Source code for dask_kubernetes.operator.kubecluster.kubecluster

from __future__ import annotations

import asyncio
import atexit
import getpass
import logging
import os
import time
import uuid
import warnings
import weakref
from contextlib import suppress
from enum import Enum
from typing import ClassVar, Dict, List, Optional

import dask.config
import httpx
import kr8s
import yaml
from distributed.core import Status, rpc
from distributed.deploy import Cluster
from distributed.utils import Log, Logs, TimeoutError, format_dashboard_link
from kr8s.asyncio.objects import Pod, Service
from rich import box
from rich.console import Group
from rich.live import Live
from rich.panel import Panel
from rich.spinner import Spinner
from rich.table import Table
from tornado.ioloop import IOLoop

from dask_kubernetes.exceptions import CrashLoopBackOffError, SchedulerStartupError
from dask_kubernetes.operator._objects import (
    DaskAutoscaler,
    DaskCluster,
    DaskWorkerGroup,
)
from dask_kubernetes.operator.networking import (
    get_scheduler_address,
    wait_for_scheduler,
    wait_for_scheduler_comm,
)

logger = logging.getLogger(__name__)


class CreateMode(Enum):
    CREATE_ONLY = "CREATE_ONLY"
    CREATE_OR_CONNECT = "CREATE_OR_CONNECT"
    CONNECT_ONLY = "CONNECT_ONLY"


[docs]class KubeCluster(Cluster): """Launch a Dask Cluster on Kubernetes using the Operator This cluster manager creates a Dask cluster by deploying the necessary kubernetes resources the Dask Operator needs to create pods. It can also connect to an existing cluster by providing the name of the cluster. Parameters ---------- name: str (required) Name given the Dask cluster. namespace: str (optional) Namespace in which to launch the workers. Defaults to current namespace if available or "default" image: str (optional) Image to run in Scheduler and Worker Pods. n_workers: int Number of workers on initial launch. Use ``scale`` to change this number in the future resources: Dict[str, str] env: List[dict] | Dict[str, str] List of environment variables to pass to worker pod. Can be a list of dicts using the same structure as k8s envs or a single dictionary of key/value pairs worker_command: List[str] | str The command to use when starting the worker. If command consists of multiple words it should be passed as a list of strings. Defaults to ``"dask-worker"``. port_forward_cluster_ip: bool (optional) If the chart uses ClusterIP type services, forward the ports locally. If you are running it locally it should be the port you are forwarding to ``<port>``. create_mode: CreateMode (optional) How to handle cluster creation if the cluster resource already exists. Default behavior is to create a new cluster if one with that name doesn't exist, or connect to an existing one if it does. You can also set ``CreateMode.CREATE_ONLY`` to raise an exception if a cluster with that name already exists. Or ``CreateMode.CONNECT_ONLY`` to raise an exception if a cluster with that name doesn't exist. shutdown_on_close: bool (optional) Whether or not to delete the cluster resource when this object is closed. Defaults to ``True`` when creating a cluster and ``False`` when connecting to an existing one. idle_timeout: int (optional) If set Kubernetes will delete the cluster automatically if the scheduler is idle for longer than this timeout in seconds. resource_timeout: int (optional) Time in seconds to wait for Kubernetes resources to enter their expected state. Example: If the ``DaskCluster`` resource that gets created isn't moved into a known ``status.phase`` by the controller then it is likely the controller isn't running or is malfunctioning and we time out and clean up with a useful error. Example 2: If the scheduler Pod enters a ``CrashBackoffLoop`` state for longer than this timeout we give up with a useful error. Defaults to ``60`` seconds. scheduler_service_type: str (optional) Kubernetes service type to use for the scheduler. Defaults to ``ClusterIP``. jupyter: bool (optional) Start Jupyter on the scheduler node. custom_cluster_spec: str | dict (optional) Path to a YAML manifest or a dictionary representation of a ``DaskCluster`` resource object which will be used to create the cluster instead of generating one from the other keyword arguments. scheduler_forward_port: int (optional) The port to use when forwarding the scheduler dashboard. Will utilize a random port by default **kwargs: dict Additional keyword arguments to pass to LocalCluster Examples -------- >>> from dask_kubernetes.operator import KubeCluster >>> cluster = KubeCluster(name="foo") You can add another group of workers (default is 3 workers) >>> cluster.add_worker_group('additional', n=4) You can then resize the cluster with the scale method >>> cluster.scale(10) And optionally scale a specific worker group >>> cluster.scale(10, worker_group='additional') You can also resize the cluster adaptively and give it a range of workers >>> cluster.adapt(20, 50) You can pass this cluster directly to a Dask client >>> from dask.distributed import Client >>> client = Client(cluster) You can also access cluster logs >>> cluster.get_logs() You can also connect to an existing cluster >>> existing_cluster = KubeCluster.from_name(name="ialreadyexist") See Also -------- KubeCluster.from_name """ _instances: ClassVar[weakref.WeakSet[KubeCluster]] = weakref.WeakSet() def __init__( self, *, name: Optional[str] = None, namespace: Optional[str] = None, image: Optional[str] = None, n_workers: Optional[int] = None, resources: Optional[Dict[str, str]] = None, env: Optional[List[dict] | Dict[str, str]] = None, worker_command: Optional[List[str]] = None, port_forward_cluster_ip: Optional[bool] = None, create_mode: Optional[CreateMode] = None, shutdown_on_close: Optional[bool] = None, idle_timeout: Optional[int] = None, resource_timeout: Optional[int] = None, scheduler_service_type: Optional[str] = None, custom_cluster_spec: Optional[str | dict] = None, scheduler_forward_port: Optional[int] = None, jupyter: bool = False, loop: Optional[IOLoop] = None, asynchronous: bool = False, **kwargs, ): name = dask.config.get("kubernetes.name", override_with=name) self.namespace = dask.config.get( "kubernetes.namespace", override_with=namespace ) self.image = dask.config.get("kubernetes.image", override_with=image) self.n_workers = dask.config.get( "kubernetes.count.start", override_with=n_workers ) if dask.config.get("kubernetes.count.max"): warnings.warn( "Setting a maximum number of workers is no longer supported. " "Please use Kubernetes Resource Quotas instead." ) self.resources = dask.config.get( "kubernetes.resources", override_with=resources ) self.env = dask.config.get("kubernetes.env", override_with=env) self.worker_command = dask.config.get( "kubernetes.worker-command", override_with=worker_command ) self.port_forward_cluster_ip = dask.config.get( "kubernetes.port-forward-cluster-ip", override_with=port_forward_cluster_ip ) self.create_mode = dask.config.get( "kubernetes.create-mode", override_with=create_mode ) self.shutdown_on_close = dask.config.get( "kubernetes.shutdown-on-close", override_with=shutdown_on_close ) self._resource_timeout = dask.config.get( "kubernetes.resource-timeout", override_with=resource_timeout ) self._custom_cluster_spec = dask.config.get( "kubernetes.custom-cluster-spec", override_with=custom_cluster_spec ) self.scheduler_service_type = dask.config.get( "kubernetes.scheduler-service-type", override_with=scheduler_service_type ) self.scheduler_forward_port = dask.config.get( "kubernetes.scheduler-forward-port", override_with=scheduler_forward_port ) self.jupyter = dask.config.get( "kubernetes.scheduler-jupyter", override_with=jupyter ) self.idle_timeout = dask.config.get( "kubernetes.idle-timeout", override_with=idle_timeout ) if self._custom_cluster_spec: if isinstance(self._custom_cluster_spec, str): with open(self._custom_cluster_spec) as f: self._custom_cluster_spec = yaml.safe_load(f.read()) name = self._custom_cluster_spec["metadata"]["name"] if isinstance(self.worker_command, str): self.worker_command = self.worker_command.split(" ") try: # Validate `resources` param is a dictionary whose # keys must either be 'limits' or 'requests' assert isinstance( self.resources, dict ), f"resources must be dict type, found {type(resources)}" for field in self.resources: if field in ("limits", "requests"): assert isinstance( self.resources[field], dict ), f"key of '{field}' must be dict type" else: raise ValueError(f"resources has unknown field '{field}'") except AssertionError as e: raise TypeError from e name = name.format( user=getpass.getuser(), uuid=str(uuid.uuid4())[:10], **os.environ ) self._instances.add(self) self._rich_spinner = Spinner("dots", speed=0.5) self._startup_component_status: dict = {} super().__init__(name=name, loop=loop, asynchronous=asynchronous, **kwargs) # If https://github.com/dask/distributed/pull/7941 is merged we can # simplify the next 8 lines to ``if not self.called_from_running_loop:`` try: called_from_running_loop = ( getattr(loop, "asyncio_loop", None) is asyncio.get_running_loop() ) except RuntimeError: called_from_running_loop = asynchronous if not called_from_running_loop: self._loop_runner.start() self.sync(self._start) def _log(self, log): temp = self.quiet self.quiet = True super()._log(log) self.quiet = temp @property def dashboard_link(self): host = self.scheduler_address.split("://")[1].split("/")[0].split(":")[0] return format_dashboard_link(host, self.forwarded_dashboard_port) async def _start(self): if not self.namespace: api = await kr8s.asyncio.api() self.namespace = api.namespace try: watch_component_status_task = asyncio.create_task( self._watch_component_status() ) if not self.quiet: show_rich_output_task = asyncio.create_task(self._show_rich_output()) cluster = await DaskCluster(self.name, namespace=self.namespace) cluster_exists = await cluster.exists() if cluster_exists and self.create_mode == CreateMode.CREATE_ONLY: raise ValueError( f"Cluster {self.name} already exists and create mode is '{CreateMode.CREATE_ONLY}'" ) elif cluster_exists: self._log("Connecting to existing cluster") await self._connect_cluster() elif not cluster_exists and self.create_mode == CreateMode.CONNECT_ONLY: raise ValueError( f"Cluster {self.name} doesn't already exist and create " f"mode is '{CreateMode.CONNECT_ONLY}'" ) else: self._log("Creating cluster") await self._create_cluster() await super()._start() self._log(f"Ready, dashboard available at {self.dashboard_link}") finally: watch_component_status_task.cancel() if not self.quiet: show_rich_output_task.cancel() def __await__(self): async def _(): if self.status == Status.created: await self._start() return self return _().__await__() async def _create_cluster(self): if self.shutdown_on_close is None: self.shutdown_on_close = True if not self._custom_cluster_spec: self._log("Generating cluster spec") data = make_cluster_spec( name=self.name, env=self.env, resources=self.resources, worker_command=self.worker_command, n_workers=self.n_workers, image=self.image, scheduler_service_type=self.scheduler_service_type, idle_timeout=self.idle_timeout, jupyter=self.jupyter, ) else: data = self._custom_cluster_spec try: self._log("Creating DaskCluster object") cluster = await DaskCluster(data, namespace=self.namespace) await cluster.create() except httpx.HTTPStatusError as e: if e.response.status_code == 404: raise RuntimeError( "Failed to create DaskCluster resource." "Are the Dask Custom Resource Definitions installed? " "https://kubernetes.dask.org/en/latest/operator.html#installing-the-operator" ) from e else: raise e try: self._log("Waiting for controller to action cluster") await self._wait_for_controller() except TimeoutError as e: await self._close() raise e try: self._log("Waiting for scheduler pod") await wait_for_scheduler( self.name, self.namespace, timeout=self._resource_timeout, ) except CrashLoopBackOffError as e: scheduler_pod = await