Customizing the kernel’s runtime environment#

Kernel Provisioning#

Introduced in the 7.0 release, Kernel Provisioning enables the ability for third parties to manage the lifecycle of a kernel’s runtime environment. By implementing and configuring a kernel provisioner, third parties now have the ability to provision kernels for different environments, typically managed by resource managers like Kubernetes, Hadoop YARN, Slurm, etc. For example, a Kubernetes Provisioner would be responsible for launching a kernel within its own Kubernetes pod, communicating the kernel’s connection information back to the application (residing in a separate pod), and terminating the pod upon the kernel’s termination. In essence, a kernel provisioner is an abstraction layer between the KernelManager and today’s kernel process (i.e., Popen).

The kernel manager and kernel provisioner relationship#

Prior to this enhancement, the only extension point for customizing a kernel’s behavior could occur by subclassing KernelManager. This proved to be a limitation because the Jupyter framework allows for a single KernelManager class at any time. While applications could introduce a KernelManager subclass of their own, that KernelManager was then tied directly to that application and thereby not usable as a KernelManager in another application. As a result, we consider the KernelManager class to be an application-owned entity upon which application-specific behaviors can be implemented.

Kernel provisioners, on the other hand, are contained within the KernelManager (i.e., a has-a relationship) and applications are agnostic as to what kind of provisioner is in use other than what is conveyed via the kernel’s specification (kernelspec). All kernel interactions still occur via the KernelManager and KernelClient classes within jupyter_client and potentially subclassed by the application.

Kernel provisioners are not related in any way to the KernelManager instance that controls their lifecycle, nor do they have any affinity to the application within which they are used. They merely provide a vehicle by which authors can extend the landscape in which a kernel can reside, while not side-effecting the application. That said, some kernel provisioners may introduce requirements on the application. For example (and completely hypothetically speaking), a SlurmProvisioner may impose the constraint that the server (jupyter_client) resides on an edge node of the Slurm cluster. These kinds of requirements can be mitigated by leveraging applications like Jupyter Kernel Gateway or Jupyter Enterprise Gateway where the gateway server resides on the edge node of (or within) the cluster, etc.


Kernel provisioning does not alter today’s kernel discovery mechanism that utilizes well-known directories of kernel.json files. Instead, it optionally extends the current metadata stanza within the kernel.json to include the specification of the kernel provisioner name, along with an optional config stanza, consisting of provisioner-specific configuration items. For example, a container-based provisioner will likely need to specify the image name in this section. The important point is that the content of this section is provisioner-specific.

"metadata": {
  "kernel_provisioner": {
    "provisioner_name": "k8s-provisioner",
    "config": {
        "image_name": "my_docker_org/kernel:2.1.5",
        "max_cpus": 4

Kernel provisioner authors implement their provisioners by deriving from KernelProvisionerBase and expose their provisioner for consumption via entry-points:

'jupyter_client.kernel_provisioners': [
            'k8s-provisioner = my_package:K8sProvisioner',

Backwards Compatibility#

Prior to this release, no kernel.json (kernelspec) will contain a provisioner entry, yet the framework is now based on using provisioners. As a result, when a kernel_provisioner stanza is not present in a selected kernelspec, jupyter client will, by default, use the built-in LocalProvisioner implementation as its provisioner. This provisioner retains today’s local kernel functionality. It can also be subclassed for those provisioner authors wanting to extend the functionality of local kernels. The result of launching a kernel in this manner is equivalent to the following stanza existing in the kernel.json file:

"metadata": {
  "kernel_provisioner": {
    "provisioner_name": "local-provisioner",
    "config": {

Should a given installation wish to use a different provisioner as their “default provisioner” (including subclasses of LocalProvisioner), they can do so by specifying a value for KernelProvisionerFactory.default_provisioner_name.

Implementing a custom provisioner#

The impact of Kernel Provisioning is that it enables the ability to implement custom kernel provisioners to manage a kernel’s lifecycle within any runtime environment. There are currently two approaches by which that can be accomplished, extending the KernelProvisionerBase class or extending the built-in class - LocalProvisioner. As more provisioners are introduced, some may be implemented in an abstract sense, from which specific implementations can be authored.

Extending LocalProvisioner#

If you’re interested in running kernels locally and yet adjust their behavior, there’s a good chance you can simply extend LocalProvisioner via subclassing. This amounts to deriving from LocalProvisioner and overriding appropriate methods to provide your custom functionality.

In this example, RBACProvisioner will verify whether the current user is in the role meant for this kernel by calling a method implemented within this provisioner. If the user is not in the role, an exception will be thrown.

class RBACProvisioner(LocalProvisioner):
    role: str = Unicode(config=True)

    async def pre_launch(self, **kwargs: Any) -> Dict[str, Any]:
        if not self.user_in_role(self.role):
            raise PermissionError(
                f"User is not in role {self.role} and " f"cannot launch this kernel."

        return await super().pre_launch(**kwargs)

It is important to note when it’s necessary to call the superclass in a given method - since the operations it performs may be critical to the kernel’s management. As a result, you’ll likely need to become familiar with how LocalProvisioner operates.

Extending KernelProvisionerBase#

If you’d like to launch your kernel in an environment other than the local server, then you will need to consider subclassing KernelProvisionerBase directly. This will allow you to implement the various kernel process controls relative to your target environment. For instance, if you wanted to have your kernel hosted in a Hadoop YARN cluster, you will need to implement process-control methods like poll() and wait() to use the YARN REST API. Or, similarly, a Kubernetes-based provisioner would need to implement the process-control methods using the Kubernetes client API, etc.

