Skip to main content
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 Compute container virtual machine (VM) managed by Serverless AI and billed only while the job is running.

​
Prerequisites

Make sure you are in a group that has at least the editor role within your tenant or project; for example, the default editors group. You can check this in the Administration → IAM section of the web console.

​
How to create a job

To run a container image as a batch workload for training, fine-tuning or data processing, create a job:
  1. In the sidebar, go to  Serverless AI → Jobs.
  2. Click  Create job.
  3. In Configuration, select Custom.
  4. Configure Job settings:
    1. In Image path, set the path to the container image.
    2. If you use a private registry, in Private registry, select an existing registry or click  Add, and provide the details for your registry.
    3. (Optional) In Entrypoint command, specify the entrypoint command for the container. If you need to pass container arguments, specify them in this field as well.
    4. (Optional) In Environment variables, specify environment variables in key-value pairs.
    5. (Optional) In Secret environment variables, click  Create secret to store a sensitive value in SecretStash and inject it as an environment variable. In the window that opens, specify the Key (environment variable name) and Value (secret data), then click Create.
    6. (Optional) In Job timeout in hours, specify the number of hours after which the job will be canceled if not completed.
  5. (Optional) Configure the Computing resources section:
    1. Select whether the container 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.
    4. If you selected the preemptible VM type, in Max price, select or create a pricing policy, or choose Follow spot price.
  6. (Optional) Configure Storage settings:
  7. (Optional) In the Files section, add one or more small configuration files to inject into the container at launch:
    • Select Upload files to upload files from your machine, or Create text files to enter the file content and click Create file.
    • In Mount path, specify the absolute path to the file in the container, for example, /mnt/files/config.yaml. Injected files are read-only and limited to 64 KiB each.
  8. (Optional) In the Access section, configure User name and SSH key to connect to the running workload for debugging. You can create 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.
  9. Configure the Network section:
    • Select a subnet or create a new one.
    • Select the IP address type: Public static IP or Private IP. If you want to connect to the resource from the internet, select Public static IP.
  10. Click Create job.
The job creation usually takes a few minutes. Jobs run until the workload finishes. When the job completes successfully or fails, the container VM is deleted automatically. If you mounted volumes, they will remain, and you should delete them manually.

​
How to check job logs

For this operation, it’s enough to be in a group that has the viewer role within your tenant; for example, the default viewers group. You can check this in the Administration → IAM section of the web console.
Checking job logs is available only in the web console and CLI.
  1. In the sidebar, go to  Serverless AI → Jobs.
  2. 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.

​
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:
  1. In the sidebar, go to  Serverless AI → Jobs.
  2. Find the job and then click  → Cancel.
  3. In the window that opens, confirm canceling the job.
Canceling a job immediately stops the container 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. If you need to remove any record about the job from the job list, delete the job instead of canceling it.

​
How to delete a job

To delete a job:
  1. In the sidebar, go to  Serverless AI → Jobs.
  2. Locate the job and then click  → Delete.
  3. In the window that opens, confirm deleting the job.
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.