⚠️ This Helm Chart has been deprecated. Please use the Dask Helm Chart instead.

Rapidsai

The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs.

This chart will deploy the following:

• 1 x Dask scheduler with port 8786 (scheduler) and 80 (Web UI) exposed on an external LoadBalancer (default)
• 3 x Dask CUDA workers with RAPIDS environment and a GPU that connect to the scheduler
• 1 x Jupyter notebook (optional) with port 80 exposed on an external LoadBalancer (default)
• All using Kubernetes Deployments

Installation

From source

$ git clone https://github.com/rapidsai/helm-chart helm-chart
$ cd helm-chart
$ helm dep update rapidsai
$ helm install myname --namespace mynamespace rapidsai

Development

Testing

$ make test
helm lint rapidsai
==> Linting rapidsai
Lint OK

1 chart(s) linted, no failures

Configuration

The following table lists the configurable parameters of the Rapidsai chart and their default values.

Parameter	Description	Default
dask.nameOverride		"rapidsai"
dask.fullnameOverride		"rapidsai"
dask.scheduler.name		"scheduler"
dask.scheduler.image.repository		"rapidsai/rapidsai"
dask.scheduler.image.tag		"cuda10.0-runtime-ubuntu16.04"
dask.scheduler.image.pullPolicy		"IfNotPresent"
dask.scheduler.image.pullSecrets		null
dask.scheduler.replicas		1
dask.scheduler.serviceType		"LoadBalancer"
dask.scheduler.servicePort		8786
dask.scheduler.resources		{}
dask.scheduler.tolerations		[{"key": "nvidia.com/gpu", "operator": "Equal", "value": "present", "effect": "NoSchedule"}]
dask.scheduler.nodeSelector		{}
dask.scheduler.affinity		{}
dask.webUI.name		"webui"
dask.webUI.servicePort		8787
dask.webUI.ingress.enabled		false
dask.webUI.ingress.tls		false
dask.webUI.ingress.hostname		"dask-ui.rapidsai.example.com"
dask.webUI.ingress.annotations		null
dask.worker.name		"worker"
dask.worker.image.repository		"rapidsai/rapidsai"
dask.worker.image.tag		"cuda10.0-runtime-ubuntu16.04"
dask.worker.image.pullPolicy		"IfNotPresent"
dask.worker.image.dask_worker		"dask-cuda-worker"
dask.worker.image.pullSecrets		null
dask.worker.replicas		3
dask.worker.env		null
dask.worker.resources.limits.cpu		1
dask.worker.resources.limits.memory		"3G"
dask.worker.resources.limits.nvidia.com/gpu		1
dask.worker.resources.requests.cpu		1
dask.worker.resources.requests.memory		"3G"
dask.worker.resources.requests.nvidia.com/gpu		1
dask.worker.tolerations		[{"key": "nvidia.com/gpu", "operator": "Equal", "value": "present", "effect": "NoSchedule"}]
dask.worker.nodeSelector		{}
dask.worker.affinity		{}
dask.jupyter.name		"jupyter"
dask.jupyter.enabled		true
dask.jupyter.image.repository		"rapidsai/rapidsai"
dask.jupyter.image.tag		"cuda10.0-runtime-ubuntu16.04"
dask.jupyter.image.pullPolicy		"IfNotPresent"
dask.jupyter.image.pullSecrets		null
dask.jupyter.replicas		1
dask.jupyter.serviceType		"LoadBalancer"
dask.jupyter.servicePort		80
dask.jupyter.password		"sha1:56152965e045:3cd9a2065e78b4a4e46c2d6f35ddd0160fe5b94d"
dask.jupyter.args		["bash", "/rapids/notebooks/utils/start-jupyter.sh"]
dask.jupyter.extraConfig		"c.ServerProxy.host_whitelist = [\"localhost\", \"127.0.0.1\", \"rapidsai-scheduler\"]"
dask.jupyter.env		[{"name": "DASK_DISTRIBUTED__DASHBOARD__LINK", "value": "/proxy/rapidsai-scheduler:8787/status"}]
dask.jupyter.resources.limits.cpu		2
dask.jupyter.resources.limits.memory		"6G"
dask.jupyter.resources.limits.nvidia.com/gpu		1
dask.jupyter.resources.requests.cpu		2
dask.jupyter.resources.requests.memory		"6G"
dask.jupyter.resources.requests.nvidia.com/gpu		1
dask.jupyter.tolerations		[{"key": "nvidia.com/gpu", "operator": "Equal", "value": "present", "effect": "NoSchedule"}]
dask.jupyter.nodeSelector		{}
dask.jupyter.affinity		{}
dask.jupyter.ingress.enabled		false
dask.jupyter.ingress.tls		false
dask.jupyter.ingress.hostname		"jupyter.rapidsai.example.com"
dask.jupyter.ingress.annotations		null