KubeCluster (classic)
Contents
KubeCluster (classic)¶
Warning
This implementation of KubeCluster
is being retired and we recommend migrating to the operator based implementation.
KubeCluster
deploys Dask clusters on Kubernetes clusters using native
Kubernetes APIs. It is designed to dynamically launch ad-hoc deployments.
Quickstart¶
To launch a Dask cluster on Kubernetes with KubeCluster
you need to first configure your worker
pod specification. Then create a cluster with that spec.
from dask_kubernetes.classic import KubeCluster, make_pod_spec
pod_spec = make_pod_spec(image='ghcr.io/dask/dask:latest',
memory_limit='4G', memory_request='4G',
cpu_limit=1, cpu_request=1)
cluster = KubeCluster(pod_spec)
cluster.scale(10) # specify number of workers explicitly
cluster.adapt(minimum=1, maximum=100) # or dynamically scale based on current workload
You can then connect a Dask dask.distributed.Client
object to the cluster and perform your work.
# Example usage
from dask.distributed import Client
import dask.array as da
# Connect Dask to the cluster
client = Client(cluster)
# Create a large array and calculate the mean
array = da.ones((1000, 1000, 1000))
print(array.mean().compute()) # Should print 1.0
You can alternatively define your worker specification via YAML by creating a pod manifest that will be used as a template.
# worker-spec.yml
kind: Pod
metadata:
labels:
foo: bar
spec:
restartPolicy: Never
containers:
- image: ghcr.io/dask/dask:latest
imagePullPolicy: IfNotPresent
args: [dask-worker, --nthreads, '2', --no-dashboard, --memory-limit, 6GB, --death-timeout, '60']
name: dask-worker
env:
- name: EXTRA_PIP_PACKAGES
value: git+https://github.com/dask/distributed
resources:
limits:
cpu: "2"
memory: 6G
requests:
cpu: "2"
memory: 6G
from dask_kubernetes.classic import KubeCluster
cluster = KubeCluster('worker-spec.yml')
cluster.scale(10)
For more information see the KubeCluster
API reference.
Best Practices¶
Your worker pod image should have a similar environment to your local environment, including versions of Python, dask, cloudpickle, and any libraries that you may wish to use (like NumPy, Pandas, or Scikit-Learn). See
dask_kubernetes.classic.KubeCluster
docstring for guidance on how to check and modify this.Your Kubernetes resource limits and requests should match the
--memory-limit
and--nthreads
parameters given to thedask-worker
command. Otherwise your workers may get killed by Kubernetes as they pack into the same node and overwhelm that nodes’ available memory, leading toKilledWorker
errors.We recommend adding the
--death-timeout, '60'
arguments and therestartPolicy: Never
attribute to your worker specification. This ensures that these pods will clean themselves up if your Python process disappears unexpectedly.
GPUs¶
Because dask-kubernetes
uses standard kubernetes pod specifications, we can
use kubernetes device plugins
and add resource limits defining the number of GPUs per pod/worker.
Additionally, we can also use tools like dask-cuda for optimized Dask/GPU interactions.
kind: Pod
metadata:
labels:
foo: bar
spec:
restartPolicy: Never
containers:
- image: nvcr.io/nvidia/rapidsai/rapidsai-core:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
imagePullPolicy: IfNotPresent
args: [dask-cuda-worker, $(DASK_SCHEDULER_ADDRESS), --rmm-pool-size, 10GB]
name: dask-cuda
resources:
limits:
cpu: "2"
memory: 6G
nvidia.com/gpu: 1 # requesting 1 GPU
requests:
cpu: "2"
memory: 6G
nvidia.com/gpu: 1 # requesting 1 GPU
Configuration¶
You can use Dask’s configuration to control the behavior of Dask-kubernetes. You can see a full set of configuration options here. Some notable ones are described below:
kubernetes.worker-template-path
: a path to a YAML file that holds a Pod spec for the worker. If provided then this will be used whendask_kubernetes.classic.KubeCluster
is called with no arguments:cluster = KubeCluster() # reads provided yaml file
distributed.dashboard.link
: a Python pre-formatted string that shows the location of Dask’s dashboard. This string will receive values forhost
,port
, and all environment variables.For example this is useful when using dask-kubernetes with JupyterHub and nbserverproxy to route the dashboard link to a proxied address as follows:
"{JUPYTERHUB_SERVICE_PREFIX}proxy/{port}/status"
kubernetes.worker-name
: a Python pre-formatted string to use when naming dask worker pods. This string will receive values foruser
,uuid
, and all environment variables. This is useful when you want to have control over the naming convention for your pods and use other tokens from the environment. For example when using zero-to-jupyterhub every user is calledjovyan
and so you may wish to usedask-{JUPYTERHUB_USER}-{uuid}
instead ofdask-{user}-{uuid}
. Ensure you keep the ``uuid`` somewhere in the template.
