create_aks_compute: Create an AksCompute cluster
In azuremlsdk: Interface to the 'Azure Machine Learning' 'SDK'

Description Usage Arguments Value Details Examples

Provision an Azure Kubernetes Service instance (AksCompute) as a compute target for web service deployment. AksCompute is recommended for high-scale production deployments and provides fast response time and autoscaling of the deployed service. Cluster autoscaling isn't supported through the Azure ML R SDK. To change the nodes in the AksCompute cluster, use the UI for the cluster in the Azure portal. Once created, the cluster can be reused for multiple deployments.

create_aks_compute(
  workspace,
  cluster_name,
  agent_count = NULL,
  vm_size = NULL,
  ssl_cname = NULL,
  ssl_cert_pem_file = NULL,
  ssl_key_pem_file = NULL,
  location = NULL,
  vnet_resourcegroup_name = NULL,
  vnet_name = NULL,
  subnet_name = NULL,
  service_cidr = NULL,
  dns_service_ip = NULL,
  docker_bridge_cidr = NULL,
  cluster_purpose = c("FastProd", "DevTest")
)

`workspace`	The `Workspace` object.
`cluster_name`	A string of the name of the cluster.
`agent_count`	An integer of the number of agents (VMs) to host containers. Defaults to `3`.
`vm_size`	A string of the size of agent VMs. More details can be found here. Note that not all sizes are available in all regions, as detailed in the aformentioned link. Defaults to `'Standard_D3_v2'`.
`ssl_cname`	A string of a CName to use if enabling SSL validation on the cluster. Must provide all three - CName, cert file, and key file - to enable SSL validation.
`ssl_cert_pem_file`	A string of a file path to a file containing cert information for SSL validation. Must provide all three - CName, cert file, and key file - to enable SSL validation.
`ssl_key_pem_file`	A string of a file path to a file containing key information for SSL validation. Must provide all three - CName, cert file, and key file - to enable SSL validation.
`location`	A string of the location to provision the cluster in. If not specified, defaults to the workspace location. Available regions for this compute can be found here: "https://azure.microsoft.com/global-infrastructure/services/?regions=all&products=kubernetes-service".
`vnet_resourcegroup_name`	A string of the name of the resource group where the virtual network is located.
`vnet_name`	A string of the name of the virtual network.
`subnet_name`	A string of the name of the subnet inside the vnet.
`service_cidr`	A string of a CIDR notation IP range from which to assign service cluster IPs.
`dns_service_ip`	A string of the container's DNS server IP address.
`docker_bridge_cidr`	A string of a CIDR notation IP for Docker bridge.
`cluster_purpose`	A string describing targeted usage of the cluster. This is used to provision Azure Machine Learning components to ensure the desired level of fault-tolerance and QoS. 'FastProd' will provision components to handle higher levels of traffic with production quality fault-tolerance. This will default the AKS cluster to have 3 nodes. 'DevTest' will provision components at a minimal level for testing. This will default the AKS cluster to have 1 node. 'FastProd'is the default value.

An AksCompute object.

For more information on using an AksCompute resource within a virtual network, see Secure Azure ML experimentation and inference jobs within an Azure Virtual Network.

# Create an AksCompute cluster using the default configuration (you can also
# provide parameters to customize this)

ws <- load_workspace_from_config()

compute_target <- create_aks_compute(ws, cluster_name = 'mycluster')
wait_for_provisioning_completion(compute_target)

azuremlsdk documentation built on Oct. 23, 2020, 8:22 p.m.