> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nebius.com/llms.txt
> Use this file to discover all available pages before exploring further.

# 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 Compute [container virtual machine](/compute/virtual-machines/containers) (VM) managed by Serverless AI and billed only while the job is running.

## Prerequisites

<Tabs group="interfaces">
  <Tab title="Web console">
    Make sure you are in a [group](/iam/authorization/groups/index) 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](https://console.nebius.com/iam) section of the web console.
  </Tab>

  <Tab title="CLI">
    * [Install and configure](/cli/install) the Nebius AI Cloud CLI.

      Check that your project ID is saved in the Nebius AI Cloud CLI profile configuration:

      ```bash theme={null}
      cat ~/.nebius/config.yaml
      ```

    * Make sure you are in a [group](/iam/authorization/groups/index) 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](https://console.nebius.com/iam) section of the web console.
  </Tab>

  <Tab title="REST API">
    * Get an access token to [authenticate to the REST API](/rest-api/authentication).

    * Make sure you are in a [group](/iam/authorization/groups/index) 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](https://console.nebius.com/iam) section of the web console.
  </Tab>
</Tabs>

## How to create a job

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

<Tabs group="interfaces">
  <Tab title="Web console">
    1. In the sidebar, go to <Icon icon="https://mintcdn.com/nebius-ai-cloud/rOlLZ_MFvrheaI-h/_assets/sidebar/serverless-ai.svg?fit=max&auto=format&n=rOlLZ_MFvrheaI-h&q=85&s=4c90b50c6c33e6bff000b87b8e37765a" width="16" height="16" data-path="_assets/sidebar/serverless-ai.svg" /> **Serverless AI** → **Jobs**.

    2. Click <Icon icon="https://mintcdn.com/nebius-ai-cloud/1Ha0sWR6e1mnIaHS/_assets/plus.svg?fit=max&auto=format&n=1Ha0sWR6e1mnIaHS&q=85&s=7c9efc69d65fc58db0eb73702fd81aa1" width="16" height="16" data-path="_assets/plus.svg" /> **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 <Icon icon="https://mintcdn.com/nebius-ai-cloud/1Ha0sWR6e1mnIaHS/_assets/plus.svg?fit=max&auto=format&n=1Ha0sWR6e1mnIaHS&q=85&s=7c9efc69d65fc58db0eb73702fd81aa1" width="16" height="16" data-path="_assets/plus.svg" /> **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 <Icon icon="https://mintcdn.com/nebius-ai-cloud/1Ha0sWR6e1mnIaHS/_assets/plus.svg?fit=max&auto=format&n=1Ha0sWR6e1mnIaHS&q=85&s=7c9efc69d65fc58db0eb73702fd81aa1" width="16" height="16" data-path="_assets/plus.svg" /> **Create secret** to store a sensitive value in [SecretStash](/mysterybox/overview) 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](/compute/virtual-machines/preemptible).

          VMs without GPUs only support the regular type.

       3. Select the [platform and preset](/compute/virtual-machines/types).

    6. (Optional) Configure **Storage** settings:

       * Under **Container disk**, set **Size**. See how [disk performance depends on disk size](/compute/storage/types#disk-performance).
       * Under **Mounted volumes**, click **Attach volume** to attach a bucket or a filesystem. You can create a bucket or filesystem, or use an existing one. To create a new bucket, see [Bucket parameters](/object-storage/buckets/manage#bucket-parameters). To create a new filesystem, see [Volume parameters](/compute/storage/manage#volume-parameters).

    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](/serverless/jobs/failure#connect-to-the-container-by-using-ssh).

