Managing Devlabs in Serverless AI

Serverless AI Devlabs run container images as interactive development environments. They are suitable for coding, prototyping and debugging in the browser, including template-based setups such as code-server, JupyterLab® or ComfyUI. Each Devlab runs on a Compute container virtual machine (VM).

How to create a Devlab

Web console
CLI

In the sidebar, go to Serverless AI → Devlabs.
Click Create Devlab.
Specify the Devlab name.
Configure Devlab settings:
1. Select Custom to provide your own container image, or Template to start from a template.
2. If you selected Custom, in Image path, set the path to the container image.
3. If you use a private registry, click Add registry and provide the details for your registry.
4. In Ports, set the container ports for the Devlab. For each port, select HTTP or TCP. You can add multiple ports. The primary web UI port gets the Devlab’s managed HTTPS URL; other ports are shown in the Devlab status but are not publicly routed.
5. (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.
6. (Optional) In Environment variables, specify environment variables in key-value pairs.
7. (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.
8. (Optional) Enable Token authentication if you want to protect the Devlab web UI with a token.
  1. Under Token, click Create.
  2. In the Create secret with token window, specify the secret Name and, optionally, a Description.
  3. In Token, enter a token or click Generate. Copy and save the token securely before proceeding. The token is saved as the first value in a SecretStash secret.
  4. Click Create secret.
  If token authentication is disabled, anyone with the Devlab URL can access the service.
(Optional) In Platform, select With GPUs or Without GPUs, then select the platform.
(Optional) In Settings, select the preset. The preset must match the selected platform.
(Optional) Configure Storage settings:
- Under Disk size, set the container disk size. See how disk performance depends on disk size. Data under the workspace path on this disk persists across start and stop. The workspace is a path on the container disk (/workspace by default, or the path declared by the template). Its size is limited only by the disk size. While the Devlab is stopped, the entire container disk continues to be billed.
- 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. To create a new filesystem, see Volume parameters.
(Optional) In Files, upload files or create text files to inject into the container. The maximum file size is 64 KB.
Configure the Network section:
- Select a network and a subnet, or create a new subnet.
- Select the IP address type: Auto dynamic IP or Private IP. A public IP address is not required to open the Devlab web UI over the managed HTTPS URL. If you add an SSH key, make sure that network access allows SSH so you can connect to the VM.
Click Create Devlab.

If you want to create a Devlab from a template, list the available templates and choose one:
nebius ai devlab template list

Create the Devlab. Use either a template or a container image:

From a template:

nebius ai devlab create \
  --name <devlab_name> \
  --template <template> \
  --input <key=value> \
  --env <key=value> \
  --env-secret <key=secret_selector> \
  --container-port <port> \
  --workspace-path <absolute_path> \
  --volume <source:container_path[:mode]|s3://bucket:/container_path[:mode[:profile]]> \
  --inject-file "<local_path>:<absolute_container_path>" \
  --platform <platform_ID> \
  --preset <preset> \
  --disk-size <size> \
  --shm-size <size> \
  --subnet-id <subnet_ID> \
  --ssh-key <SSH_public_key> \
  --public

Example: Create a Devlab from the JupyterLab template

nebius ai devlab create \
  --name my-jupyter \
  --template jupyter \
  --input jupyter_token=A1b2C3d4E5f6G7h8

Provide your own random Jupyter token as --input jupyter_token=<token>. The token must contain exactly 16 alphanumeric characters. For example, you can generate one with openssl rand -hex 8.

From a container image:

nebius ai devlab create \
  --name <devlab_name> \
  --image <image> \
  --registry-secret <secret_selector> \
  --container-command "<command>" \
  --args "<arguments>" \
  --working-dir <absolute_path> \
  --primary-route-port <port> \
  --env <key=value> \
  --env-secret <key=secret_selector> \
  --container-port <port> \
  --workspace-path <absolute_path> \
  --volume <source:container_path[:mode]|s3://bucket:/container_path[:mode[:profile]]> \
  --inject-file "<local_path>:<absolute_container_path>" \
  --platform <platform_ID> \
  --preset <preset> \
  --disk-size <size> \
  --shm-size <size> \
  --subnet-id <subnet_ID> \
  --ssh-key <SSH_public_key> \
  --public

Example: Create a Devlab from a container image

nebius ai devlab create \
  --name nginx-devlab \
  --image docker.io/library/nginx:1.27-alpine \
  --platform cpu-e2 \
  --primary-route-port 80

In the command, specify the following parameters:

