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). Also, JupyterHub is not necessary to deploy Dask:
Alternatively, you may want to experiment with Kubernetes locally using Minikube.
Helm Install Dask¶
Dask maintains a Helm chart in the default stable channel at https://kubernetes-charts.storage.googleapis.com . This should be added to your helm installation by default. You can update the known channels to make sure you have up-to-date charts as follows:
helm repo update
Now, you can launch Dask on your Kubernetes cluster using the Dask Helm chart:
helm install stable/dask
This deploys a
dask-worker processes, and
also an optional Jupyter server.
This might take a minute to deploy. You can check its status with
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 22.214.171.124 80:30173/TCP 2m bald-eel-scheduler LoadBalancer 10.11.245.241 126.96.36.199 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:
[email protected]:~$ kubectl get services NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE bald-eel-jupyter LoadBalancer 10.11.247.201 188.8.131.52 80:30173/TCP 2m bald-eel-scheduler LoadBalancer 10.11.245.241 184.108.40.206 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,
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 in the
>>> from dask.distributed import Client, config >>> config['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()
By default, the Helm deployment launches three workers using two cores 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 stable/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
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
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>
[email protected]:~$ 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 [email protected]:~$ 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