prefect_kubernetes.worker

Module containing the Kubernetes worker used for executing flow runs as Kubernetes jobs.

To start a Kubernetes worker, run the following command:

prefect worker start --pool 'my-work-pool' --type kubernetes

Replace my-work-pool with the name of the work pool you want the worker to poll for flow runs.

Securing your Prefect Cloud API key

If you are using Prefect Cloud and would like to pass your Prefect Cloud API key to created jobs via a Kubernetes secret, set the PREFECT_KUBERNETES_WORKER_STORE_PREFECT_API_IN_SECRET environment variable before starting your worker:

export PREFECT_KUBERNETES_WORKER_STORE_PREFECT_API_IN_SECRET="true"
prefect worker start --pool 'my-work-pool' --type kubernetes

Note that your work will need permission to create secrets in the same namespace(s) that Kubernetes jobs are created in to execute flow runs.

Using a custom Kubernetes job manifest template

The default template used for Kubernetes job manifests looks like this:

---
apiVersion: batch/v1
kind: Job
metadata:
labels: "{{ labels }}"
namespace: "{{ namespace }}"
generateName: "{{ name }}-"
spec:
ttlSecondsAfterFinished: "{{ finished_job_ttl }}"
template:
    spec:
    parallelism: 1
    completions: 1
    restartPolicy: Never
    serviceAccountName: "{{ service_account_name }}"
    containers:
    - name: prefect-job
        env: "{{ env }}"
        image: "{{ image }}"
        imagePullPolicy: "{{ image_pull_policy }}"
        args: "{{ command }}"

Each values enclosed in {{ }} is a placeholder that will be replaced with a value at runtime. The values that can be used a placeholders are defined by the variables schema defined in the base job template.

The default job manifest and available variables can be customized on a work pool by work pool basis. These customizations can be made via the Prefect UI when creating or editing a work pool.

For example, if you wanted to allow custom memory requests for a Kubernetes work pool you could update the job manifest template to look like this:

---
apiVersion: batch/v1
kind: Job
metadata:
labels: "{{ labels }}"
namespace: "{{ namespace }}"
generateName: "{{ name }}-"
spec:
ttlSecondsAfterFinished: "{{ finished_job_ttl }}"
template:
    spec:
    parallelism: 1
    completions: 1
    restartPolicy: Never
    serviceAccountName: "{{ service_account_name }}"
    containers:
    - name: prefect-job
        env: "{{ env }}"
        image: "{{ image }}"
        imagePullPolicy: "{{ image_pull_policy }}"
        args: "{{ command }}"
        resources:
            requests:
                memory: "{{ memory }}Mi"
            limits:
                memory: 128Mi

In this new template, the memory placeholder allows customization of the memory allocated to Kubernetes jobs created by workers in this work pool, but the limit is hard-coded and cannot be changed by deployments.

For more information about work pools and workers, checkout out the Prefect docs.

Classes

KubernetesImagePullPolicy

Bases: Enum

Enum representing the image pull policy options for a Kubernetes job.

Source code in prefect_kubernetes/worker.py

class KubernetesImagePullPolicy(enum.Enum):
    """Enum representing the image pull policy options for a Kubernetes job."""

    IF_NOT_PRESENT = "IfNotPresent"
    ALWAYS = "Always"
    NEVER = "Never"

KubernetesWorker

Bases: BaseWorker

Prefect worker that executes flow runs within Kubernetes Jobs.

Source code in prefect_kubernetes/worker.py

class KubernetesWorker(BaseWorker):
    """Prefect worker that executes flow runs within Kubernetes Jobs."""

    type = "kubernetes"
    job_configuration = KubernetesWorkerJobConfiguration
    job_configuration_variables = KubernetesWorkerVariables
    _description = (
        "Execute flow runs within jobs scheduled on a Kubernetes cluster. Requires a "
        "Kubernetes cluster."
    )
    _display_name = "Kubernetes"
    _documentation_url = "https://prefecthq.github.io/prefect-kubernetes/worker/"
    _logo_url = "https://cdn.sanity.io/images/3ugk85nk/production/2d0b896006ad463b49c28aaac14f31e00e32cfab-250x250.png"  # noqa

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._created_secrets = {}

    async def run(
        self,
        flow_run: "FlowRun",
        configuration: KubernetesWorkerJobConfiguration,
        task_status: Optional[anyio.abc.TaskStatus] = None,
    ) -> KubernetesWorkerResult:
        """
        Executes a flow run within a Kubernetes Job and waits for the flow run
        to complete.

        Args:
            flow_run: The flow run to execute
            configuration: The configuration to use when executing the flow run.
            task_status: The task status object for the current flow run. If provided,
                the task will be marked as started.