Pod.get( namespace=self.namespace, label_selector=f"dask.org/component=scheduler,dask.org/cluster-name={self.name}", ) await self._close() raise SchedulerStartupError( "Scheduler failed to start.", "Scheduler Pod logs:", await scheduler_pod.logs(), ) from e self._log("Waiting for scheduler service") await wait_for_service(f"{self.name}-scheduler", self.namespace) scheduler_address = await self._get_scheduler_address() self._log("Connecting to scheduler") await wait_for_scheduler_comm(scheduler_address) self.scheduler_comm = rpc(scheduler_address) local_port = self.scheduler_forward_port if local_port: local_port = int(local_port) self._log("Getting dashboard URL") dashboard_address = await get_scheduler_address( f"{self.name}-scheduler", self.namespace, port_name="http-dashboard", port_forward_cluster_ip=self.port_forward_cluster_ip, local_port=local_port, ) self.forwarded_dashboard_port = dashboard_address.split(":")[-1] async def _connect_cluster(self): if self.shutdown_on_close is None: self.shutdown_on_close = False cluster = await DaskCluster.get(self.name, namespace=self.namespace) container_spec = cluster.spec.worker.spec.containers[0] self.image = container_spec.image self.n_workers = cluster.replicas if "resources" in container_spec: self.resources = container_spec.resources else: self.resources = None if "env" in container_spec: self.env = container_spec.env else: self.env = {} self.jupyter = "--jupyter" in cluster.spec.scheduler.spec.containers[0].args service_name = f"{cluster.name}-scheduler" self._log("Waiting for scheduler pod") await wait_for_scheduler( self.name, self.namespace, timeout=self._resource_timeout ) self._log("Waiting for scheduler service") await wait_for_service(service_name, self.namespace) scheduler_address = await self._get_scheduler_address() self._log("Connecting to scheduler") await wait_for_scheduler_comm(scheduler_address) self.scheduler_comm = rpc(scheduler_address) local_port = self.scheduler_forward_port if local_port: local_port = int(local_port) self._log("Getting dashboard URL") dashboard_address = await get_scheduler_address( service_name, self.namespace, port_name="http-dashboard", port_forward_cluster_ip=self.port_forward_cluster_ip, local_port=local_port, ) self.forwarded_dashboard_port = dashboard_address.split(":")[-1] async def _get_scheduler_address(self): address = await get_scheduler_address( f"{self.name}-scheduler", self.namespace, port_forward_cluster_ip=self.port_forward_cluster_ip, ) return address async def _wait_for_controller(self): """Wait for the operator to set the status.phase.""" start = time.time() cluster = await DaskCluster.get(self.name, namespace=self.namespace, timeout=30) while start + self._resource_timeout > time.time(): if await cluster.ready(): return await asyncio.sleep(0.25) raise TimeoutError( f"Dask Cluster resource not actioned after {self._resource_timeout} seconds, is the Dask Operator running?" ) async def _watch_component_status(self): while True: # Get DaskCluster status with suppress(kr8s.NotFoundError): cluster = await DaskCluster.get(self.name, namespace=self.namespace) if "status" in cluster.raw and "phase" in cluster.status: self._startup_component_status["cluster"] = cluster.status.phase # Get Scheduler Pod status with suppress(kr8s.NotFoundError): scheduler_pod = await Pod.get( namespace=self.namespace, label_selector=f"dask.org/component=scheduler,dask.org/cluster-name={self.name}", ) phase = scheduler_pod.status.phase if scheduler_pod.status.phase == "Running": if not await scheduler_pod.ready(): phase = "Health Checking" if "container_statuses" in scheduler_pod.status: for container in scheduler_pod.status.container_statuses: if "waiting" in container.state: phase = container.state.waiting.reason self._startup_component_status["schedulerpod"] = phase # Get Scheduler Service status with suppress(kr8s.NotFoundError): await Service.get(self.name + "-scheduler", namespace=self.namespace) self._startup_component_status["schedulerservice"] = "Created" # Get