aks_webservice_deployment_config: Create a deployment config for deploying an AKS web service

In azuremlsdk: Interface to the 'Azure Machine Learning' 'SDK'

Description

Deploy a web service to Azure Kubernetes Service for high-scale prodution deployments. Provides fast response time and autoscaling of the deployed service. Using GPU for inference when deployed as a web service is only supported on AKS.

Deploy to AKS if you need one or more of the following capabilities:

• Fast response time

• Autoscaling of the deployed service

• Hardware acceleration options

Usage

aks_webservice_deployment_config(
  autoscale_enabled = NULL,
  autoscale_min_replicas = NULL,
  autoscale_max_replicas = NULL,
  autoscale_refresh_seconds = NULL,
  autoscale_target_utilization = NULL,
  auth_enabled = NULL,
  cpu_cores = NULL,
  memory_gb = NULL,
  enable_app_insights = NULL,
  scoring_timeout_ms = NULL,
  replica_max_concurrent_requests = NULL,
  max_request_wait_time = NULL,
  num_replicas = NULL,
  primary_key = NULL,
  secondary_key = NULL,
  tags = NULL,
  properties = NULL,
  description = NULL,
  gpu_cores = NULL,
  period_seconds = NULL,
  initial_delay_seconds = NULL,
  timeout_seconds = NULL,
  success_threshold = NULL,
  failure_threshold = NULL,
  namespace = NULL,
  token_auth_enabled = NULL
)

Arguments

autoscale_enabled	If TRUE enable autoscaling for the web service. Defaults to TRUE if num_replicas = NULL.
autoscale_min_replicas	An int of the minimum number of containers to use when autoscaling the web service. Defaults to 1.
autoscale_max_replicas	An int of the maximum number of containers to use when autoscaling the web service. Defaults to 10.
autoscale_refresh_seconds	An int of how often in seconds the autoscaler should attempt to scale the web service. Defaults to 1.
autoscale_target_utilization	An int of the target utilization (in percent out of 100) the autoscaler should attempt to maintain for the web service. Defaults to 70.
auth_enabled	If TRUE enable key-based authentication for the web service. Defaults to TRUE.
cpu_cores	The number of cpu cores to allocate for the web service. Can be a decimal. Defaults to 0.1.
memory_gb	The amount of memory (in GB) to allocate for the web service. Can be a decimal. Defaults to 0.5.
enable_app_insights	If TRUE enable AppInsights for the web service. Defaults to FALSE.
scoring_timeout_ms	An int of the timeout (in milliseconds) to enforce for scoring calls to the web service. Defaults to 60000.
replica_max_concurrent_requests	An int of the number of maximum concurrent requests per node to allow for the web service. Defaults to 1.
max_request_wait_time	An int of the maximum amount of time a request will stay in the queue (in milliseconds) before returning a 503 error. Defaults to 500.
num_replicas	An int of the number of containers to allocate for the web service. If this parameter is not set then the autoscaler is enabled by default.
primary_key	A string of the primary auth key to use for the web service.
secondary_key	A string of the secondary auth key to use for the web service.
tags	A named list of key-value tags for the web service, e.g. list("key" = "value").
properties	A named list of key-value properties for the web service, e.g. list("key" = "value"). These properties cannot be changed after deployment, but new key-value pairs can be added.
description	A string of the description to give the web service
gpu_cores	An int of the number of gpu cores to allocate for the web service. Defaults to 1.
period_seconds	An int of how often in seconds to perform the liveness probe. Default to 10. Minimum value is 1.
initial_delay_seconds	An int of the number of seconds after the container has started before liveness probes are initiated. Defaults to 310.
timeout_seconds	An int of the number of seconds after which the liveness probe times out. Defaults to 2. Minimum value is 1.
success_threshold	An int of the minimum consecutive successes for the liveness probe to be considered successful after having failed. Defaults to 1. Minimum value is 1.
failure_threshold	An int of the number of times Kubernetes will try the liveness probe when a Pod starts and the probe fails, before giving up. Defaults to 3. Minimum value is 1.
namespace	A string of the Kubernetes namespace in which to deploy the web service: up to 63 lowercase alphanumeric ('a'-'z', '0'-'9') and hyphen ('-') characters. The first last characters cannot be hyphens.
token_auth_enabled	If TRUE, enable token-based authentication for the web service. If enabled, users can access the web service by fetching an access token using their Azure Active Directory credentials. Defaults to FALSE. Both token_auth_enabled and auth_enabled cannot be set to TRUE.

Details

AKS compute target

When deploying to AKS, you deploy to an AKS cluster that is connected to your workspace. There are two ways to connect an AKS cluster to your workspace:

• Create the AKS cluster using Azure ML (see create_aks_compute()).

• Attach an existing AKS cluster to your workspace (see attach_aks_compute()).

Pass the AksCompute object to the deployment_target parameter of deploy_model().

Token-based authentication

We strongly recommend that you create your Azure ML workspace in the same region as your AKS cluster. To authenticate with a token, the web service will make a call to the region in which your workspace is created. If your workspace's region is unavailable, then you will not be able to fetch a token for your web service, even if your cluster is in a different region than your workspace. This effectively results in token-based auth being unavailable until your workspace's region is available again. In addition, the greater the distance between your cluster's region and your workspace's region, the longer it will take to fetch a token.

Value

The AksServiceDeploymentConfiguration object.

See Also

deploy_model()

Examples

## Not run: 
deployment_config <- aks_webservice_deployment_config(cpu_cores = 1, memory_gb = 1)

## End(Not run)

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