Kubernetes and Helm
Contents
Kubernetes and Helm¶
It is easy to launch a Dask cluster and a Jupyter notebook server on cloud resources using Kubernetes and Helm.
This is particularly useful when you want to deploy a fresh Python environment on Cloud services like Amazon Web Services, Google Compute Engine, or Microsoft Azure.
If you already have Python environments running in a pre-existing Kubernetes cluster, then you may prefer the Kubernetes native documentation, which is a bit lighter weight.
Launch Kubernetes Cluster¶
This document assumes that you have a Kubernetes cluster and Helm installed.
If this is not the case, then you might consider setting up a Kubernetes cluster on one of the common cloud providers like Google, Amazon, or Microsoft. We recommend the first part of the documentation in the guide Zero to JupyterHub that focuses on Kubernetes and Helm (you do not need to follow all of these instructions). In particular, you don’t need to install JupyterHub.
Alternatively, you may want to experiment with Kubernetes locally using Minikube.
Which Chart is Right for You?¶
Dask maintains a Helm chart repository containing various charts for the Dask community https://helm.dask.org/ . You will need to add this to your known channels and update your local charts:
helm repo add dask https://helm.dask.org/
helm repo update
We provides two Helm charts. The right one to choose depends on whether you’re deploying Dask for a single user or for many users.
Helm Chart |
Use Case |
|---|---|
|
Single-user deployment with one notebook server and one Dask Cluster. |
|
Multi-user deployment with JupyterHub and Dask Gateway. |
See Helm Install Dask for a Single User or Helm Install Dask for Multiple Users for detailed
instructions on deploying either of these.
As you might suspect, deploying dask/daskhub is a bit more complicated since
there are more components. If you’re just deploying for a single user we’d recommend
using dask/dask.
Helm Install Dask for a Single User¶
Once your Kubernetes cluster is ready, you can deploy dask using the Dask Helm chart:
helm install my-dask dask/dask
This deploys a dask-scheduler, several dask-worker processes, and
also an optional Jupyter server.
Verify Deployment¶
This might take a minute to deploy. You can check its status with
kubectl:
kubectl get pods
kubectl get services
$ kubectl get pods
NAME READY STATUS RESTARTS AGE
bald-eel-jupyter-924045334-twtxd 0/1 ContainerCreating 0 1m
bald-eel-scheduler-3074430035-cn1dt 1/1 Running 0 1m
bald-eel-worker-3032746726-202jt 1/1 Running 0 1m
bald-eel-worker-3032746726-b8nqq 1/1 Running 0 1m
bald-eel-worker-3032746726-d0chx 0/1 ContainerCreating 0 1m
$ kubectl get services
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
bald-eel-jupyter LoadBalancer 10.11.247.201 35.226.183.149 80:30173/TCP 2m
bald-eel-scheduler LoadBalancer 10.11.245.241 35.202.201.129 8786:31166/TCP,80:31626/TCP 2m
kubernetes ClusterIP 10.11.240.1 <none> 443/TCP
48m
You can use the addresses under EXTERNAL-IP to connect to your now-running
Jupyter and Dask systems.
Notice the name bald-eel. This is the name that Helm has given to your
particular deployment of Dask. You could, for example, have multiple
Dask-and-Jupyter clusters running at once, and each would be given a different
name. Note that you will need to use this name to refer to your deployment in the future.
Additionally, you can list all active helm deployments with:
helm list
NAME REVISION UPDATED STATUS CHART NAMESPACE
bald-eel 1 Wed Dec 6 11:19:54 2017 DEPLOYED dask-0.1.0 default
Connect to Dask and Jupyter¶
When we ran kubectl get services, we saw some externally visible IPs:
mrocklin@pangeo-181919:~$ kubectl get services
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
bald-eel-jupyter LoadBalancer 10.11.247.201 35.226.183.149 80:30173/TCP 2m
bald-eel-scheduler LoadBalancer 10.11.245.241 35.202.201.129 8786:31166/TCP,80:31626/TCP 2m
kubernetes ClusterIP 10.11.240.1 <none> 443/TCP 48m
We can navigate to these services from any web browser. Here, one is the Dask diagnostic
dashboard, and the other is the Jupyter server. You can log into the Jupyter
notebook server with the password, dask.