By modeling the KernelProvisionerBase methods after subprocess.Popen a natural mapping between today’s kernel lifecycle management takes place. This, coupled with the ability to add configuration directly into the config: stanza of the kernel_provisioner metadata, allows for things like endpoint address, image names, namespaces, hosts lists, etc. to be specified relative to your kernel provisioner implementation.

The kernel_id corresponding to the launched kernel and used by the kernel manager is now available prior to the kernel’s launch. This enables provisioners with a unique key they can use to discover and control their kernel when launched into resource-managed clusters such as Hadoop YARN or Kubernetes.


Use kernel_id as a discovery mechanism from your provisioner!

Here’s a prototyped implementation of a couple of the abstract methods of KernelProvisionerBase for use in an Hadoop YARN cluster to help illustrate a provisioner’s implementation. Note that the built-in implementation of LocalProvisioner can also be used as a reference.

Notice the internal method _get_application_id(). This method is what the provisioner uses to determine if the YARN application (i.e., the kernel) is still running within the cluster. Although the provisioner doesn’t dictate the application id, the application id is discovered via the application name which is a function of kernel_id.

async def poll(self) -> Optional[int]:
    """Submitting a new kernel/app to YARN will take a while to be ACCEPTED.
    Thus application ID will probably not be available immediately for poll.
    So will regard the application as RUNNING when application ID still in

    :return: None if the application's ID is available and state is
             ACCEPTED/SUBMITTED/RUNNING. Otherwise 0.
    result = 0
    if self._get_application_id():
        state = self._query_app_state_by_id(self.application_id)
        if state in YarnProvisioner.initial_states:
            result = None

    return result

async def send_signal(self, signum):
    """Currently only support 0 as poll and other as kill.

    :param signum
    if signum == 0:
        return await self.poll()
    elif signum == signal.SIGKILL:
        return await self.kill()
        return await super().send_signal(signum)

Notice how in some cases we can compose provisioner methods to implement others. For example, since sending a signal number of 0 is tantamount to polling the process, we go ahead and call poll() to handle signum of 0 and kill() to handle SIGKILL requests.

Here we see how _get_application_id uses the kernel_id to acquire the application id - which is the primary id for controlling YARN application lifecycles. Since startup in resource-managed clusters can tend to take much longer than local kernels, you’ll typically need a polling or notification mechanism within your provisioner. In addition, your provisioner will be asked by the KernelManager what is an acceptable startup time. This answer is implemented in the provisioner via the get_shutdown_wait_time() method.

def _get_application_id(self, ignore_final_states: bool = False) -> str:
    if not self.application_id:
        app = self._query_app_by_name(self.kernel_id)
        state_condition = True
        if type(app) is dict:
            state = app.get("state")
            self.last_known_state = state

            if ignore_final_states:
                state_condition = state not in YarnProvisioner.final_states

            if len(app.get("id", "")) > 0 and state_condition:
                self.application_id = app["id"]
                    f"ApplicationID: '{app['id']}' assigned for "
                    f"KernelID: '{self.kernel_id}', state: {state}."
        if not self.application_id:
                f"ApplicationID not yet assigned for KernelID: "
                f"'{self.kernel_id}' - retrying..."
    return self.application_id

def get_shutdown_wait_time(self, recommended: Optional[float] = 5.0) -> float:
    if recommended < yarn_shutdown_wait_time:
        recommended = yarn_shutdown_wait_time
            f"{type(self).__name__} shutdown wait time adjusted to "
            f"{recommended} seconds."

    return recommended

Registering your custom provisioner#

Once your custom provisioner has been authored, it needs to be exposed as an entry point. To do this add the following to your (or equivalent) in its entry_points stanza using the group name jupyter_client.kernel_provisioners:

'jupyter_client.kernel_provisioners': [
    'rbac-provisioner = acme.rbac.provisioner:RBACProvisioner',


  • rbac-provisioner is the name of your provisioner and what will be referenced within the kernel.json file

  • acme.rbac.provisioner identifies the provisioner module name, and

  • RBACProvisioner is custom provisioner object name (implementation) that (directly or indirectly) derives from KernelProvisionerBase

Deploying your custom provisioner#

The final step in getting your custom provisioner deployed is to add a kernel_provisioner stanza to the appropriate kernel.json files. This can be accomplished manually or programmatically (in which some tooling is implemented to create the appropriate kernel.json file). In either case, the end result is the same - a kernel.json file with the appropriate stanza within metadata. The vision is that kernel provisioner packages will include an application that creates kernel specifications (i.e., kernel.json et. al.) pertaining to that provisioner.

Following on the previous example of RBACProvisioner, one would find the following kernel.json file in directory /usr/local/share/jupyter/kernels/rbac_kernel:

  "argv": ["python", "-m", "ipykernel_launcher", "-f", "{connection_file}"],
  "env": {},
  "display_name": "RBAC Kernel",
  "language": "python",
  "interrupt_mode": "signal",
  "metadata": {
    "kernel_provisioner": {
      "provisioner_name": "rbac-provisioner",
      "config": {
          "role": "data_scientist"

Listing available kernel provisioners#

To confirm that your custom provisioner is available for use, the jupyter kernelspec command has been extended to include a provisioners sub-command. As a result, running jupyter kernelspec provisioners will list the available provisioners by name followed by their module and object names (colon-separated):

$ jupyter kernelspec provisioners

Available kernel provisioners:
  local-provisioner    jupyter_client.provisioning:LocalProvisioner
  rbac-provisioner     acme.rbac.provisioner:RBACProvisioner