        Returns:
            KubernetesWorkerResult: A result object containing information about the
                final state of the flow run
        """
        logger = self.get_flow_run_logger(flow_run)

        with self._get_configured_kubernetes_client(configuration) as client:
            logger.info("Creating Kubernetes job...")
            job = await run_sync_in_worker_thread(
                self._create_job, configuration, client
            )
            pid = await run_sync_in_worker_thread(
                self._get_infrastructure_pid, job, client
            )
            # Indicate that the job has started
            if task_status is not None:
                task_status.started(pid)

            # Monitor the job until completion

            events_replicator = KubernetesEventsReplicator(
                client=client,
                job_name=job.metadata.name,
                namespace=configuration.namespace,
                worker_resource=self._event_resource(),
                related_resources=self._event_related_resources(
                    configuration=configuration
                ),
                timeout_seconds=configuration.pod_watch_timeout_seconds,
            )

            with events_replicator:
                status_code = await run_sync_in_worker_thread(
                    self._watch_job, logger, job.metadata.name, configuration, client
                )
            return KubernetesWorkerResult(identifier=pid, status_code=status_code)

    async def kill_infrastructure(
        self,
        infrastructure_pid: str,
        configuration: KubernetesWorkerJobConfiguration,
        grace_seconds: int = 30,
    ):
        """
        Stops a job for a cancelled flow run based on the provided infrastructure PID
        and run configuration.
        """
        await run_sync_in_worker_thread(
            self._stop_job, infrastructure_pid, configuration, grace_seconds
        )

    async def teardown(self, *exc_info):
        await super().teardown(*exc_info)

        await self._clean_up_created_secrets()

    async def _clean_up_created_secrets(self):
        """Deletes any secrets created during the worker's operation."""
        coros = []
        for key, configuration in self._created_secrets.items():
            with self._get_configured_kubernetes_client(configuration) as client:
                with self._get_core_client(client) as core_client:
                    coros.append(
                        run_sync_in_worker_thread(
                            core_client.delete_namespaced_secret,
                            name=key[0],
                            namespace=key[1],
                        )
                    )

        results = await asyncio.gather(*coros, return_exceptions=True)
        for result in results:
            if isinstance(result, Exception):
                self._logger.warning(
                    "Failed to delete created secret with exception: %s", result
                )

    def _stop_job(
        self,
        infrastructure_pid: str,
        configuration: KubernetesWorkerJobConfiguration,
        grace_seconds: int = 30,
    ):
        """Removes the given Job from the Kubernetes cluster"""
        with self._get_configured_kubernetes_client(configuration) as client:
            job_cluster_uid, job_namespace, job_name = self._parse_infrastructure_pid(
                infrastructure_pid
            )

            if job_namespace != configuration.namespace:
                raise InfrastructureNotAvailable(
                    f"Unable to kill job {job_name!r}: The job is running in namespace "
                    f"{job_namespace!r} but this worker expected jobs to be running in "
                    f"namespace {configuration.namespace!r} based on the work pool and "
                    "deployment configuration."
                )

            current_cluster_uid = self._get_cluster_uid(client)
            if job_cluster_uid != current_cluster_uid:
                raise InfrastructureNotAvailable(
                    f"Unable to kill job {job_name!r}: The job is running on another "
                    "cluster than the one specified by the infrastructure PID."
                )

            with self._get_batch_client(client) as batch_client:
                try:
                    batch_client.delete_namespaced_job(
                        name=job_name,
                        namespace=job_namespace,
                        grace_period_seconds=grace_seconds,
                        # Foreground propagation deletes dependent objects before deleting # noqa
                        # owner objects. This ensures that the pods are cleaned up before # noqa
                        # the job is marked as deleted.
                        # See: https://kubernetes.io/docs/concepts/architecture/garbage-collection/#foreground-deletion # noqa
                        propagation_policy="Foreground",
                    )
                except kubernetes.client.exceptions.ApiException as exc:
                    if exc.status == 404:
                        raise InfrastructureNotFound(
                            f"Unable to kill job {job_name!r}: The job was not found."
                        ) from exc
                    else:
                        raise

    @contextmanager
    def _get_configured_kubernetes_client(
        self, configuration: KubernetesWorkerJobConfiguration
    ) -> Generator["ApiClient", None, None]:
        """
        Returns a configured Kubernetes client.
        """

        try:
            if configuration.cluster_config:
                client = kubernetes.config.new_client_from_config_dict(
                    config_dict=configuration.cluster_config.config,
                    context=configuration.cluster_config.context_name,
                )
            else:
                # If no hardcoded config specified, try to load Kubernetes configuration
                # within a cluster. If that doesn't work, try to load the configuration
                # from the local environment, allowing any further ConfigExceptions to
                # bubble up.
                try:
                    kubernetes.config.load_incluster_config()
                    config = kubernetes.client.Configuration.get_default_copy()
                    client = kubernetes.client.ApiClient(configuration=config)
                except kubernetes.config.ConfigException:
                    client = kubernetes.config.new_client_from_config()

            if os.environ.get(
                "PREFECT_KUBERNETES_WORKER_ADD_TCP_KEEPALIVE", "TRUE"
            ).strip().lower() in ("true", "1"):
                enable_socket_keep_alive(client)

            yield client
        finally:
            client.rest_client.pool_manager.clear()