Devlab settings:
- --name: Devlab name.
- --template or --image: How to define the container. Specify one of these parameters:
  - --template: Template to use, for example, jupyter. Mutually exclusive with --image. The template declares the container image, entrypoint command, arguments, working directory, ports, primary web route and the default workspace path. Do not set --image, --container-command, --args, --working-dir or --primary-route-port with --template. To see available templates, run nebius ai devlab template list.
  - --image: Container image reference in the registry/path:tag or registry/path@digest format. Mutually exclusive with --template. Required unless you set --template.
- --input (optional for some templates; required for JupyterLab): Template input in the key=value format. Requires --template. To set several inputs, repeat the parameter. For the JupyterLab template, set --input jupyter_token=<token>, where <token> is a random string of exactly 16 alphanumeric characters that you provide.
- --registry-secret (optional): SecretStash secret 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***. Use when you pull an image from a private registry. Applies only with --image.
- --container-command (optional): Entrypoint command for the container. Applies only with --image. Declared by the template when you use --template.
- --args (optional): Arguments for the entrypoint command. Applies only with --image. Declared by the template when you use --template.
- --working-dir (optional): Working directory (absolute path). Applies only with --image. Declared by the template when you use --template.
- --primary-route-port: Container port for the primary web UI behind the managed HTTPS URL. Required with --image. Declared by the template when you use --template. Must match an HTTP port from --container-port.
- --env (optional): Environment variables for the container in the key=value format. To set several variables, repeat the parameter. With --template, these variables extend the template-declared ones.
- --env-secret (optional): Environment variables loaded from a SecretStash secret in the key=secret_selector format. The value can be a secret name, secret ID, version ID or a combined secret/version selector such as mbsec-e00***@mbsecver-e00***. To set several variables, repeat the parameter.
- --container-port (optional): Port to expose from the container, for example, 8080/http or 9000/tcp. Supported protocols are http, tcp and udp. To expose several ports, repeat the parameter. With --template, these ports extend the template-declared ones. Additional ports appear in the Devlab status but are not publicly routed unless they are the primary web route.
- --workspace-path (optional): Absolute path of the workspace on the container disk. Data under this path persists across start and stop. The default is /workspace. With --template, the template can declare a different path. You can set this path in the CLI or API; the web console does not expose it. The path must not overlap mounted volume paths. The workspace size is limited only by the container disk size (--disk-size).
- --volume (optional): Bucket or shared filesystem to mount to the Devlab container. Volume mount paths must not overlap the workspace path. 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, 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:
  --volume 'computefilesystem-e***:/input:ro' \ --volume 'storagebucket-e***:/output:rw' \ --volume 's3://training-results:/output:rw:default'
- --inject-file (optional): Mount a local file into the Devlab 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.
Underlying VM characteristics:
- --platform (optional): VM platform. If you omit the parameter, the CLI selects the default GPU platform. For CPU-only templates, such as Open WebUI, specify a CPU platform explicitly. See available platforms in Types of virtual machines and GPUs in Nebius AI Cloud.
- --preset (optional): Number of GPUs, vCPUs and RAM allocated to the container. If you omit the parameter, the CLI selects the smallest suitable preset for the platform. The preset must match the selected platform. See available presets in Presets for GPU platforms.
- --disk-size (optional): Disk size of the container VM, for example, 100Gi, 500Gi or 1Ti. If you omit the parameter, the CLI uses 250Gi. See how disk performance depends on disk size.
- --shm-size (optional): Shared memory size of /dev/shm, for example, 64Mi, 128Mi or 1Gi. With --template, you can set this only when the template leaves shared memory unset.
- --subnet-id: Subnet ID for the Devlab. Required if the project has multiple subnets.
- --ssh-key (optional): SSH public 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.
- --public (optional): Assigns a public IP address to the container VM. Not required to open the Devlab web UI over the managed HTTPS URL.

Devlab creation usually takes a few minutes. When the Devlab is running, you can open its web UI and view logs.

How to open a Devlab

Make sure you are in a group that has at least 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. When a Devlab is running, you can open its web UI over a managed HTTPS URL. A public IP address is not required. If you configured an SSH key and network access that allows SSH, you can also connect to the VM by SSH.

Web console
CLI

In the sidebar, go to Serverless AI → Devlabs.
Open the page of the required Devlab.
Wait until the Devlab is running. Then, in the Network section, copy the public Devlab address from the Public Devlabs field.
In a browser, open the URL. For the JupyterLab template, enter the Jupyter token in the JupyterLab password field. If you created the Devlab in the console, use the token that the console generated.

List Devlabs and copy the required ID:
nebius ai devlab list
You can also copy the Devlab ID from the overview page in the web console. The ID is shown under the Devlab name.
Get the Devlab details and copy the HTTPS URL from the output:
nebius ai devlab get <devlab_ID>
In a browser, open the HTTPS URL. For the JupyterLab template, enter the jupyter_token value that you set at create time in the JupyterLab password field.

How to check Devlab logs

Web console
CLI

In the sidebar, go to Serverless AI → Devlabs.
Next to the Devlab, click View logs. Alternatively, select the Devlab 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 devlab logs <devlab_ID> --follow

How to stop or start a Devlab

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. If you don’t currently need your Devlab but you want to preserve it, you can stop the Devlab and then start it later. While the Devlab is stopped, you are not billed for computing resources, but you are billed for the entire container disk. Data under the workspace path persists across start and stop. If you mounted a volume to the Devlab, you are also charged for the volume even if the Devlab is stopped.

Web console
CLI

In the sidebar, go to Serverless AI → Devlabs.
Find the Devlab and then click → Stop or Start.
In the window that opens, confirm the action.

List Devlabs and copy the required ID:
nebius ai devlab list
To stop a Devlab, run the following command:
nebius ai devlab stop <devlab_ID>
To start a Devlab, run the following command:
nebius ai devlab start <devlab_ID>

How to delete a Devlab

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. When you delete a Devlab, Serverless AI automatically deletes its VM and container disk. If you no longer need the Devlab, delete it:

Web console
CLI

In the sidebar, go to Serverless AI → Devlabs.
Find the Devlab and then click → Delete.
In the window that opens, confirm the deletion.

List Devlabs and copy the required ID:
nebius ai devlab list
Delete the Devlab:
nebius ai devlab delete <devlab_ID>

If the Devlab 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. “Jupyter” and the Jupyter logos are trademarks or registered trademarks of LF Charities, used by Nebius B.V. with permission.

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Managing Devlabs in Serverless AI

How to create a Devlab

How to open a Devlab

How to check Devlab logs

How to stop or start a Devlab

How to delete a Devlab

​How to create a Devlab

​How to open a Devlab

​How to check Devlab logs

​How to stop or start a Devlab

​How to delete a Devlab

How to create a Devlab

How to open a Devlab

How to check Devlab logs

How to stop or start a Devlab

How to delete a Devlab