Getting started with Serverless AI jobs: Run nvidia-smi within a job

You can use Serverless AI jobs to work with AI models and perform such operations as fine-tuning, scientific simulations, data processing or batch inference. To get started with jobs, use the example below. It demonstrates how to run nvidia-smi in a Serverless AI job. As a result, you receive information about the GPUs of the container over a virtual machine in which the job is running. In the web console, Quick start Serverless job runs the same kind of workload with settings already filled in.

Prerequisites

The preparations for this guide depend on your preferred interface.

Web console
CLI

Make sure you are in a group that has the admin role within your tenant; for example, the default admins group. You can check this in the Administration → IAM section of the web console.
On the Administration → Limits → Quotas page of the web console, check the Number of virtual machines (VMs) quota, under Compute: it should have at least one VM available. If necessary, increase the quota.

Make sure you are in a group that has the admin role within your tenant; for example, the default admins group. You can check this in the Administration → IAM section of the web console.
On the Administration → Limits → Quotas page of the web console, check the Number of virtual machines (VMs) quota, under Compute: it should have at least one VM available. If necessary, increase the quota.
Install and configure the Nebius AI Cloud CLI. Check that your project ID is saved in the Nebius AI Cloud CLI profile configuration:
```
cat ~/.nebius/config.yaml
```

Steps

Run a Serverless AI job

Web console
CLI

In the sidebar, go to AI Services → Jobs.
Click Quick start Serverless job. The flow uses a verified container image and prefilled job settings.
Click Create job.

Get the ID of the default subnet where you create a job:

nebius vpc subnet get-by-name --name default-subnet \
  --format jsonpath='{.metadata.id}'

Create a job:
```
nebius ai job create \
  --name my-job \
  --image nvidia/cuda:13.1.1-runtime-ubuntu24.04 \
  --container-command bash \
  --args "-c nvidia-smi" \
  --platform gpu-l40s-a \
  --preset 1gpu-8vcpu-32gb \
  --timeout 1h \
  --subnet-id <subnet_ID>
```
The command contains the following parameters:
- --name: Job name.
- --image: Container image to run. In the given example, the nvidia/cuda:13.1.1-runtime-ubuntu24.04 is used. It contains the Ubuntu 24.04 operating system as well as drivers and other components for GPUs.
- --container-command: Terminal shell for commands to run.
- --args: Arguments for docker run to pass to the entrypoint command. The specified arguments require the container to run nvidia-smi.
- --platform: VM platform for the job. As Serverless AI jobs are based on containers over VMs, every job uses Compute platforms and presets.
- --preset: Number of GPUs, vCPUs and RAM allocated to the container. The preset must match the selected platform.
- --timeout: Duration after which the job is canceled if it has not completed.
- --subnet-id: Subnet ID.

The job takes several minutes to complete. While the job is running, you can check its status and logs.

Check the job results

Web console
CLI

In the sidebar, go to AI Services → Jobs.
Next to the job, click View logs. Alternatively, select the job that you want to view the logs for and switch to the Logs tab.

Save the job ID to an environment variable:

export JOB_ID=$(nebius ai job get-by-name --name my-job \
  --format jsonpath='{.metadata.id}')

View information about the job:
```
nebius ai job get $JOB_ID
```
The command output contains such data as the job status, start and finish times.
If the output doesn’t indicate that the job has started, wait a few seconds. Next, run the previous command again and check that the job is running now.
When the job has started, check its logs:
```
nebius ai job logs $JOB_ID
```
The logs contain information about the GPUs that nvidia-smi provides (in other words, the output of the nvidia-smi command).

You can run nebius ai job get $JOB_ID and nebius ai logs $JOB_ID multiple times to determine whether the job is complete, and the full output of nvidia-smi is returned in logs.

(Optional) Delete the job

When the job is complete, Serverless AI automatically releases all allocated computing resources and the container disk of the job. When you delete a complete job, you remove it from the list of Serverless AI jobs. If you delete a running job, all the resources are released as well.

Web console
CLI

In the sidebar, go to AI Services → Jobs.
Locate the job and then click → Delete.
In the window that opens, confirm deleting the job.

To delete the job, run the following command:

nebius ai job delete $JOB_ID

Serverless AI

Managed Service for MLflow

Applications in Nebius AI Cloud

Tutorials

Third-party integrations

Getting started with Serverless AI jobs: Run nvidia-smi within a job

Prerequisites

Steps

Run a Serverless AI job

Check the job results

(Optional) Delete the job

Serverless AI

Managed Service for MLflow

Applications in Nebius AI Cloud

Tutorials

Third-party integrations

​Prerequisites

​Steps

​Run a Serverless AI job

​Check the job results

​(Optional) Delete the job

Prerequisites

Steps

Run a Serverless AI job

Check the job results

(Optional) Delete the job