Role-Based Access Control (RBAC)¶
In order to spawn a Dask cluster, the service account creating those pods will require a set of RBAC permissions. Create a service account you will use for Dask, and then attach the following Role to that ServiceAccount via a RoleBinding:
kind: Role
apiVersion: rbac.authorization.k8s.io/v1beta1
metadata:
name: daskKubernetes
rules:
- apiGroups:
- "" # indicates the core API group
resources:
- "pods"
verbs:
- "get"
- "list"
- "watch"
- "create"
- "delete"
- apiGroups:
- "" # indicates the core API group
resources:
- "pods/log"
verbs:
- "get"
- "list"
- apiGroups:
- "" # indicates the core API group
resources:
- "services"
verbs:
- "get"
- "list"
- "watch"
- "create"
- "delete"
- apiGroups:
- "policy" # indicates the policy API group
resources:
- "poddisruptionbudgets"
verbs:
- "get"
- "list"
- "watch"
- "create"
- "delete"
Docker Images¶
Example Dask docker images ghcr.io/dask/dask and ghcr.io/dask/dask-notebook are available on https://github.com/orgs/dask/packages . More information about these images is available at the Dask documentation.
Note that these images can be further customized with extra packages using
EXTRA_PIP_PACKAGES
, EXTRA_APT_PACKAGES
, and EXTRA_CONDA_PACKAGES
as described in the
Extensibility section.
Deployment Details¶
Scheduler¶
Before workers are created a scheduler will be deployed with the following resources:
A pod with a scheduler running
A service (svc) to expose scheduler and dashboard ports
A PodDisruptionBudget avoid voluntary disruptions of the scheduler pod
By default the Dask configuration option kubernetes.scheduler-service-type
is
set to ClusterIp
. In order to connect to the scheduler the KubeCluster
will first attempt to connect directly,
but this will only be successful if dask-kubernetes
is being run from within the Kubernetes cluster.
If it is unsuccessful it will attempt to port forward the service locally using the kubectl
utility.
If you update the service type to NodePort
. The scheduler will be exposed on the same random high port on all
nodes in the cluster. In this case KubeCluster
will attempt to list nodes in order to get an IP to connect on
and requires additional permissions to do so.
- apiGroups:
- "" # indicates the core API group
resources:
- "nodes"
verbs:
- "get"
- "list"
If you set the service type to LoadBalancer
then KubeCluster
will connect to the external address of the assigned
loadbalancer, but this does require that your Kubernetes cluster has the appropriate operator to assign loadbalancers.
Legacy mode¶
For backward compatibility with previous versions of dask-kubernetes
it is also possible to run the scheduler locally.
A local
scheduler is created where the Dask client will be created.
from dask_kubernetes.classic import KubeCluster
from dask.distributed import Client
cluster = KubeCluster.from_yaml('worker-spec.yml', deploy_mode='local')
cluster.scale(10)
client = Client(cluster)
In this mode the Dask workers will attempt to connect to the machine where you are running dask-kubernetes
.
Generally this will need to be within the Kubernetes cluster in order for the workers to make a successful connection.
Workers¶
Workers are created directly as simple pods. These worker pods are configured
to shutdown if they are unable to connect to the scheduler for 60 seconds.
The pods are cleaned up when close()
is called,
or the scheduler process exits.
The pods are created with two default tolerations:
k8s.dask.org/dedicated=worker:NoSchedule
k8s.dask.org_dedicated=worker:NoSchedule
If you have nodes with the corresponding taints, then the worker pods will schedule to those nodes (and no other pods will be able to schedule to those nodes).