    def _replace_api_key_with_secret(
        self, configuration: KubernetesWorkerJobConfiguration, client: "ApiClient"
    ):
        """Replaces the PREFECT_API_KEY environment variable with a Kubernetes secret"""
        manifest_env = configuration.job_manifest["spec"]["template"]["spec"][
            "containers"
        ][0].get("env")
        manifest_api_key_env = next(
            (
                env_entry
                for env_entry in manifest_env
                if env_entry.get("name") == "PREFECT_API_KEY"
            ),
            {},
        )
        api_key = manifest_api_key_env.get("value")
        if api_key:
            secret_name = f"prefect-{_slugify_name(self.name)}-api-key"
            secret = self._upsert_secret(
                name=secret_name,
                value=api_key,
                namespace=configuration.namespace,
                client=client,
            )
            # Store configuration so that we can delete the secret when the worker shuts
            # down
            self._created_secrets[
                (secret.metadata.name, secret.metadata.namespace)
            ] = configuration
            new_api_env_entry = {
                "name": "PREFECT_API_KEY",
                "valueFrom": {"secretKeyRef": {"name": secret_name, "key": "value"}},
            }
            manifest_env = [
                entry if entry.get("name") != "PREFECT_API_KEY" else new_api_env_entry
                for entry in manifest_env
            ]
            configuration.job_manifest["spec"]["template"]["spec"]["containers"][0][
                "env"
            ] = manifest_env

    @retry(
        stop=stop_after_attempt(MAX_ATTEMPTS),
        wait=wait_fixed(RETRY_MIN_DELAY_SECONDS)
        + wait_random(
            RETRY_MIN_DELAY_JITTER_SECONDS,
            RETRY_MAX_DELAY_JITTER_SECONDS,
        ),
        reraise=True,
    )
    def _create_job(
        self, configuration: KubernetesWorkerJobConfiguration, client: "ApiClient"
    ) -> "V1Job":
        """
        Creates a Kubernetes job from a job manifest.
        """
        if os.environ.get(
            "PREFECT_KUBERNETES_WORKER_STORE_PREFECT_API_IN_SECRET", ""
        ).strip().lower() in ("true", "1"):
            self._replace_api_key_with_secret(
                configuration=configuration, client=client
            )
        try:
            with self._get_batch_client(client) as batch_client:
                job = batch_client.create_namespaced_job(
                    configuration.namespace, configuration.job_manifest
                )
        except kubernetes.client.exceptions.ApiException as exc:
            # Parse the reason and message from the response if feasible
            message = ""
            if exc.reason:
                message += ": " + exc.reason
            if exc.body and "message" in (body := json.loads(exc.body)):
                message += ": " + body["message"]

            raise InfrastructureError(
                f"Unable to create Kubernetes job{message}"
            ) from exc

        return job

    def _upsert_secret(
        self, name: str, value: str, namespace: str, client: "ApiClient"
    ):
        encoded_value = base64.b64encode(value.encode("utf-8")).decode("utf-8")
        with self._get_core_client(client) as core_client:
            try:
                # Get the current version of the Secret and update it with the
                # new value
                current_secret = core_client.read_namespaced_secret(
                    name=name, namespace=namespace
                )
                current_secret.data = {"value": encoded_value}
                secret = core_client.replace_namespaced_secret(
                    name=name, namespace=namespace, body=current_secret
                )
            except ApiException as exc:
                if exc.status != 404:
                    raise
                # Create the secret if it doesn't already exist
                metadata = V1ObjectMeta(name=name, namespace=namespace)
                secret = V1Secret(
                    api_version="v1",
                    kind="Secret",
                    metadata=metadata,
                    data={"value": encoded_value},
                )
                secret = core_client.create_namespaced_secret(
                    namespace=namespace, body=secret
                )
            return secret

    @contextmanager
    def _get_batch_client(
        self, client: "ApiClient"
    ) -> Generator["BatchV1Api", None, None]:
        """
        Context manager for retrieving a Kubernetes batch client.
        """
        try:
            yield kubernetes.client.BatchV1Api(api_client=client)
        finally:
            client.rest_client.pool_manager.clear()

    def _get_infrastructure_pid(self, job: "V1Job", client: "ApiClient") -> str:
        """
        Generates a Kubernetes infrastructure PID.

        The PID is in the format: "<cluster uid>:<namespace>:<job name>".
        """
        cluster_uid = self._get_cluster_uid(client)
        pid = f"{cluster_uid}:{job.metadata.namespace}:{job.metadata.name}"
        return pid

    def _parse_infrastructure_pid(
        self, infrastructure_pid: str
    ) -> Tuple[str, str, str]:
        """
        Parse a Kubernetes infrastructure PID into its component parts.

        Returns a cluster UID, namespace, and job name.
        """
        cluster_uid, namespace, job_name = infrastructure_pid.split(":", 2)
        return cluster_uid, namespace, job_name

    @contextmanager
    def _get_core_client(
        self, client: "ApiClient"
    ) -> Generator["CoreV1Api", None, None]:
        """
        Context manager for retrieving a Kubernetes core client.
        """
        try:
            yield kubernetes.client.CoreV1Api(api_client=client)
        finally:
            client.rest_client.pool_manager.clear()

    def _get_cluster_uid(self, client: "ApiClient") -> str:
        """
        Gets a unique id for the current cluster being used.

        There is no real unique identifier for a cluster. However, the `kube-system`
        namespace is immutable and has a persistence UID that we use instead.

        PREFECT_KUBERNETES_CLUSTER_UID can be set in cases where the `kube-system`
        namespace cannot be read e.g. when a cluster role cannot be created. If set,
        this variable will be used and we will not attempt to read the `kube-system`
        namespace.