DaskWorkerGroup status with suppress(kr8s.NotFoundError): await DaskWorkerGroup.get( self.name + "-default", namespace=self.namespace ) self._startup_component_status["workergroup"] = "Created" await asyncio.sleep(1) async def generate_rich_output(self): table = Table(show_header=False, box=box.SIMPLE, expand=True) table.add_column("Component") table.add_column("Status", justify="right") for label, component in [ ("DaskCluster", "cluster"), ("Scheduler Pod", "schedulerpod"), ("Scheduler Service", "schedulerservice"), ("Default Worker Group", "workergroup"), ]: try: status = self._startup_component_status[component] except KeyError: status = "-" if status in ["Running", "Created"]: status = "[green]" + status if status in ["Pending", "Health Checking"]: status = "[yellow]" + status if status in ["CrashLoopBackOff", "Error"]: status = "[red]" + status table.add_row(label, status) if self._cluster_manager_logs: self._rich_spinner.update(text=self._cluster_manager_logs[-1][1]) return Panel( Group(table, self._rich_spinner), title=f"Creating KubeCluster [magenta]'{self.name}'", width=80, ) async def _show_rich_output(self): with Live(await self.generate_rich_output(), auto_refresh=False) as live: while True: await asyncio.sleep(0.25) live.update(await self.generate_rich_output(), refresh=True)
[docs] def get_logs(self): """Get logs for Dask scheduler and workers. Examples -------- >>> cluster.get_logs() {'foo': ..., 'foo-default-worker-0269dbfa0cfd4a22bcd9d92ae032f4d2': ..., 'foo-default-worker-7c1ccb04cd0e498fb21babaedd00e5d4': ..., 'foo-default-worker-d65bee23bdae423b8d40c5da7a1065b6': ...} Each log will be a string of all logs for that container. To view it is recommeded that you print each log. >>> print(cluster.get_logs()["testdask-scheduler-5c8ffb6b7b-sjgrg"]) ... distributed.scheduler - INFO - ----------------------------------------------- distributed.scheduler - INFO - Clear task state distributed.scheduler - INFO - Scheduler at: tcp://10.244.0.222:8786 distributed.scheduler - INFO - dashboard at: :8787 ... """ return self.sync(self._get_logs)
async def _get_logs(self): logs = Logs() pods = await kr8s.asyncio.get( "pods", namespace=self.namespace, label_selector=f"dask.org/cluster-name={self.name}", ) for pod in pods: if "scheduler" in pod.name or "worker" in pod.name: try: if pod.status.phase != "Running": raise ValueError( f"Cannot get logs for pod with status {pod.status.phase}.", ) log = Log(await pod.logs()) except ValueError: log = Log(f"Cannot find logs. Pod is {pod.status.phase}.") logs[pod.name] = log return logs
[docs] def add_worker_group( self, name, n_workers=3, image=None, resources=None, worker_command=None, env=None, custom_spec=None, ): """Create a dask worker group by name Parameters ---------- name: str Name of the worker group n_workers: int Number of workers on initial launch. Use ``.scale(n_workers, worker_group=name)`` to change this number in the future. image: str (optional) Image to run in Scheduler and Worker Pods. If ommitted will use the cluster default. resources: Dict[str, str] Resources to be passed to the underlying pods. If ommitted will use the cluster default. env: List[dict] List of environment variables to pass to worker pod. If ommitted will use the cluster default. custom_spec: dict (optional) A dictionary representation of a worker spec which will be used to create the ``DaskWorkerGroup`` instead of generating one from the other keyword arguments. Examples -------- >>> cluster.add_worker_group("high-mem-workers", n_workers=5) """ return self.sync( self._add_worker_group, name=name, n_workers=n_workers, image=image, resources=resources, worker_command=worker_command, env=env, custom_spec=custom_spec, )