API¶
|
Launch a Dask cluster on Kubernetes |
|
Turn on adaptivity |
|
Create cluster with worker pod spec defined by Python dictionary |
|
Create cluster with worker pod spec defined by a YAML file |
|
Return logs for the cluster, scheduler and workers |
Scale cluster to n workers |
|
Configure the Kubernetes connection from a container's environment. |
|
|
Configure the Kubernetes connection from a kubeconfig file. |
|
Configure the Kubernetes connection explicitly. |
|
Create generic pod template from input parameters |
- class dask_kubernetes.KubeCluster(pod_template=None, name=None, namespace=None, n_workers=None, host=None, port=None, env=None, auth=[<dask_kubernetes.common.auth.InCluster object>, <dask_kubernetes.common.auth.KubeConfig object>], idle_timeout=None, deploy_mode=None, interface=None, protocol=None, dashboard_address=None, security=None, scheduler_service_wait_timeout=None, scheduler_service_name_resolution_retries=None, scheduler_pod_template=None, apply_default_affinity='preferred', **kwargs)[source]¶
Launch a Dask cluster on Kubernetes
This starts a local Dask scheduler and then dynamically launches Dask workers on a Kubernetes cluster. The Kubernetes cluster is taken to be either the current one on which this code is running, or as a fallback, the default one configured in a kubeconfig file.
Environments
Your worker pod image should have a similar environment to your local environment, including versions of Python, dask, cloudpickle, and any libraries that you may wish to use (like NumPy, Pandas, or Scikit-Learn). See examples below for suggestions on how to manage and check for this.
Network
Since the Dask scheduler is launched locally, for it to work, we need to be able to open network connections between this local node and all the workers nodes on the Kubernetes cluster. If the current process is not already on a Kubernetes node, some network configuration will likely be required to make this work.
Resources
Your Kubernetes resource limits and requests should match the
--memory-limit
and--nthreads
parameters given to thedask-worker
command.- Parameters
- pod_template: (kubernetes.client.V1Pod, dict, str)
A Kubernetes specification for a Pod for a dask worker. Can be either a
V1Pod
, a dict representation of a pod, or a path to a yaml file containing a pod specification.- scheduler_pod_template: kubernetes.client.V1Pod (optional)
A Kubernetes specification for a Pod for a dask scheduler. Defaults to the pod_template.
- name: str (optional)
Name given to the pods. Defaults to
dask-$USER-random
- namespace: str (optional)
Namespace in which to launch the workers. Defaults to current namespace if available or “default”
- n_workers: int
Number of workers on initial launch. Use
scale
to change this number in the future- env: Dict[str, str]
Dictionary of environment variables to pass to worker pod
- host: str
Listen address for local scheduler. Defaults to 0.0.0.0
- port: int
Port of local scheduler
- auth: List[ClusterAuth] (optional)
Configuration methods to attempt in order. Defaults to
[InCluster(), KubeConfig()]
.- idle_timeout: str (optional)
The scheduler task will exit after this amount of time if there are no requests from the client. Default is to never timeout.
- scheduler_service_wait_timeout: int (optional)
Timeout, in seconds, to wait for the remote scheduler service to be ready. Defaults to 30 seconds. Set to 0 to disable the timeout (not recommended).
- scheduler_service_name_resolution_retries: int (optional)
Number of retries to resolve scheduler service name when running from within the Kubernetes cluster. Defaults to 20. Must be set to 1 or greater.