        See https://github.com/kubernetes/kubernetes/issues/44954
        """
        # Default to an environment variable
        env_cluster_uid = os.environ.get("PREFECT_KUBERNETES_CLUSTER_UID")
        if env_cluster_uid:
            return env_cluster_uid

        # Read the UID from the cluster namespace
        with self._get_core_client(client) as core_client:
            namespace = core_client.read_namespace("kube-system")
        cluster_uid = namespace.metadata.uid

        return cluster_uid

    def _job_events(
        self,
        watch: kubernetes.watch.Watch,
        batch_client: kubernetes.client.BatchV1Api,
        job_name: str,
        namespace: str,
        watch_kwargs: dict,
    ) -> Generator[Union[Any, dict, str], Any, None]:
        """
        Stream job events.

        Pick up from the current resource version returned by the API
        in the case of a 410.

        See https://kubernetes.io/docs/reference/using-api/api-concepts/#efficient-detection-of-changes  # noqa
        """
        while True:
            try:
                return watch.stream(
                    func=batch_client.list_namespaced_job,
                    namespace=namespace,
                    field_selector=f"metadata.name={job_name}",
                    **watch_kwargs,
                )
            except ApiException as e:
                if e.status == 410:
                    job_list = batch_client.list_namespaced_job(
                        namespace=namespace, field_selector=f"metadata.name={job_name}"
                    )
                    resource_version = job_list.metadata.resource_version
                    watch_kwargs["resource_version"] = resource_version
                else:
                    raise

    def _watch_job(
        self,
        logger: logging.Logger,
        job_name: str,
        configuration: KubernetesWorkerJobConfiguration,
        client: "ApiClient",
    ) -> int:
        """
        Watch a job.

        Return the final status code of the first container.
        """
        logger.debug(f"Job {job_name!r}: Monitoring job...")

        job = self._get_job(logger, job_name, configuration, client)
        if not job:
            return -1

        pod = self._get_job_pod(logger, job_name, configuration, client)
        if not pod:
            return -1

        # Calculate the deadline before streaming output
        deadline = (
            (time.monotonic() + configuration.job_watch_timeout_seconds)
            if configuration.job_watch_timeout_seconds is not None
            else None
        )

        if configuration.stream_output:
            with self._get_core_client(client) as core_client:
                logs = core_client.read_namespaced_pod_log(
                    pod.metadata.name,
                    configuration.namespace,
                    follow=True,
                    _preload_content=False,
                    container="prefect-job",
                )
                try:
                    for log in logs.stream():
                        print(log.decode().rstrip())

                        # Check if we have passed the deadline and should stop streaming
                        # logs
                        remaining_time = (
                            deadline - time.monotonic() if deadline else None
                        )
                        if deadline and remaining_time <= 0:
                            break

                except Exception:
                    logger.warning(
                        (
                            "Error occurred while streaming logs - "
                            "Job will continue to run but logs will "
                            "no longer be streamed to stdout."
                        ),
                        exc_info=True,
                    )

        with self._get_batch_client(client) as batch_client:
            # Check if the job is completed before beginning a watch
            job = batch_client.read_namespaced_job(
                name=job_name, namespace=configuration.namespace
            )
            completed = job.status.completion_time is not None

            while not completed:
                remaining_time = (
                    math.ceil(deadline - time.monotonic()) if deadline else None
                )
                if deadline and remaining_time <= 0:
                    logger.error(
                        f"Job {job_name!r}: Job did not complete within "
                        f"timeout of {configuration.job_watch_timeout_seconds}s."
                    )
                    return -1

                watch = kubernetes.watch.Watch()

                # The kubernetes library will disable retries if the timeout kwarg is
                # present regardless of the value so we do not pass it unless given
                # https://github.com/kubernetes-client/python/blob/84f5fea2a3e4b161917aa597bf5e5a1d95e24f5a/kubernetes/base/watch/watch.py#LL160
                watch_kwargs = {"timeout_seconds": remaining_time} if deadline else {}

                for event in self._job_events(
                    watch,
                    batch_client,
                    job_name,
                    configuration.namespace,
                    watch_kwargs,
                ):
                    if event["type"] == "DELETED":
                        logger.error(f"Job {job_name!r}: Job has been deleted.")
                        completed = True
                    elif event["object"].status.completion_time:
                        if not event["object"].status.succeeded:
                            # Job failed, exit while loop and return pod exit code
                            logger.error(f"Job {job_name!r}: Job failed.")
                        completed = True
                    # Check if the job has reached its backoff limit
                    # and stop watching if it has
                    elif (
                        event["object"].spec.backoff_limit is not None
                        and event["object"].status.failed is not None
                        and event["object"].status.failed
                        > event["object"].spec.backoff_limit
                    ):
                        logger.error(f"Job {job_name!r}: Job reached backoff limit.")
                        completed = True
                    # If the job has no backoff limit, check if it has failed
                    # and stop watching if it has
                    elif (
                        not event["object"].spec.backoff_limit
                        and event["object"].status.failed
                    ):
                        completed = True

                    if completed:
                        watch.stop()
                        break

        with self._get_core_client(client) as core_client:
            # Get all pods for the job
            pods = core_client.list_namespaced_pod(
                namespace=configuration.namespace, label_selector=f"job-name={job_name}"
            )
            # Get the status for only the most recently used pod
            pods.items.sort(
                key=lambda pod: pod.metadata.creation_timestamp, reverse=True
            )
            most_recent_pod = pods.items[0] if pods.items else None
            first_container_status = (
                most_recent_pod.status.container_statuses[0]
                if most_recent_pod
                else None
            )
            if not first_container_status:
                logger.error(f"Job {job_name!r}: No pods found for job.")
                return -1

