Vinh Tran created SPARK-32411:
---------------------------------
Summary: GPU Cluster Fail
Key: SPARK-32411
URL: https://issues.apache.org/jira/browse/SPARK-32411
Project: Spark
Issue Type: Bug
Components: PySpark, Web UI
Affects Versions: 3.0.0
Environment: Ihave a Apache Spark 3.0 cluster consisting of machines
with multiple nvidia-gpus and I connect my jupyter notebook to the cluster
using pyspark,
Reporter: Vinh Tran
I'm having a difficult time getting a GPU cluster started on Apache Spark 3.0.
It was hard to find documentation on this, but I stumbled on a NVIDIA github
page for Rapids which suggested the following additional edits to the
spark-defaults.conf:
{code:java}
spark.task.resource.gpu.amount 0.25
spark.executor.resource.gpu.discoveryScript
./usr/local/spark/getGpusResources.sh{code}
I have a Apache Spark 3.0 cluster consisting of machines with multiple
nvidia-gpus and I connect my jupyter notebook to the cluster using pyspark,
however it results in the following error:
{code:java}
Py4JJavaError: An error occurred while calling
None.org.apache.spark.api.java.JavaSparkContext.
: org.apache.spark.SparkException: You must specify an amount for gpu
at
org.apache.spark.resource.ResourceUtils$.$anonfun$parseResourceRequest$1(ResourceUtils.scala:142)
at scala.collection.immutable.Map$Map1.getOrElse(Map.scala:119)
at
org.apache.spark.resource.ResourceUtils$.parseResourceRequest(ResourceUtils.scala:142)
at
org.apache.spark.resource.ResourceUtils$.$anonfun$parseAllResourceRequests$1(ResourceUtils.scala:159)
at
scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
at scala.collection.mutable.ArraySeq.foreach(ArraySeq.scala:75)
at scala.collection.TraversableLike.map(TraversableLike.scala:238)
at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at
org.apache.spark.resource.ResourceUtils$.parseAllResourceRequests(ResourceUtils.scala:159)
at
org.apache.spark.SparkContext$.checkResourcesPerTask$1(SparkContext.scala:2773)
at
org.apache.spark.SparkContext$.org$apache$spark$SparkContext$$createTaskScheduler(SparkContext.scala:2884)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:528)
at
org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at
sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at
sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:238)
at
py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
{code}
After this, I then tried adding another line to the conf per the instructions
which results in no errors, however when I log in to the Web UI at
localhost:8080, under Running Applications, the state remains at waiting.
{code:java}
spark.task.resource.gpu.amount 2
spark.executor.resource.gpu.discoveryScript
./usr/local/spark/getGpusResources.sh
spark.executor.resource.gpu.amount 1
{code}
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