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defaults.yaml
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cluster:
num_nodes: 3 # how many machines to ask Google for
machine_type: n1-standard-8 # type of each machine https://cloud.google.com/compute/docs/machine-types
disk_size: 50 # local volume size (GB) available on each machine
zone: us-east1-b # one of https://cloud.google.com/compute/docs/regions-zones/
autoscaling: False # whether to spin up/down nodes as number of containers is changed
min_nodes: 2 # if autoscaling, never have fewer than this many machines
max_nodes: 32 # if autoscaling, never have more than this many machines
preemptible: False # cheaper nodes that may be killed at any time
jupyter:
memory: 4096Mi # memory allocated to the jupyter container, including all kernels
cpus: 1 # cores used by the jupyter container, including kernels
port: 8888 # external HTTP access port for jupyter notebook
lab_port: 8889 # external HTTP access port for jupyterlab interface
image: mdurant/dask-kubernetes:latest # docker image of the jupyter environment
scheduler:
memory: 4096Mi # memory allocated for the dask scheduler container
cpus: 1 # cores used for the scheduler (which is a single-threaded process)
image: mdurant/dask-kubernetes:latest # docker image of the dask scheduler's environment
tcp_port: 8786 # external access port for the scheduler, through which clients connect
http_port: 9786 # external HTTP port for REST/JSON information from the scheduler
bokeh_port: 8787 # external HTTP port for the diagnostics dashboards
workers:
count: 8 # number of worker containers to launch, with one worker process per container
cpus_per_worker: 1.8 # cores allocated per worker container; the number of threads per worker will
# be rounded up to an integer
memory_per_worker: 6800Mi # memory of each worker container
mem_factor: 0.95 # memory limit option to pass to worker will be this multiplied by the container limit;
# slightly less than one, so worker should not exceed available memory in the container
image: mdurant/dask-kubernetes:latest # docker image of each worker's environment