            # In some cases, such as spot instance evictions, the pod will be forcibly
            # terminated and not report a status correctly.
            elif (
                first_container_status.state is None
                or first_container_status.state.terminated is None
                or first_container_status.state.terminated.exit_code is None
            ):
                logger.error(
                    f"Could not determine exit code for {job_name!r}."
                    "Exit code will be reported as -1."
                    f"First container status info did not report an exit code."
                    f"First container info: {first_container_status}."
                )
                return -1

        return first_container_status.state.terminated.exit_code

    def _get_job(
        self,
        logger: logging.Logger,
        job_id: str,
        configuration: KubernetesWorkerJobConfiguration,
        client: "ApiClient",
    ) -> Optional["V1Job"]:
        """Get a Kubernetes job by id."""
        with self._get_batch_client(client) as batch_client:
            try:
                job = batch_client.read_namespaced_job(
                    name=job_id, namespace=configuration.namespace
                )
            except kubernetes.client.exceptions.ApiException:
                logger.error(f"Job {job_id!r} was removed.", exc_info=True)
                return None
            return job

    def _get_job_pod(
        self,
        logger: logging.Logger,
        job_name: str,
        configuration: KubernetesWorkerJobConfiguration,
        client: "ApiClient",
    ) -> Optional["V1Pod"]:
        """Get the first running pod for a job."""
        from kubernetes.client.models import V1Pod

        watch = kubernetes.watch.Watch()
        logger.debug(f"Job {job_name!r}: Starting watch for pod start...")
        last_phase = None
        last_pod_name: Optional[str] = None
        with self._get_core_client(client) as core_client:
            for event in watch.stream(
                func=core_client.list_namespaced_pod,
                namespace=configuration.namespace,
                label_selector=f"job-name={job_name}",
                timeout_seconds=configuration.pod_watch_timeout_seconds,
            ):
                pod: V1Pod = event["object"]
                last_pod_name = pod.metadata.name

                phase = pod.status.phase
                if phase != last_phase:
                    logger.info(f"Job {job_name!r}: Pod has status {phase!r}.")

                if phase != "Pending":
                    watch.stop()
                    return pod

                last_phase = phase

        # If we've gotten here, we never found the Pod that was created for the flow run
        # Job, so let's inspect the situation and log what we can find.  It's possible
        # that the Job ran into scheduling constraints it couldn't satisfy, like
        # memory/CPU requests, or a volume that wasn't available, or a node with an
        # available GPU.
        logger.error(f"Job {job_name!r}: Pod never started.")
        self._log_recent_events(logger, job_name, last_pod_name, configuration, client)

    def _log_recent_events(
        self,
        logger: logging.Logger,
        job_name: str,
        pod_name: Optional[str],
        configuration: KubernetesWorkerJobConfiguration,
        client: "ApiClient",
    ) -> None:
        """Look for reasons why a Job may not have been able to schedule a Pod, or why
        a Pod may not have been able to start and log them to the provided logger."""
        from kubernetes.client.models import CoreV1Event, CoreV1EventList

        def best_event_time(event: CoreV1Event) -> datetime:
            """Choose the best timestamp from a Kubernetes event"""
            return event.event_time or event.last_timestamp

        def log_event(event: CoreV1Event):
            """Log an event in one of a few formats to the provided logger"""
            if event.count and event.count > 1:
                logger.info(
                    "%s event %r (%s times) at %s: %s",
                    event.involved_object.kind,
                    event.reason,
                    event.count,
                    best_event_time(event),
                    event.message,
                )
            else:
                logger.info(
                    "%s event %r at %s: %s",
                    event.involved_object.kind,
                    event.reason,
                    best_event_time(event),
                    event.message,
                )

        with self._get_core_client(client) as core_client:
            events: CoreV1EventList = core_client.list_namespaced_event(
                configuration.namespace
            )
            event: CoreV1Event
            for event in sorted(events.items, key=best_event_time):
                if (
                    event.involved_object.api_version == "batch/v1"
                    and event.involved_object.kind == "Job"
                    and event.involved_object.namespace == configuration.namespace
                    and event.involved_object.name == job_name
                ):
                    log_event(event)

                if (
                    pod_name
                    and event.involved_object.api_version == "v1"
                    and event.involved_object.kind == "Pod"
                    and event.involved_object.namespace == configuration.namespace
                    and event.involved_object.name == pod_name
                ):
                    log_event(event)

Functions

kill_infrastructure async

Stops a job for a cancelled flow run based on the provided infrastructure PID and run configuration.

Source code in prefect_kubernetes/worker.py

async def kill_infrastructure(
    self,
    infrastructure_pid: str,
    configuration: KubernetesWorkerJobConfiguration,
    grace_seconds: int = 30,
):
    """
    Stops a job for a cancelled flow run based on the provided infrastructure PID
    and run configuration.
    """
    await run_sync_in_worker_thread(
        self._stop_job, infrastructure_pid, configuration, grace_seconds
    )

run async

Executes a flow run within a Kubernetes Job and waits for the flow run to complete.