- deploy_mode: str (optional)
Run the scheduler as “local” or “remote”. Defaults to
"remote"
.- apply_default_affinity: str (optional)
Apply a default affinity to pods: “required”, “preferred” or “none” Defaults to
"preferred"
.- **kwargs: dict
Additional keyword arguments to pass to SpecCluster
See also
KubeCluster.adapt
Examples
>>> from dask_kubernetes.classic import KubeCluster, make_pod_spec >>> pod_spec = make_pod_spec(image='ghcr.io/dask/dask:latest', ... memory_limit='4G', memory_request='4G', ... cpu_limit=1, cpu_request=1, ... env={'EXTRA_PIP_PACKAGES': 'fastparquet git+https://github.com/dask/distributed'}) >>> cluster = KubeCluster(pod_spec) >>> cluster.scale(10)
You can also create clusters with worker pod specifications as dictionaries or stored in YAML files
>>> cluster = KubeCluster('worker-template.yml') >>> cluster = KubeCluster({...})
Rather than explicitly setting a number of workers you can also ask the cluster to allocate workers dynamically based on current workload
>>> cluster.adapt()
You can pass this cluster directly to a Dask client
>>> from dask.distributed import Client >>> client = Client(cluster)
You can verify that your local environment matches your worker environments by calling
client.get_versions(check=True)
. This will raise an informative error if versions do not match.>>> client.get_versions(check=True)
The
ghcr.io/dask/dask
docker images supportEXTRA_PIP_PACKAGES
,EXTRA_APT_PACKAGES
andEXTRA_CONDA_PACKAGES
environment variables to help with small adjustments to the worker environments. We recommend the use of pip over conda in this case due to a much shorter startup time. These environment variables can be modified directly from the KubeCluster constructor methods using theenv=
keyword. You may list as many packages as you like in a single string like the following:>>> pip = 'pyarrow gcsfs git+https://github.com/dask/distributed' >>> conda = '-c conda-forge scikit-learn' >>> KubeCluster(..., env={'EXTRA_PIP_PACKAGES': pip, ... 'EXTRA_CONDA_PACKAGES': conda})
You can also start a KubeCluster with no arguments if the worker template is specified in the Dask config files, either as a full template in
kubernetes.worker-template
or a path to a YAML file inkubernetes.worker-template-path
.See https://docs.dask.org/en/latest/configuration.html for more information about setting configuration values.:
$ export DASK_KUBERNETES__WORKER_TEMPLATE_PATH=worker_template.yaml
>>> cluster = KubeCluster() # automatically finds 'worker_template.yaml'
- Attributes
asynchronous
Are we running in the event loop?
- called_from_running_loop
- dashboard_link
- loop
- name
- observed
- plan
- requested
- scheduler_address
Methods
adapt
([Adaptive, minimum, maximum, ...])Turn on adaptivity
from_dict
(pod_spec, **kwargs)Create cluster with worker pod spec defined by Python dictionary
from_name
(name)Create an instance of this class to represent an existing cluster by name.
from_yaml
(yaml_path, **kwargs)Create cluster with worker pod spec defined by a YAML file
get_client
()Return client for the cluster
get_logs
([cluster, scheduler, workers])Return logs for the cluster, scheduler and workers
new_worker_spec
()Return name and spec for the next worker
scale
(n)Scale cluster to n workers
scale_up
([n, memory, cores])Scale cluster to n workers
sync
(func, *args[, asynchronous, ...])Call func with args synchronously or asynchronously depending on the calling context
wait_for_workers
([n_workers, timeout])Blocking call to wait for n workers before continuing
close
logs
scale_down
- classmethod from_dict(pod_spec, **kwargs)[source]¶
Create cluster with worker pod spec defined by Python dictionary
Deprecated, please use the KubeCluster constructor directly.
See also
Examples
>>> spec = { ... 'metadata': {}, ... 'spec': { ... 'containers': [{ ... 'args': ['dask-worker', '$(DASK_SCHEDULER_ADDRESS)', ... '--nthreads', '1', ... '--death-timeout', '60'], ... 'command': None, ... 'image': 'ghcr.io/dask/dask:latest', ... 'name': 'dask-worker', ... }], ... 'restartPolicy': 'Never', ... } ... } >>> cluster = KubeCluster.from_dict(spec, namespace='my-ns')
- classmethod from_yaml(yaml_path, **kwargs)[source]¶
Create cluster with worker pod spec defined by a YAML file
Deprecated, please use the KubeCluster constructor directly.
We can start a cluster with pods defined in an accompanying YAML file like the following:
kind: Pod metadata: labels: foo: bar baz: quux spec: containers: - image: ghcr.io/dask/dask:latest name: dask-worker args: [dask-worker, $(DASK_SCHEDULER_ADDRESS), --nthreads, '2', --memory-limit, 8GB] restartPolicy: Never
See also
Examples
>>> cluster = KubeCluster.from_yaml('pod.yaml', namespace='my-ns')
- class dask_kubernetes.ClusterAuth[source]¶
An abstract base class for methods for configuring a connection to a Kubernetes API server.
Examples
>>> from dask_kubernetes import KubeConfig >>> auth = KubeConfig(context='minikube')
>>> from dask_kubernetes import KubeAuth >>> auth = KubeAuth(host='https://localhost', username='superuser', password='pass')
Methods
load
()Load Kubernetes configuration and set as default
load_first
([auth])Load the first valid configuration in the list auth.