       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**.
  </Tab>

  <Tab title="CLI">
    <Tip>
      With the command below, you specify all values directly in the command. This is useful for scripts, configuration-driven workflows, continuous integration (CI) and agents. Alternatively, you can run `nebius ai create` to use the interactive flow where the CLI prompts you for parameter values step by step.
    </Tip>

    Run the following command:

    ```bash theme={null}
    nebius ai job create \
      --name <job_name> \
      --image <image_path> \
      --registry-username <username> \
      --registry-password <password> \
      --container-command "<command>" \
      --args "<arguments>" \
      --env <key=value> \
      --env-secret <key=secret_selector> \
      --working-dir <absolute_path> \
      --timeout <duration> \
      --platform <platform_ID> \
      --preset <preset> \
      --disk-size <size> \
      --volume <source:container_path[:mode]|s3://bucket:/container_path[:mode[:profile]]> \
      --inject-file "<local_path>:<absolute_container_path>" \
      --subnet-id <subnet_ID> \
      --shm-size <size> \
      --ssh-key <SSH_public_key> \
      --preemptible
    ```

    <Accordion title="Job creation example">
      ```bash theme={null}
      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 \
        --inject-file "config.yaml:/workspace/config.yaml" \
        --subnet-id vpcsubnet-e***
      ```
    </Accordion>

    In the command, specify the following parameters:

    * **Job settings:**

      * `--name`: Job name.

      - `--image`: Container image reference in the `registry/path:tag` or `registry/path@digest` format. Use an image from a public registry or your authenticated private registry.

      - `--registry-username`, `--registry-password` (optional): Credentials to authenticate if you pull an image from a private registry. Alternatively, use `--registry-secret` for credentials stored in [SecretStash](/mysterybox/index).

        * `--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](/container-registry/index) in the same project, you don't need to specify credentials.

      - `--registry-secret` (optional): [SecretStash secret](/mysterybox/overview#secrets-and-versions) selector with `registry_username` and `registry_password` payload keys. You can specify a secret name, secret ID, version ID or a combined secret/version selector such as `mbsec-e00***@mbsecver-e00***`.

      - `--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 `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.

      - `--env-secret` (optional): Environment variables loaded from a [SecretStash secret](/mysterybox/overview#secrets-and-versions). Set them in the `key=secret_selector` format, where `key` is the environment variable name and must match a [payload](/mysterybox/overview#secrets-and-versions) key in the secret, and `secret_selector` is a secret name, secret ID, version ID or a combined secret/version selector such as `mbsec-e00***@mbsecver-e00***`. If you need to set several variables, list the pairs separated by commas. You cannot use the same `key` in both `--env` and `--env-secret`.

      * `--working-dir` (optional): Working directory (absolute path).
      * `--timeout` (optional): Job timeout (for example, `2h30m10s`, `24h`). Minimum: `1h`, maximum: `168h`. Default: `24h`.
      * `--inject-file` (optional): Mount a local file into the job container at launch. Use the format `<local_path>:<absolute_container_path>`. To inject multiple files, repeat the parameter. The mounted file is read-only and limited to 64 KiB.
      * `--volume` (optional): [Bucket](/object-storage/overview#buckets) or [shared filesystem](/compute/storage/types#shared-filesystems) 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](/compute/virtual-machines/maintenance).

        Specify the value in either format:

        * `source:container_path[:mode]` for mounting Nebius shared filesystems and existing bucket or volume resources by ID or name.
        * `s3://bucket:/container_path[:mode[:profile]]` for mounting an Object Storage bucket with AWS profile credentials or S3 credentials stored in SecretStash. The `profile` is the AWS credentials profile to use. If you manage your credentials with [SecretStash](/mysterybox/overview), use `profile@<secret_selector>`, where `<secret_selector>` is a secret name, secret ID, version ID or a combined secret/version selector such as `mbsec-e00***@mbsecver-e00***`

        The supported modes are `ro`, read only, and `rw`, read-write (default). Repeat for multiple volumes. For example:

        ```bash theme={null}
        --volume 'computefilesystem-e***:/input:ro' \
        --volume 'storagebucket-e***:/output:rw' \
        --volume 's3://training-results:/output:rw:default'
        ```

    * **Underlying container VM characteristics:**

      * `--subnet-id`: [Subnet ID](/vpc/networking/resources#how-to-get-a-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](/compute/virtual-machines/types).

      * `--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](/compute/virtual-machines/types#presets-for-gpu-platforms).

