Running on GKE (Google Kubernetes Engine)
Prerequisites
In order to deploy Jupyter with a PySeQuiLa kernel configured on Kubernetes please make sure have Helm 3.x installed
Connect to your Kubernetes cluster
Run gcloud command to do it, e.g.
[13]:
%%bash
gcloud container clusters get-credentials pysequila --zone us-central1-c --project sincere-woods-273112
Fetching cluster endpoint and auth data.
kubeconfig entry generated for pysequila.
Add PySeQuiLa Helm charts repo
[2]:
%%bash
helm repo add biodatageeks https://biodatageeks.github.io/charts/
"biodatageeks" has been added to your repositories
Fetch the newest helm chars
[5]:
%%bash
helm repo update
Hang tight while we grab the latest from your chart repositories...
...Successfully got an update from the "biodatageeks" chart repository
...Successfully got an update from the "biodatageeks-snapshots" chart repository
...Successfully got an update from the "cetic" chart repository
...Successfully got an update from the "jupyterhub" chart repository
...Successfully got an update from the "bitnami" chart repository
Update Complete. ⎈Happy Helming!⎈
Install PySeqQuiLa
[14]:
%%bash
helm install pysequila biodatageeks/pysequila --version=0.1.3 --atomic --timeout 3600s
NAME: pysequila
LAST DEPLOYED: Sat Oct 24 19:53:33 2020
NAMESPACE: default
STATUS: deployed
REVISION: 1
TEST SUITE: None
[16]:
%%bash
kubectl get pods
NAME READY STATUS RESTARTS AGE
continuous-image-puller-2d7lw 1/1 Running 0 6m28s
continuous-image-puller-86dz4 1/1 Running 0 6m28s
continuous-image-puller-rg9xv 1/1 Running 0 6m28s
hub-5599c9bfbc-gztk5 1/1 Running 0 6m28s
proxy-6fc65c5b79-rs6c2 1/1 Running 0 6m28s
user-scheduler-7fb9cdfb69-hgq4c 1/1 Running 0 6m28s
user-scheduler-7fb9cdfb69-xnxz8 1/1 Running 0 6m28s
Get public ip address of Jupyterhub
Once your Jupyterlab session is spawned you should be able to start your analysis using a PySeQuiLa kernel
For detailed information on how to run and customize Jupyter on Kubernetes please refer to Zero to JupyterHub with Kubernetes
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