tab title="API" %}
To use the API, you will need to obtain a token as shown in [API authentication](https://app.gitbook.com/s/b5QLzc5pV7wpXz3CDYyp/getting-started/how-to-authenticate-to-the-api).
{% endtab %}
{% endtabs %}

## Step 2: Submitting a Standard Training Workload

{% tabs %}
{% tab title="UI" %}

1. Go to the Workload manager → Workloads
2. Click **+NEW WORKLOAD** and select **Training**
3. Select under which **cluster** to create the workload
4. Select the **project** in which your workload will run
5. Under **Workload architecture**, select **Standard**
6. Select **Start from scratch** to launch a new workload quickly
7. Enter a **name** for the standard training workload (if the name already exists in the project, you will be requested to submit a different name)
8. Click **CONTINUE**

   In the next step:
9. Create an environment for your workload

   * Click **+NEW ENVIRONMENT**
   * Enter **quickstart** as the name
   * Enter `runai.jfrog.io/demo/quickstart` as the **Image URL**
   * Click **CREATE ENVIRONMENT**

   The newly created environment will be selected automatically
10. Select the **‘one-gpu’** compute resource for your workload (GPU devices: 1)

    * If ‘one-gpu’ is not displayed in the gallery, follow the below steps:
      * Click **+NEW COMPUTE RESOURCE**
      * Enter a **name** for the compute resource. The name must be unique.
      * Set **GPU devices per pod** - 1
      * Set **GPU memory per device**
        * Select **% (of device) -** Fraction of a GPU device’s memory
        * Set the memory **Request** - 100 (the workload will allocate 100% of the GPU memory)
      * Optional: set the **CPU compute per pod** - 0.1 cores (default)
      * Optional: set the **CPU memory per pod** - 100 MB (default)
      * Click **CREATE COMPUTE RESOURCE**

    The newly created compute resource will be selected automatically
11. Click **CREATE TRAINING**
    {% endtab %}

{% tab title="CLI v2" %}
Copy the following command to your terminal. Make sure to update the below with the name of your project and workload. For more details, see [CLI reference](https://run-ai-docs.nvidia.com/self-hosted/2.20/reference/cli/runai):

<pre class="language-sh"><code class="lang-sh">runai project set "project-name"
<strong>runai training submit "workload-name" -i runai.jfrog.io/demo/quickstart -g 1
</strong></code></pre>

{% endtab %}

{% tab title="CLI v1 (Deprecated)" %}
Copy the following command to your terminal. Make sure to update the below with the name of your project and workload. For more details, see [CLI reference](https://docs.run.ai/latest/Researcher/cli-reference/Introduction/):

```sh
runai config project "project-name" 
runai submit "workload-name" -i runai.jfrog.io/demo/quickstart -g 1
```

{% endtab %}

{% tab title="API" %}
Copy the following command to your terminal. Make sure to update the below parameters according. For more details, see [Trainings](https://app.gitbook.com/s/b5QLzc5pV7wpXz3CDYyp/workloads/trainings) API:

```bash
curl -L 'https://<COMPANY-URL>/api/v1/workloads/training' \
-H 'Content-Type: application/json' \ 
-H 'Authorization: Bearer <TOKEN>' \   
-d '{  
    "name": "workload-name",  
    "projectId": "<PROJECT-ID>", 
    "clusterId": "<CLUSTER-UUID>", 
    "spec": {
        "image": "runai.jfrog.io/demo/quickstart", 
        "compute": { 
            "gpuDevicesRequest": 1
        } 
    } 
 }
```

* `<COMPANY-URL>` - The link to the NVIDIA Run:ai user interface
* `<TOKEN>` - The API access token obtained in [Step 1](#step-1-logging-in)
* `<PROJECT-ID>` - The ID of the Project the workload is running on. You can get the Project ID via the [Get Projects](https://app.gitbook.com/s/b5QLzc5pV7wpXz3CDYyp/organizations/projects#get-api-v1-org-unit-projects) API.
* `<CLUSTER-UUID>` - The unique identifier of the Cluster. You can get the Cluster UUID via the [Get Clusters](https://app.gitbook.com/s/b5QLzc5pV7wpXz3CDYyp/organizations/clusters#get-api-v1-clusters) API.

{% hint style="info" %}
**Note**

The above API snippet runs with NVIDIA Run:ai clusters of 2.18 and above only.
{% endhint %}
{% endtab %}
{% endtabs %}

## Next Steps

* Manage and monitor your newly created workload using the [Workloads](https://run-ai-docs.nvidia.com/self-hosted/2.20/workloads-in-nvidia-run-ai/workloads) table.
* After validating your training performance and results, deploy your model using [inference.](https://run-ai-docs.nvidia.com/self-hosted/2.20/workloads-in-nvidia-run-ai/using-inference)