tgravescs commented on a change in pull request #24634:
[SPARK-27361][YARN][test-hadoop3.2] YARN support for GPU-aware scheduling
URL: https://github.com/apache/spark/pull/24634#discussion_r288743416
##########
File path:
resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
##########
@@ -39,6 +40,25 @@ object YarnSparkHadoopUtil {
val MEMORY_OVERHEAD_MIN = 384L
val ANY_HOST = "*"
+ val YARN_GPU_RESOURCE_CONFIG = "yarn.io/gpu"
+ val YARN_FPGA_RESOURCE_CONFIG = "yarn.io/fpga"
+
+ /**
+ * Convert Spark resources into YARN resources.
+ * The only resources we know how to map from spark configs to yarn configs
are
+ * gpus and fpgas, everything else the user has to specify them in both the
+ * spark.yarn.*.resource and the spark.*.resource configs.
+ */
+ private[yarn] def getYarnResourcesFromSparkResources(
+ confPrefix: String,
+ sparkConf: SparkConf
+ ): Map[String, String] = {
+ Map("gpu" -> YARN_GPU_RESOURCE_CONFIG, "fpga" ->
YARN_FPGA_RESOURCE_CONFIG).map {
+ case (rName, yarnName) =>
+ val resourceCountSparkConf =
s"${confPrefix}${rName}${SPARK_RESOURCE_COUNT_SUFFIX}"
+ (yarnName ->
sparkConf.getOption(resourceCountSparkConf).getOrElse("0"))
Review comment:
updated
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