async def _add_worker_group( self, name, n_workers=3, image=None, resources=None, worker_command=None, env=None, custom_spec=None, ): if custom_spec is not None: spec = custom_spec else: spec = make_worker_spec( env=env or self.env, resources=resources or self.resources, worker_command=worker_command or self.worker_command, n_workers=n_workers or self.n_workers, image=image or self.image, ) wg = await DaskWorkerGroup( { "apiVersion": "kubernetes.dask.org/v1", "kind": "DaskWorkerGroup", "metadata": { "name": f"{self.name}-{name}", "namespace": self.namespace, }, "spec": { "cluster": f"{self.name}", "worker": spec, }, } ) await wg.create()
[docs] def delete_worker_group(self, name): """Delete a dask worker group by name Parameters ---------- name: str Name of the worker group Examples -------- >>> cluster.delete_worker_group("high-mem-workers") """ return self.sync(self._delete_worker_group, name)
async def _delete_worker_group(self, name): wg = await DaskWorkerGroup(f"{self.name}-{name}", namespace=self.namespace) await wg.delete()
[docs] def close(self, timeout=3600): """Delete the dask cluster""" return self.sync(self._close, timeout=timeout)
async def _close(self, timeout=3600): await super()._close() if self.shutdown_on_close: try: cluster = await DaskCluster.get(self.name, namespace=self.namespace) await cluster.delete() except kr8s.NotFoundError: logger.warning( "Failed to delete DaskCluster, looks like it has already been deleted." ) return start = time.time() while await cluster.exists(): if time.time() > start + timeout: raise TimeoutError( f"Timed out deleting cluster resource {self.name}" ) await asyncio.sleep(1)
[docs] def scale(self, n, worker_group="default"): """Scale cluster to n workers Parameters ---------- n : int Target number of workers worker_group : str Worker group to scale Examples -------- >>> cluster.scale(10) # scale cluster to ten workers >>> cluster.scale(7, worker_group="high-mem-workers") # scale worker group high-mem-workers to seven workers """ return self.sync(self._scale, n, worker_group)
async def _scale(self, n, worker_group="default"): # Disable adaptivity if enabled with suppress(kr8s.NotFoundError): autoscaler = await DaskAutoscaler(self.name, self.namespace) await autoscaler.delete() wg = await DaskWorkerGroup( f"{self.name}-{worker_group}", namespace=self.namespace ) await wg.scale(n) for instance in self._instances: if instance.name == self.name: instance.scheduler_info = self.scheduler_info
[docs] def adapt(self, minimum=None, maximum=None): """Turn on adaptivity Parameters ---------- minimum : int Minimum number of workers minimum : int Maximum number of workers Examples -------- >>> cluster.adapt() # Allow scheduler to add/remove workers within k8s cluster resource limits >>> cluster.adapt(minimum=1, maximum=10) # Allow scheduler to add/remove workers within 1-10 range """ return self.sync(self._adapt, minimum, maximum)
async def _adapt(self, minimum=None, maximum=None): autoscaler = await DaskAutoscaler( { "apiVersion": "kubernetes.dask.org/v1", "kind": "DaskAutoscaler", "metadata": { "name": self.name, "dask.org/cluster-name": self.name, "dask.org/component": "autoscaler", }, "spec": { "cluster": self.name, "minimum": minimum, "maximum": maximum, }, }, self.namespace, ) try: await autoscaler.patch({"spec": {"minimum": minimum, "maximum": maximum}}) except kr8s.NotFoundError: await autoscaler.create() def __enter__(self): return self def __exit__(self, typ, value, traceback): self.close()
[docs] @classmethod def from_name(cls, name, **kwargs): """Create an instance of this class to represent an existing cluster by name. Will fail if a cluster with that name doesn't already exist. Parameters ---------- name: str Name of the cluster to connect to Examples -------- >>> cluster = KubeCluster.from_name(name="simple-cluster") """ defaults = {"create_mode": CreateMode.CONNECT_ONLY, "shutdown_on_close": False} kwargs = defaults | kwargs return cls( name=name, **kwargs, )
@property def jupyter_link(self): if self.jupyter: return self.dashboard_link.replace("/status", "/jupyter/lab") raise RuntimeError("KubeCluster not started with jupyter enabled")
