Google Cloud Ai Platform V1 Client - Class RaySpec (1.35.0)

Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class RaySpec.

Configuration information for the Ray cluster.

For experimental launch, Ray cluster creation and Persistent cluster creation are 1:1 mapping: We will provision all the nodes within the Persistent cluster as Ray nodes.

Generated from protobuf message google.cloud.aiplatform.v1.RaySpec

Namespace

Google \ Cloud \ AIPlatform \ V1

Methods

__construct

Constructor.

Parameters
Name	Description
`data`	`array` Optional. Data for populating the Message object.
`↳ image_uri`	`string` Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
`↳ resource_pool_images`	`array\|Google\Protobuf\Internal\MapField` Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }
`↳ head_node_resource_pool_id`	`string` Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
`↳ ray_metric_spec`	`RayMetricSpec` Optional. Ray metrics configurations.
`↳ ray_logs_spec`	`RayLogsSpec` Optional. OSS Ray logging configurations.

getImageUri

Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images.

Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.

Returns
Type	Description
`string`

setImageUri

Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images.

Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getResourcePoolImages

Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids.

Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

Returns
Type	Description
`Google\Protobuf\Internal\MapField`

setResourcePoolImages

Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

Parameter
Name	Description
`var`	`array\|Google\Protobuf\Internal\MapField`

Returns
Type	Description
`$this`

getHeadNodeResourcePoolId

Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.

Returns
Type	Description
`string`

setHeadNodeResourcePoolId

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getRayMetricSpec

Optional. Ray metrics configurations.

Returns
Type	Description
`RayMetricSpec\|null`

hasRayMetricSpec

clearRayMetricSpec

setRayMetricSpec