Managing jobs in Serverless AI

Serverless AI jobs run container images as one-off or scheduled batch workloads. They are suitable for training, fine-tuning and data processing where you want to use computing resources only to perform a task and stop when the task is done. Each job runs on a container over a Compute virtual machine (VM) that is billed only while the job is running.

How to create a job

To run a container image as a batch workload for training, fine-tuning or data processing, create a job:

Web console
CLI

In the sidebar, go to AI Services → Jobs.
Click Create job.
In the Job settings section, specify the path to the container image. If you use a private registry, click Add registry, and provide the details for your registry.
(Optional) Add advanced settings:
- Entrypoint command: Specify an entrypoint command for the job container.
- Arguments: Override container arguments that are passed to the entrypoint.
- Job timeout in hours: Specify the number of hours after which the job will be canceled if not completed.
- Environment variables: Specify environment variables in key-value pairs.
(Optional) Configure the Computing resources section:
1. Select whether the VM should have GPUs.
2. Specify the VM type: regular or preemptible. VMs without GPUs only support the regular type.
3. Select the platform and preset.
(Optional) Configure Storage settings:
- Set the container disk size. See how disk performance depends on disk size.
- Attach a bucket or a filesystem to provide storage. You can create a bucket or filesystem, or use an existing one. To create a new bucket, see Bucket parameters. To create a new filesystem, see Volume parameters.
(Optional) In the Access section, add an SSH key for the VM’s user so you can connect to the VM. You can add new credentials or select existing ones. If you decide to use an existing credential, make sure that the SSH key is stored for the nebius username.
Review the Network section. The subnet is selected by default.
Click Create.

Run the following command:

nebius ai job create \
  --name <job_name> \
  --image <image:tag> \
  --registry-username <username> \
  --registry-password <password> \
  --container-command "<command>" \
  --args "<arguments>" \
  --env <key=value> \
  --working-dir <absolute_path> \
  --timeout <duration> \
  --platform <platform_ID> \
  --preset <preset> \
  --disk-size <size> \
  --volume <source:container_path|source:container_path:mode> \
  --subnet-id <subnet_ID> \
  --shm-size <size> \
  --ssh-key <SSH_public_key>

Job creation example

nebius ai job create \
  --name training-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 vpcsubnet-e***
  

In the command, specify the following parameters:

Job settings:
- --name: Job name.
- --image: Container image in the <registry>/<image>:<tag> format. Use an image from a public registry or your authenticated private registry.
- --registry-username, --registry-password (optional): If you pull an image from a private registry, specify your credentials for this registry:
  - --registry-username: Username.
  - --registry-password: Personal access token, password or an API key. Depends on where your registry is hosted. It can be Docker Hub, Microsoft Azure, GitHub, NVIDIA or a custom registry.
  If you pull an image from a public registry or from Container Registry in the same project, you don’t need to specify the credentials.
- --container-command (optional): Entrypoint command for the container.
- --args (optional): Arguments for docker run to pass to the entrypoint command.
- --env (optional): Environment variables for the container. Set them in the key=value format where the key is the environment variable and the value is the value of this variable. If you need to set several variables, list the key=value pairs separated by commas.
- --working-dir (optional): Working directory (absolute path).
- --timeout (optional): Job timeout (e.g., 2h30m10s, 24h). Minimum: 1h, maximum: 168h. Default: 24h.
- --volume (optional): Bucket or shared filesystem to mount to the job container and to store the job results and checkpoints. Volumes persist if the job is recreated after a maintenance event. Specify the value in the source:container_path or source:container_path:mode format. The supported modes are ro, read only, and rw, read-write (default). Repeat for multiple volumes. For example:
  --volume 'computefilesystem-e***:/input:ro' \ --volume 'storagebucket-e***:/output:rw'
Underlying container over VM characteristics:
- --subnet-id: Subnet ID. Required if the project has multiple subnets.
- --platform: VM platform. See available platforms in Types of virtual machines and GPUs in Nebius AI Cloud.
- --preset: Number of GPUs, vCPUs and RAM allocated to the container. The preset must match the selected platform. See available presets in Presets for GPU platforms.
- --disk-size: Disk size of the container over VM. Specify the value such as 100Gi, 500Gi or 1Ti. The default value is 250Gi. See how disk performance depends on disk size.
- --shm-size (optional): Shared memory size of /dev/shm. Specify the value such as 64Mi, 128Mi or 1Gi. The default value is 16Gi.
- --ssh-key (optional): SSH key to access the container over VM by SSH. When you add an SSH key, a public dynamic IP address is assigned. Before you add the key, check the quota on the number of public IP addresses in the web console.

The job creation usually takes a few minutes. Jobs run until the workload finishes. When the job completes successfully or fails, the container over VM is deleted automatically. If you mounted volumes, they will remain, and you should delete them manually.

How to check job logs

To view logs from a running or completed job:

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.

You can use the period or log level filters to filter the logs. You can also use the LogQL query language.

Run the following command:

 nebius ai job logs <job_ID> --follow

You can add the following options to control the output:

--follow or -f: Stream logs in real time.
--since <value>: Show logs starting from the specified time. For example, 1h (from 1 hour ago), 30m (from 30 minutes ago) or 2024-01-01 (from that date).
--tail <value>: Number of recent lines to show in the output.
--timestamps: Include timestamps in the output.
--until <value>: Show logs up to the specified time. For example, 1h (up to 1 hour ago), 30m (up to 30 minutes ago) or 2024-01-01 (up to that date).

How to cancel a job

If you don’t need a job to continue running, you can cancel it. The jobs that finish with COMPLETED status are canceled automatically. To cancel a job:

Web console
CLI

In the sidebar, go to AI Services → Jobs.
Find the job and then click → Cancel.
In the window that opens, confirm canceling the job.

List jobs:
```
nebius ai job list
```
In the output, copy the ID of the required job.
To cancel a job, run:
```
nebius ai job cancel <job_ID>
```

Canceling a job immediately stops the container over VM and deletes the container disk. The job remains in the list of jobs. Mounted volumes are retained. You can remove the mounted volumes manually. See the guides on deleting a filesystem and deleting a bucket.

How to delete a job

To delete a job:

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.

List jobs:
```
nebius ai job list
```
In the output, copy the ID of the required job.
Delete the job:
```
nebius ai job delete <job_ID>
```

When the job is deleted, it disappears from the list of jobs. If a job is running, deleting cancels the job first. If the job uses additional volumes, they are not deleted with it. You can remove the mounted volumes manually. See the guides on deleting a filesystem and deleting a bucket.

Serverless AI

Managed Service for MLflow

Applications in Nebius AI Cloud

Tutorials

Third-party integrations

Managing jobs in Serverless AI

How to create a job

How to check job logs

How to cancel a job

How to delete a job

Serverless AI

Managed Service for MLflow

Applications in Nebius AI Cloud

Tutorials

Third-party integrations

​How to create a job

​How to check job logs

​How to cancel a job

​How to delete a job

How to create a job

How to check job logs

How to cancel a job

How to delete a job