文件列表:

Dockerfile (1625, 2018-08-26)
config (0, 2018-08-26)
config\jupyter-config.py (458, 2018-08-26)
config\run.sh (323, 2018-08-26)
config\worker-spec-sample.yaml (398, 2018-08-26)
kube (0, 2018-08-26)
kube\20-service-account.yaml (625, 2018-08-26)
kube\30-deployment.yaml (1273, 2018-08-26)
kube\40-service.yaml (426, 2018-08-26)

# Dask ML on Kubernetes (GKE) `dask-kubernetes` creates a Dask cluster on Google Container Engine. It uses Google Cloud Storage bucket to store your notebook for persistence so there is no need to use a persistent volume. ## How to use 0. Create a GCS bucket for storing your notebooks 1. Change `c.GoogleStorageContentManager.default_path` in `jupyter-config.py` to your GCS path 3. Create a GKE cluster of your choice (Recommend 2CPU 7.5G or larger each node), make sure turn on **legacy authorisation mode** 4. `kubectl apply -f ./kube/` 5. Connect to service using port forwarding `kubectl port-forward svc/svc-notebooks 8888:8888`, or use the public ip from `kubectl get svc` 6. Start using cluster! ``` from dask_kubernetes import KubeCluster # See a sample worker spec in `config/worker-spec-sample.yaml` cluster = KubeCluster.from_yaml('...your yaml path') cluster.scale(3) # the desired number of nodes from dask.distributed import Client client = Client(cluster) ``` ### How to customise the image 1. Change the `Dockerfile`, build your image, and push it to any of the image storage service. 2. Change the image name in `30-deployment.yaml` file 3. Apply your kubernetes configuration

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