Parameters:

Name	Type	Description	Default
flow_run	FlowRun	The flow run to execute	required
configuration	KubernetesWorkerJobConfiguration	The configuration to use when executing the flow run.	required
task_status	Optional[TaskStatus]	The task status object for the current flow run. If provided, the task will be marked as started.	None

Returns:

Name	Type	Description
KubernetesWorkerResult	KubernetesWorkerResult	A result object containing information about the final state of the flow run

Source code in prefect_kubernetes/worker.py

async def run(
    self,
    flow_run: "FlowRun",
    configuration: KubernetesWorkerJobConfiguration,
    task_status: Optional[anyio.abc.TaskStatus] = None,
) -> KubernetesWorkerResult:
    """
    Executes a flow run within a Kubernetes Job and waits for the flow run
    to complete.

    Args:
        flow_run: The flow run to execute
        configuration: The configuration to use when executing the flow run.
        task_status: The task status object for the current flow run. If provided,
            the task will be marked as started.

    Returns:
        KubernetesWorkerResult: A result object containing information about the
            final state of the flow run
    """
    logger = self.get_flow_run_logger(flow_run)

    with self._get_configured_kubernetes_client(configuration) as client:
        logger.info("Creating Kubernetes job...")
        job = await run_sync_in_worker_thread(
            self._create_job, configuration, client
        )
        pid = await run_sync_in_worker_thread(
            self._get_infrastructure_pid, job, client
        )
        # Indicate that the job has started
        if task_status is not None:
            task_status.started(pid)

        # Monitor the job until completion

        events_replicator = KubernetesEventsReplicator(
            client=client,
            job_name=job.metadata.name,
            namespace=configuration.namespace,
            worker_resource=self._event_resource(),
            related_resources=self._event_related_resources(
                configuration=configuration
            ),
            timeout_seconds=configuration.pod_watch_timeout_seconds,
        )

        with events_replicator:
            status_code = await run_sync_in_worker_thread(
                self._watch_job, logger, job.metadata.name, configuration, client
            )
        return KubernetesWorkerResult(identifier=pid, status_code=status_code)

KubernetesWorkerJobConfiguration

Bases: BaseJobConfiguration

Configuration class used by the Kubernetes worker.

An instance of this class is passed to the Kubernetes worker's run method for each flow run. It contains all of the information necessary to execute the flow run as a Kubernetes job.

Attributes:

Name	Type	Description
name		The name to give to created Kubernetes job.
command		The command executed in created Kubernetes jobs to kick off flow run execution.
env		The environment variables to set in created Kubernetes jobs.
labels		The labels to set on created Kubernetes jobs.
namespace	str	The Kubernetes namespace to create Kubernetes jobs in.
job_manifest	Dict[str, Any]	The Kubernetes job manifest to use to create Kubernetes jobs.
cluster_config	Optional[KubernetesClusterConfig]	The Kubernetes cluster configuration to use for authentication to a Kubernetes cluster.
job_watch_timeout_seconds	Optional[int]	The number of seconds to wait for the job to complete before timing out. If None, the worker will wait indefinitely.
pod_watch_timeout_seconds	int	The number of seconds to wait for the pod to complete before timing out.
stream_output	bool	Whether or not to stream the job's output.

Source code in prefect_kubernetes/worker.py

class KubernetesWorkerJobConfiguration(BaseJobConfiguration):
    """
    Configuration class used by the Kubernetes worker.

    An instance of this class is passed to the Kubernetes worker's `run` method
    for each flow run. It contains all of the information necessary to execute
    the flow run as a Kubernetes job.

    Attributes:
        name: The name to give to created Kubernetes job.
        command: The command executed in created Kubernetes jobs to kick off
            flow run execution.
        env: The environment variables to set in created Kubernetes jobs.
        labels: The labels to set on created Kubernetes jobs.
        namespace: The Kubernetes namespace to create Kubernetes jobs in.
        job_manifest: The Kubernetes job manifest to use to create Kubernetes jobs.
        cluster_config: The Kubernetes cluster configuration to use for authentication
            to a Kubernetes cluster.
        job_watch_timeout_seconds: The number of seconds to wait for the job to
            complete before timing out. If `None`, the worker will wait indefinitely.
        pod_watch_timeout_seconds: The number of seconds to wait for the pod to
            complete before timing out.
        stream_output: Whether or not to stream the job's output.
    """

    namespace: str = Field(default="default")
    job_manifest: Dict[str, Any] = Field(template=_get_default_job_manifest_template())
    cluster_config: Optional[KubernetesClusterConfig] = Field(default=None)
    job_watch_timeout_seconds: Optional[int] = Field(default=None)
    pod_watch_timeout_seconds: int = Field(default=60)
    stream_output: bool = Field(default=True)

    # internal-use only
    _api_dns_name: Optional[str] = None  # Replaces 'localhost' in API URL

    @validator("job_manifest")
    def _ensure_metadata_is_present(cls, value: Dict[str, Any]):
        """Ensures that the metadata is present in the job manifest."""
        if "metadata" not in value:
            value["metadata"] = {}
        return value