You can create a notebook and create a Dask client from there. The
DASK_SCHEDULER_ADDRESS environment variable has been populated with the
address of the Dask scheduler. This is available in Python from the dask.config object.
>>> import dask
>>> dask.config.get('scheduler_address')
'bald-eel-scheduler:8786'
Although you don’t need to use this address, the Dask client will find this variable automatically.
from dask.distributed import Client, config
client = Client()
Configure Environment¶
By default, the Helm deployment launches three workers using one core each and a standard conda environment. We can customize this environment by creating a small yaml file that implements a subset of the values in the dask helm chart values.yaml file.
For example, we can increase the number of workers, and include extra conda and pip packages to install on the both the workers and Jupyter server (these two environments should be matched).
# config.yaml
worker:
replicas: 8
resources:
limits:
cpu: 2
memory: 7.5G
requests:
cpu: 2
memory: 7.5G
env:
- name: EXTRA_CONDA_PACKAGES
value: numba xarray -c conda-forge
- name: EXTRA_PIP_PACKAGES
value: s3fs dask-ml --upgrade
# We want to keep the same packages on the worker and jupyter environments
jupyter:
enabled: true
env:
- name: EXTRA_CONDA_PACKAGES
value: numba xarray matplotlib -c conda-forge
- name: EXTRA_PIP_PACKAGES
value: s3fs dask-ml --upgrade
This config file overrides the configuration for the number and size of workers and the conda and pip packages installed on the worker and Jupyter containers. In general, we will want to make sure that these two software environments match.
Update your deployment to use this configuration file. Note that you will not use helm install for this stage: that would create a new deployment on the same Kubernetes cluster. Instead, you will upgrade your existing deployment by using the current name:
helm upgrade bald-eel dask/dask -f config.yaml
This will update those containers that need to be updated. It may take a minute or so.
As a reminder, you can list the names of deployments you have using helm
list
Check status and logs¶
For standard issues, you should be able to see the worker status and logs using the
Dask dashboard (in particular, you can see the worker links from the info/ page).
However, if your workers aren’t starting, you can check the status of pods and
their logs with the following commands:
kubectl get pods
kubectl logs <PODNAME>
mrocklin@pangeo-181919:~$ kubectl get pods
NAME READY STATUS RESTARTS AGE
bald-eel-jupyter-3805078281-n1qk2 1/1 Running 0 18m
bald-eel-scheduler-3074430035-cn1dt 1/1 Running 0 58m
bald-eel-worker-1931881914-1q09p 1/1 Running 0 18m
bald-eel-worker-1931881914-856mm 1/1 Running 0 18m
bald-eel-worker-1931881914-9lgzb 1/1 Running 0 18m
bald-eel-worker-1931881914-bdn2c 1/1 Running 0 16m
bald-eel-worker-1931881914-jq70m 1/1 Running 0 17m
bald-eel-worker-1931881914-qsgj7 1/1 Running 0 18m
bald-eel-worker-1931881914-s2phd 1/1 Running 0 17m
bald-eel-worker-1931881914-srmmg 1/1 Running 0 17m
mrocklin@pangeo-181919:~$ kubectl logs bald-eel-worker-1931881914-856mm
EXTRA_CONDA_PACKAGES environment variable found. Installing.
Fetching package metadata ...........
Solving package specifications: .
Package plan for installation in environment /opt/conda/envs/dask:
The following NEW packages will be INSTALLED:
fasteners: 0.14.1-py36_2 conda-forge
monotonic: 1.3-py36_0 conda-forge
zarr: 2.1.4-py36_0 conda-forge
Proceed ([y]/n)?
monotonic-1.3- 100% |###############################| Time: 0:00:00 11.16 MB/s
fasteners-0.14 100% |###############################| Time: 0:00:00 576.56 kB/s
...