      * `--disk-size`: Disk size of the container VM. Specify the value such as `100Gi`, `500Gi` or `1Ti`. The default value is `250Gi`.

        See how [disk performance depends on disk size](/compute/storage/types#disk-performance).

      * `--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 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](https://console.nebius.com/quota).

      * `--preemptible` (optional): Use a [preemptible VM](/compute/virtual-machines/preemptible). Preemptible VMs are cheaper but can be stopped by Compute at any time. Only GPU platforms offer preemptible VMs. If you omit this parameter, the container runs on a regular VM.
  </Tab>

  <Tab title="REST API">
    Send the following request:

    ```bash theme={null}
    curl --request POST \
      --url 'https://api.nebius.cloud/ai/v1/jobs' \
      --header "Authorization: Bearer <access_token>" \
      --header 'Content-Type: application/json' \
      --data '{
        "metadata": {
          "parentId": "<project_ID>",
          "name": "my-job-1"
        },
        "spec": {
          "image": "<image_path>",
          "registryCredentials": {"username": "<username>", "password": "<password>"},
          "containerCommand": "<command>",
          "args": "<arguments>",
          "environmentVariables": [
            {
              "name": "<environment_variable_name>",
              "value": "<environment_variable_value>"
            }
          ],
          "workingDir": "<absolute_path>",
          "timeout": "<duration>",
          "platform": "<platform_ID>",
          "preset": "<preset>",
          "disk": {
            "type": "NETWORK_SSD",
            "sizeBytes": "<disk_size_in_bytes>"
          },
          "volumes": [
            {
              "source": "<volume_ID_or_name>",
              "containerPath": "<absolute_container_path>",
              "mode": "<READ_ONLY_or_READ_WRITE>"
            }
          ],
          "injectedFiles": [
            {
              "containerPath": "<absolute_container_path>",
              "content": "<base64_encoded_file_content>"
            }
          ],
          "shmSizeBytes": "<shared_memory_size_in_bytes>",
          "sshAuthorizedKeys": [
            "<SSH_public_key>"
          ],
          "subnetId": "<subnet_ID>",
          "preemptible": true
        }
      }'

    ```

    <Accordion title="Job creation example">
      ```bash theme={null}
      curl --request POST \
        --url 'https://api.nebius.cloud/ai/v1/jobs' \
        --header "Authorization: Bearer <access_token>" \
        --header 'Content-Type: application/json' \
        --data '{
          "metadata": {
            "parentId": "<project_ID>",
            "name": "training-job"
          },
          "spec": {
            "image": "nvidia/cuda:13.1.1-runtime-ubuntu24.04",
            "containerCommand": "bash",
            "args": "-c nvidia-smi",
            "platform": "gpu-l40s-a",
            "preset": "1gpu-8vcpu-32gb",
            "timeout": "3600s",
            "subnetId": "vpcsubnet-e***",
            "disk": {
              "type": "NETWORK_SSD",
              "sizeBytes": "268435456000"
            }
          }
        }'

      ```
    </Accordion>

    In the `Authorization` header, replace `<access_token>` with the access token that you got in the prerequisites.

    The request includes the following parameters:

    * **Job settings:**

      * `metadata.parentId`: [Project ID](/iam/manage-projects#how-to-get-a-project-id).
      * `metadata.name`: Job name.
      * `spec.image`: Container image reference in the `registry/path:tag` or `registry/path@digest` format. Use an image from a public registry or your authenticated private registry.
      * `spec.registryCredentials` (optional): Credentials to authenticate if you pull an image from a private registry.