[docs]def make_cluster_spec( name, image="ghcr.io/dask/dask:latest", n_workers=None, resources=None, env=None, worker_command="dask-worker", scheduler_service_type="ClusterIP", idle_timeout=0, jupyter=False, ): """Generate a ``DaskCluster`` kubernetes resource. Populate a template with some common options to generate a ``DaskCluster`` kubernetes resource. Parameters ---------- name: str Name of the cluster image: str (optional) Container image to use for the scheduler and workers n_workers: int (optional) Number of workers in the default worker group resources: dict (optional) Resource limits to set on scheduler and workers env: dict (optional) Environment variables to set on scheduler and workers worker_command: str (optional) Worker command to use when starting the workers idle_timeout: int (optional) Timeout to cleanup idle cluster jupyter: bool (optional) Start Jupyter on the Dask scheduler """ return { "apiVersion": "kubernetes.dask.org/v1", "kind": "DaskCluster", "metadata": {"name": name}, "spec": { "idleTimeout": idle_timeout, "worker": make_worker_spec( env=env, resources=resources, worker_command=worker_command, n_workers=n_workers, image=image, ), "scheduler": make_scheduler_spec( cluster_name=name, env=env, resources=resources, image=image, scheduler_service_type=scheduler_service_type, jupyter=jupyter, ), }, }
[docs]def make_worker_spec( image="ghcr.io/dask/dask:latest", n_workers=3, resources=None, env=None, worker_command="dask-worker", ): if isinstance(env, dict): env = [{"name": key, "value": value} for key, value in env.items()] else: # If they gave us a list, assume its a list of dicts and already ready to go env = env if isinstance(worker_command, str): worker_command = worker_command.split(" ") args = worker_command + [ "--name", "$(DASK_WORKER_NAME)", "--dashboard", "--dashboard-address", "8788", ] return { "replicas": n_workers, "spec": { "containers": [ { "name": "worker", "image": image, "args": args, "env": env, "resources": resources, "ports": [ { "name": "http-dashboard", "containerPort": 8788, "protocol": "TCP", }, ], } ] }, }
def make_scheduler_spec( cluster_name, env=None, resources=None, image="ghcr.io/dask/dask:latest", scheduler_service_type="ClusterIP", jupyter=False, ): # TODO: Take the values provided in the current class constructor # and build a DaskWorker compatible dict if isinstance(env, dict): env = [{"name": key, "value": value} for key, value in env.items()] else: # If they gave us a list, assume its a list of dicts and already ready to go env = env args = ["dask-scheduler", "--host", "0.0.0.0"] if jupyter: args.append("--jupyter") return { "spec": { "containers": [ { "name": "scheduler", "image": image, "args": args, "env": env, "resources": resources, "ports": [ { "name": "tcp-comm", "containerPort": 8786, "protocol": "TCP", }, { "name": "http-dashboard", "containerPort": 8787, "protocol": "TCP", }, ], "readinessProbe": { "httpGet": {"port": "http-dashboard", "path": "/health"}, "initialDelaySeconds": 0, "periodSeconds": 1, "timeoutSeconds": 300, }, "livenessProbe": { "httpGet": {"port": "http-dashboard", "path": "/health"}, "initialDelaySeconds": 15, "periodSeconds": 20, }, } ] }, "service": { "type": scheduler_service_type, "selector": { "dask.org/cluster-name": cluster_name, "dask.org/component": "scheduler", }, "ports": [ { "name": "tcp-comm", "protocol": "TCP", "port": 8786, "targetPort": "tcp-comm", }, { "name": "http-dashboard", "protocol": "TCP", "port": 8787, "targetPort": "http-dashboard", }, ], }, } async def wait_for_service(service_name, namespace): """Block until service is available.""" while True: with suppress(kr8s.NotFoundError): service = await Service.get(service_name, namespace) if await service.ready(): return await asyncio.sleep(0.1) @atexit.register def reap_clusters(): async def _reap_clusters(): for cluster in list(KubeCluster._instances): if cluster.shutdown_on_close and cluster.status != Status.closed: with suppress(TimeoutError): if cluster.asynchronous: await cluster.close(timeout=10) else: cluster.close(timeout=10) asyncio.run(_reap_clusters())