- class dask_kubernetes.InCluster[source]¶
Configure the Kubernetes connection from a container’s environment.
This authentication method is intended for use when the client is running in a container started by Kubernetes with an authorized service account. This loads the mounted service account token and discovers the Kubernetes API via Kubernetes service discovery.
Methods
load
()Load Kubernetes configuration and set as default
load_first
([auth])Load the first valid configuration in the list auth.
- class dask_kubernetes.KubeConfig(config_file=None, context=None, persist_config=True)[source]¶
Configure the Kubernetes connection from a kubeconfig file.
- Parameters
- config_file: str (optional)
The path of the kubeconfig file to load. Defaults to the value of the
KUBECONFIG
environment variable, or the string~/.kube/config
.- context: str (optional)
The kubeconfig context to use. Defaults to the value of
current-context
in the configuration file.- persist_config: bool (optional)
Whether changes to the configuration will be saved back to disk (e.g. GCP token refresh). Defaults to
True
.
Methods
get_kube_config_loader_for_yaml_file
()load
()Load Kubernetes configuration and set as default
load_first
([auth])Load the first valid configuration in the list auth.
load_kube_config
()
- class dask_kubernetes.KubeAuth(host, **kwargs)[source]¶
Configure the Kubernetes connection explicitly.
- Parameters
- host: str
The base URL of the Kubernetes host to connect
- username: str (optional)
Username for HTTP basic authentication
- password: str (optional)
Password for HTTP basic authentication
- debug: bool (optional)
Debug switch
- verify_ssl: bool (optional)
Set this to false to skip verifying SSL certificate when calling API from https server. Defaults to
True
.- ssl_ca_cert: str (optional)
Set this to customize the certificate file to verify the peer.
- cert_file: str (optional)
Client certificate file
- key_file: str (optional)
Client key file
- assert_hostname: bool (optional)
Set this to True/False to enable/disable SSL hostname verification. Defaults to True.
- proxy: str (optional)
URL for a proxy to connect through
Methods
load
()Load Kubernetes configuration and set as default
load_first
([auth])Load the first valid configuration in the list auth.
- dask_kubernetes.make_pod_spec(image, labels={}, threads_per_worker=1, env={}, extra_container_config={}, extra_pod_config={}, resources=None, memory_limit=None, memory_request=None, cpu_limit=None, cpu_request=None, gpu_limit=None, annotations={})[source]¶
Create generic pod template from input parameters
- Parameters
- imagestr
Docker image name
- labelsdict
Dict of labels to pass to
V1ObjectMeta
- threads_per_workerint
Number of threads per each worker
- envdict
Dict of environment variables to pass to
V1Container
- extra_container_configdict
Extra config attributes to set on the container object
- extra_pod_configdict
Extra config attributes to set on the pod object
- resourcesstr
Resources for task constraints like “GPU=2 MEM=10e9”. Resources are applied separately to each worker process (only relevant when starting multiple worker processes. Passed to the –resources option in
dask-worker
.- memory_limitint, float, or str
Bytes of memory per process that the worker can use (applied to both
dask-worker --memory-limit
andspec.containers[].resources.limits.memory
). This can be:an integer (bytes), note 0 is a special case for no memory management.
a float (bytes). Note: fraction of total system memory is not supported by k8s.
a string (like 5GiB or 5000M). Note: ‘GB’ is not supported by k8s.
‘auto’ for automatically computing the memory limit. [default: auto]
- memory_requestint, float, or str
Like
memory_limit
(applied only tospec.containers[].resources.requests.memory
and ignored bydask-worker
).- cpu_limitfloat or str
CPU resource limits (applied to
spec.containers[].resources.limits.cpu
).- cpu_requestfloat or str
CPU resource requests (applied to
spec.containers[].resources.requests.cpu
).- gpu_limitint
GPU resource limits (applied to
spec.containers[].resources.limits."nvidia.com/gpu"
).- annotationsdict
Dict of annotations passed to
V1ObjectMeta
- Returns
- podV1PodSpec
Examples
>>> make_pod_spec(image='ghcr.io/dask/dask:latest', memory_limit='4G', memory_request='4G')