        * `spec.registryCredentials.username`: Username.
        * `spec.registryCredentials.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](/container-registry/index) in the same project, you don't need to specify credentials.
      * `spec.containerCommand` (optional): Entrypoint command for the container.
      * `spec.args` (optional): Arguments for `docker run` to pass to the entrypoint command.
      * `spec.environmentVariables` (optional): Environment variables for the container.
        * `spec.environmentVariables[].name`: Environment variable name.
        * `spec.environmentVariables[].value`: Environment variable value.
      * `spec.workingDir` (optional): Working directory (absolute path).
      * `spec.timeout` (optional): Job timeout in seconds with the `s` suffix (for example, `9000s`). Minimum: `3600s` (1 hour), maximum: `604800s` (168 hours). Default: `86400s` (24 hours).
      * `spec.volumes` (optional): [Buckets](/object-storage/overview#buckets) or [shared filesystems](/compute/storage/types#shared-filesystems) 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](/compute/virtual-machines/maintenance).
        * `spec.volumes[].source`: Volume ID or name.
        * `spec.volumes[].containerPath`: Absolute path where the volume is mounted in the container.
        * `spec.volumes[].mode`: Mount mode: `READ_ONLY` or `READ_WRITE`.
      * `spec.injectedFiles` (optional): Files to mount into the job container at launch. To inject multiple files, add multiple items. Each mounted file is read-only and limited to 64 KiB.
        * `spec.injectedFiles[].containerPath`: Absolute path where the file is mounted in the container.
        * `spec.injectedFiles[].content`: Base64-encoded file content.

    * **Underlying container VM characteristics:**

      * `spec.subnetId`: [Subnet ID](/vpc/networking/resources#how-to-get-a-subnet-id). Required if the project has multiple subnets.
      * `spec.platform`: VM platform. See available platforms in [Types of virtual machines and GPUs in Nebius AI Cloud](/compute/virtual-machines/types).
      * `spec.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](/compute/virtual-machines/types#presets-for-gpu-platforms).
      * `spec.disk.type`: Disk type for the container VM.
      * `spec.disk.sizeBytes`: Disk size of the container VM in bytes. The default value is 250 GiB. See how [disk performance depends on disk size](/compute/storage/types#disk-performance).
      * `spec.shmSizeBytes` (optional): Shared memory size of `/dev/shm` in bytes. The default value is 16 GiB.
      * `spec.sshAuthorizedKeys` (optional): SSH public keys to access the container 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](https://console.nebius.com/quota).
      * `spec.preemptible` (optional): Whether to use a [preemptible VM](/compute/virtual-machines/preemptible). Preemptible VMs are cheaper but can be stopped by Compute at any time. Only GPU platforms offer preemptible VMs. If omitted or set to `false`, the container runs on a regular VM.
  </Tab>
</Tabs>

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](/iam/authorization/groups/index) that has the `viewer` role within your tenant; for example, the default `viewers` group. You can check this in the [Administration → IAM](https://console.nebius.com/iam) section of the web console.

<Note>
  Checking job logs is available only in the web console and CLI.
</Note>

<Tabs group="interfaces">
  <Tab title="Web console">
    1. In the sidebar, go to <Icon icon="https://mintcdn.com/nebius-ai-cloud/rOlLZ_MFvrheaI-h/_assets/sidebar/serverless-ai.svg?fit=max&auto=format&n=rOlLZ_MFvrheaI-h&q=85&s=4c90b50c6c33e6bff000b87b8e37765a" width="16" height="16" data-path="_assets/sidebar/serverless-ai.svg" /> **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](/observability/logs/query-language).
  </Tab>

  <Tab title="CLI">
    Run the following command:

    ```bash theme={null}
    nebius ai job logs <job_ID> --follow
    ```

    You can add the following parameters 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).
  </Tab>
</Tabs>

## 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:

<Tabs group="interfaces">
  <Tab title="Web console">
    1. In the sidebar, go to <Icon icon="https://mintcdn.com/nebius-ai-cloud/rOlLZ_MFvrheaI-h/_assets/sidebar/serverless-ai.svg?fit=max&auto=format&n=rOlLZ_MFvrheaI-h&q=85&s=4c90b50c6c33e6bff000b87b8e37765a" width="16" height="16" data-path="_assets/sidebar/serverless-ai.svg" /> **Serverless AI** → **Jobs**.
    2. Find the job and then click <Icon icon="https://mintcdn.com/nebius-ai-cloud/1Ha0sWR6e1mnIaHS/_assets/button-vellipsis.svg?fit=max&auto=format&n=1Ha0sWR6e1mnIaHS&q=85&s=e80b8e57c43bfd117679262e6a1334ad" width="12" height="24" data-path="_assets/button-vellipsis.svg" /> → **Cancel**.
    3. In the window that opens, confirm canceling the job.
  </Tab>

  <Tab title="CLI">
    1. List jobs:

       ```bash theme={null}
       nebius ai job list
       ```

       In the output, copy the ID of the required job.