    @validator("job_manifest")
    def _ensure_labels_is_present(cls, value: Dict[str, Any]):
        """Ensures that the metadata is present in the job manifest."""
        if "labels" not in value["metadata"]:
            value["metadata"]["labels"] = {}
        return value

    @validator("job_manifest")
    def _ensure_namespace_is_present(cls, value: Dict[str, Any], values):
        """Ensures that the namespace is present in the job manifest."""
        if "namespace" not in value["metadata"]:
            value["metadata"]["namespace"] = values["namespace"]
        return value

    @validator("job_manifest")
    def _ensure_job_includes_all_required_components(cls, value: Dict[str, Any]):
        """
        Ensures that the job manifest includes all required components.
        """
        patch = JsonPatch.from_diff(value, _get_base_job_manifest())
        missing_paths = sorted([op["path"] for op in patch if op["op"] == "add"])
        if missing_paths:
            raise ValueError(
                "Job is missing required attributes at the following paths: "
                f"{', '.join(missing_paths)}"
            )
        return value

    @validator("job_manifest")
    def _ensure_job_has_compatible_values(cls, value: Dict[str, Any]):
        patch = JsonPatch.from_diff(value, _get_base_job_manifest())
        incompatible = sorted(
            [
                f"{op['path']} must have value {op['value']!r}"
                for op in patch
                if op["op"] == "replace"
            ]
        )
        if incompatible:
            raise ValueError(
                "Job has incompatible values for the following attributes: "
                f"{', '.join(incompatible)}"
            )
        return value

    def prepare_for_flow_run(
        self,
        flow_run: "FlowRun",
        deployment: Optional["DeploymentResponse"] = None,
        flow: Optional["Flow"] = None,
    ):
        """
        Prepares the job configuration for a flow run.

        Ensures that necessary values are present in the job manifest and that the
        job manifest is valid.

        Args:
            flow_run: The flow run to prepare the job configuration for
            deployment: The deployment associated with the flow run used for
                preparation.
            flow: The flow associated with the flow run used for preparation.
        """
        super().prepare_for_flow_run(flow_run, deployment, flow)
        # Update configuration env and job manifest env
        self._update_prefect_api_url_if_local_server()
        self._populate_env_in_manifest()
        # Update labels in job manifest
        self._slugify_labels()
        # Add defaults to job manifest if necessary
        self._populate_image_if_not_present()
        self._populate_command_if_not_present()
        self._populate_generate_name_if_not_present()

    def _populate_env_in_manifest(self):
        """
        Populates environment variables in the job manifest.

        When `env` is templated as a variable in the job manifest it comes in as a
        dictionary. We need to convert it to a list of dictionaries to conform to the
        Kubernetes job manifest schema.

        This function also handles the case where the user has removed the `{{ env }}`
        placeholder and hard coded a value for `env`. In this case, we need to prepend
        our environment variables to the list to ensure Prefect setting propagation.
        An example reason the a user would remove the `{{ env }}` placeholder to
        hardcode Kubernetes secrets in the base job template.
        """
        transformed_env = [{"name": k, "value": v} for k, v in self.env.items()]

        template_env = self.job_manifest["spec"]["template"]["spec"]["containers"][
            0
        ].get("env")

        # If user has removed `{{ env }}` placeholder and hard coded a value for `env`,
        # we need to prepend our environment variables to the list to ensure Prefect
        # setting propagation.
        if isinstance(template_env, list):
            self.job_manifest["spec"]["template"]["spec"]["containers"][0]["env"] = [
                *transformed_env,
                *template_env,
            ]
        # Current templating adds `env` as a dict when the kubernetes manifest requires
        # a list of dicts. Might be able to improve this in the future with a better
        # default `env` value and better typing.
        else:
            self.job_manifest["spec"]["template"]["spec"]["containers"][0][
                "env"
            ] = transformed_env

    def _update_prefect_api_url_if_local_server(self):
        """If the API URL has been set by the base environment rather than the by the
        user, update the value to ensure connectivity when using a bridge network by
        updating local connections to use the internal host
        """
        if self.env.get("PREFECT_API_URL") and self._api_dns_name:
            self.env["PREFECT_API_URL"] = (
                self.env["PREFECT_API_URL"]
                .replace("localhost", self._api_dns_name)
                .replace("127.0.0.1", self._api_dns_name)
            )

    def _slugify_labels(self):
        """Slugifies the labels in the job manifest."""
        all_labels = {**self.job_manifest["metadata"].get("labels", {}), **self.labels}
        self.job_manifest["metadata"]["labels"] = {
            _slugify_label_key(k): _slugify_label_value(v)
            for k, v in all_labels.items()
        }

    def _populate_image_if_not_present(self):
        """Ensures that the image is present in the job manifest. Populates the image
        with the default Prefect image if it is not present."""
        try:
            if (
                "image"
                not in self.job_manifest["spec"]["template"]["spec"]["containers"][0]
            ):
                self.job_manifest["spec"]["template"]["spec"]["containers"][0][
                    "image"
                ] = get_prefect_image_name()
        except KeyError:
            raise ValueError(
                "Unable to verify image due to invalid job manifest template."
            )

