tgravescs commented on a change in pull request #25047:
[WIP][SPARK-27371][CORE] Support GPU-aware resources scheduling in Standalone
URL: https://github.com/apache/spark/pull/25047#discussion_r310165369
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File path: core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala
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@@ -19,17 +19,34 @@ package org.apache.spark.deploy.master
import scala.collection.mutable
+import org.apache.spark.resource.{ResourceAllocator, ResourceInformation,
ResourceRequirement}
import org.apache.spark.rpc.RpcEndpointRef
import org.apache.spark.util.Utils
+private[spark] case class WorkerResourceInfo(name: String, addresses:
Seq[String])
+ extends ResourceAllocator(name, addresses) {
+
+ def toResourceInformation(): ResourceInformation = {
+ new ResourceInformation(name, addresses.toArray)
+ }
+
+ def acquire(amount: Int): ResourceInformation = {
+ val allocated = availableAddrs.take(amount)
+ acquire(allocated)
+ new ResourceInformation(name, allocated.toArray)
+ }
+}
+
private[spark] class WorkerInfo(
val id: String,
val host: String,
val port: Int,
val cores: Int,
val memory: Int,
val endpoint: RpcEndpointRef,
- val webUiAddress: String)
+ val webUiAddress: String,
+ val resources: Map[String, WorkerResourceInfo],
+ val pid: Int = 0)
Review comment:
I don't think pid is used here
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