    2. To cancel a job, run:

       ```bash theme={null}
       nebius ai job cancel <job_ID>
       ```
  </Tab>

  <Tab title="REST API">
    1. List jobs:

       ```bash theme={null}
       curl --request GET \
         --url "https://api.nebius.cloud/ai/v1/jobs?parentId=<project_ID>" \
         --header "Authorization: Bearer <access_token>"
       ```

       In the request, specify:

       * Your access token in the `Authorization` header.
       * The [project ID](/iam/manage-projects#how-to-get-a-project-id) in the `parentId` query parameter.

       In the response, copy the `metadata.id` of the required job.

    2. Cancel the job:

       ```bash theme={null}
       curl --request POST \
         --url 'https://api.nebius.cloud/ai/v1/jobs/cancel' \
         --header "Authorization: Bearer <access_token>" \
         --header 'Content-Type: application/json' \
         --data '{
           "id": "<job_ID>"
         }'
       ```

       In the request, specify:

       * Your access token in the `Authorization` header.
       * `<job_ID>`: Job ID that you copied.
  </Tab>
</Tabs>

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](/kubernetes/storage/filesystem-over-csi#how-to-delete-the-created-resources) and [deleting a bucket](/object-storage/buckets/manage#how-to-delete-buckets).

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:

<Tabs group="interfaces">
  <Tab title="Web console">
    1. In the sidebar, go to <Icon icon="https://mintcdn.com/nebius-ai-cloud/rOlLZ_MFvrheaI-h/_assets/sidebar/serverless-ai.svg?fit=max&auto=format&n=rOlLZ_MFvrheaI-h&q=85&s=4c90b50c6c33e6bff000b87b8e37765a" width="16" height="16" data-path="_assets/sidebar/serverless-ai.svg" /> **Serverless AI** → **Jobs**.
    2. Locate the job and then click <Icon icon="https://mintcdn.com/nebius-ai-cloud/1Ha0sWR6e1mnIaHS/_assets/button-vellipsis.svg?fit=max&auto=format&n=1Ha0sWR6e1mnIaHS&q=85&s=e80b8e57c43bfd117679262e6a1334ad" width="12" height="24" data-path="_assets/button-vellipsis.svg" /> → **Delete**.
    3. In the window that opens, confirm deleting the job.
  </Tab>

  <Tab title="CLI">
    1. List jobs:

       ```bash theme={null}
       nebius ai job list
       ```

       In the output, copy the ID of the required job.

    2. Delete the job:

       ```bash theme={null}
       nebius ai job delete <job_ID>
       ```
  </Tab>

  <Tab title="REST API">
    1. List jobs:

       ```bash theme={null}
       curl --request GET \
         --url "https://api.nebius.cloud/ai/v1/jobs?parentId=<project_ID>" \
         --header "Authorization: Bearer <access_token>"
       ```

       In the request, specify:

       * Your access token in the `Authorization` header.
       * The [project ID](/iam/manage-projects#how-to-get-a-project-id) in the `parentId` query parameter.

       In the response, copy the `metadata.id` of the required job.

    2. Delete the job:

       ```bash theme={null}
       curl --request DELETE \
         --url "https://api.nebius.cloud/ai/v1/jobs/<job_ID>" \
         --header "Authorization: Bearer <access_token>"
       ```

       In the request, specify:

       * Your access token in the `Authorization` header.
       * The job ID that you copied in the request path.
  </Tab>
</Tabs>

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](/kubernetes/storage/filesystem-over-csi#how-to-delete-the-created-resources) and [deleting a bucket](/object-storage/buckets/manage#how-to-delete-buckets).