    def _populate_command_if_not_present(self):
        """
        Ensures that the command is present in the job manifest. Populates the command
        with the `prefect -m prefect.engine` if a command is not present.
        """
        try:
            command = self.job_manifest["spec"]["template"]["spec"]["containers"][
                0
            ].get("args")
            if command is None:
                self.job_manifest["spec"]["template"]["spec"]["containers"][0][
                    "args"
                ] = shlex.split(self._base_flow_run_command())
            elif isinstance(command, str):
                self.job_manifest["spec"]["template"]["spec"]["containers"][0][
                    "args"
                ] = shlex.split(command)
            elif not isinstance(command, list):
                raise ValueError(
                    "Invalid job manifest template: 'command' must be a string or list."
                )
        except KeyError:
            raise ValueError(
                "Unable to verify command due to invalid job manifest template."
            )

    def _populate_generate_name_if_not_present(self):
        """Ensures that the generateName is present in the job manifest."""
        manifest_generate_name = self.job_manifest["metadata"].get("generateName", "")
        has_placeholder = len(find_placeholders(manifest_generate_name)) > 0
        # if name wasn't present during template rendering, generateName will be
        # just a hyphen
        manifest_generate_name_templated_with_empty_string = (
            manifest_generate_name == "-"
        )
        if (
            not manifest_generate_name
            or has_placeholder
            or manifest_generate_name_templated_with_empty_string
        ):
            generate_name = None
            if self.name:
                generate_name = _slugify_name(self.name)
            # _slugify_name will return None if the slugified name in an exception
            if not generate_name:
                generate_name = "prefect-job"
            self.job_manifest["metadata"]["generateName"] = f"{generate_name}-"

Functions

prepare_for_flow_run

Prepares the job configuration for a flow run.

Ensures that necessary values are present in the job manifest and that the job manifest is valid.

Parameters:

Name	Type	Description	Default
flow_run	FlowRun	The flow run to prepare the job configuration for	required
deployment	Optional[DeploymentResponse]	The deployment associated with the flow run used for preparation.	None
flow	Optional[Flow]	The flow associated with the flow run used for preparation.	None

Source code in prefect_kubernetes/worker.py

def prepare_for_flow_run(
    self,
    flow_run: "FlowRun",
    deployment: Optional["DeploymentResponse"] = None,
    flow: Optional["Flow"] = None,
):
    """
    Prepares the job configuration for a flow run.

    Ensures that necessary values are present in the job manifest and that the
    job manifest is valid.

    Args:
        flow_run: The flow run to prepare the job configuration for
        deployment: The deployment associated with the flow run used for
            preparation.
        flow: The flow associated with the flow run used for preparation.
    """
    super().prepare_for_flow_run(flow_run, deployment, flow)
    # Update configuration env and job manifest env
    self._update_prefect_api_url_if_local_server()
    self._populate_env_in_manifest()
    # Update labels in job manifest
    self._slugify_labels()
    # Add defaults to job manifest if necessary
    self._populate_image_if_not_present()
    self._populate_command_if_not_present()
    self._populate_generate_name_if_not_present()

KubernetesWorkerResult

Bases: BaseWorkerResult

Contains information about the final state of a completed process

Source code in prefect_kubernetes/worker.py

class KubernetesWorkerResult(BaseWorkerResult):
    """Contains information about the final state of a completed process"""

KubernetesWorkerVariables

Bases: BaseVariables

Default variables for the Kubernetes worker.

The schema for this class is used to populate the variables section of the default base job template.

Source code in prefect_kubernetes/worker.py

class KubernetesWorkerVariables(BaseVariables):
    """
    Default variables for the Kubernetes worker.

    The schema for this class is used to populate the `variables` section of the default
    base job template.
    """

    namespace: str = Field(
        default="default", description="The Kubernetes namespace to create jobs within."
    )
    image: Optional[str] = Field(
        default=None,
        description="The image reference of a container image to use for created jobs. "
        "If not set, the latest Prefect image will be used.",
        example="docker.io/prefecthq/prefect:2-latest",
    )
    service_account_name: Optional[str] = Field(
        default=None,
        description="The Kubernetes service account to use for job creation.",
    )
    image_pull_policy: Literal["IfNotPresent", "Always", "Never"] = Field(
        default=KubernetesImagePullPolicy.IF_NOT_PRESENT,
        description="The Kubernetes image pull policy to use for job containers.",
    )
    finished_job_ttl: Optional[int] = Field(
        default=None,
        title="Finished Job TTL",
        description="The number of seconds to retain jobs after completion. If set, "
        "finished jobs will be cleaned up by Kubernetes after the given delay. If not "
        "set, jobs will be retained indefinitely.",
    )
    job_watch_timeout_seconds: Optional[int] = Field(
        default=None,
        description=(
            "Number of seconds to wait for each event emitted by a job before "
            "timing out. If not set, the worker will wait for each event indefinitely."
        ),
    )
    pod_watch_timeout_seconds: int = Field(
        default=60,
        description="Number of seconds to watch for pod creation before timing out.",
    )
    stream_output: bool = Field(
        default=True,
        description=(
            "If set, output will be streamed from the job to local standard output."
        ),
    )
    cluster_config: Optional[KubernetesClusterConfig] = Field(
        default=None,
        description="The Kubernetes cluster config to use for job creation.",
    )

Functions