Delete a Helm deployment¶
You can always delete a helm deployment using its name:
helm delete bald-eel --purge
Note that this does not destroy any clusters that you may have allocated on a Cloud service (you will need to delete those explicitly).
Avoid the Jupyter Server¶
Sometimes you do not need to run a Jupyter server alongside your Dask cluster.
jupyter:
enabled: false
Helm Install Dask for Multiple Users¶
The dask/daskhub Helm Chart deploys JupyterHub, Dask Gateway, and configures
the two to work well together. In particular, Dask Gateway is registered as
a JupyterHub service so that Dask Gateway can re-use JupyterHub’s authentication,
and the JupyterHub environment is configured to connect to the Dask Gateway
without any arguments.
Note
The dask/daskhub helm chart came out of the Pangeo project, a community
platform for big data geoscience.
The dask/daskhub helm chart uses the JupyterHub and Dask-Gateway helm charts.
You’ll want to consult the JupyterHub helm documentation and
and Dask Gateway helm documentation for further customization. The default values
are at https://github.com/dask/helm-chart/blob/main/daskhub/values.yaml.
Verify that you’ve set up a Kubernetes cluster and added Dask’s helm charts:
$ helm repo add dask https://helm.dask.org/
$ helm repo update
JupyterHub and Dask Gateway require a few secret tokens. We’ll generate them
on the command line and insert the tokens in a secrets.yaml file that will
be passed to Helm.
Run the following command, and copy the output. This is our token-1.
$ openssl rand -hex 32 # generate token-1
Run command again and copy the output again. This is our token-2.
$ openssl rand -hex 32 # generate token-2
Now substitute those two values for <token-1> and <token-2> below.
Note that <token-2> is used twice, once for jupyterhub.hub.services.dask-gateway.apiToken, and a second time for dask-gateway.gateway.auth.jupyterhub.apiToken.
# file: secrets.yaml
jupyterhub:
proxy:
secretToken: "<token-1>"
hub:
services:
dask-gateway:
apiToken: "<token-2>"
dask-gateway:
gateway:
auth:
jupyterhub:
apiToken: "<token-2>"
Now we’re ready to install DaskHub
$ helm upgrade --wait --install --render-subchart-notes \
dhub dask/daskhub \
--values=secrets.yaml
The output explains how to find the IPs for your JupyterHub depoyment.
$ kubectl get service proxy-public
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
proxy-public LoadBalancer 10.43.249.239 35.202.158.223 443:31587/TCP,80:30500/TCP 2m40s
Creating a Dask Cluster¶
To create a Dask cluster on this deployment, users need to connect to the Dask Gateway
>>> from dask_gateway import GatewayCluster
>>> cluster = GatewayCluster()
>>> client = cluster.get_client()
>>> cluster
Depending on the configuration, users may need to cluster.scale(n) to
get workers. See https://gateway.dask.org/ for more on Dask Gateway.
Matching the User Environment¶
Dask Clients will be running the JupyterHub’s singleuser environment. To ensure
that the same environment is used for the scheduler and workers, you can provide
it as a Gateway option and configure the singleuser environment to default
to the value set by JupyterHub.
# config.yaml
jupyterhub:
singleuser:
extraEnv:
DASK_GATEWAY__CLUSTER__OPTIONS__IMAGE: '{JUPYTER_IMAGE_SPEC}'
dask-gateway:
gateway:
extraConfig:
optionHandler: |
from dask_gateway_server.options import Options, Integer, Float, String
def option_handler(options):
if ":" not in options.image:
raise ValueError("When specifying an image you must also provide a tag")
return {
"image": options.image,
}
c.Backend.cluster_options = Options(
String("image", default="pangeo/base-notebook:2020.07.28", label="Image"),
handler=option_handler,
)
The user environment will need to include dask-gateway. Any packages installed
manually after the singleuser pod started will not be included in the worker
environment.