HyukjinKwon commented on a change in pull request #28085: [SPARK-29641][PYTHON][CORE] Stage Level Sched: Add python api's and tests URL: https://github.com/apache/spark/pull/28085#discussion_r403649296
########## File path: python/pyspark/executorresourcerequest.py ########## @@ -0,0 +1,73 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +class ExecutorResourceRequest(object): + """ + .. note:: Evolving + + An Executor resource request. This is used in conjunction with the ResourceProfile to + programmatically specify the resources needed for an RDD that will be applied at the + stage level. + + This is used to specify what the resource requirements are for an Executor and how + Spark can find out specific details about those resources. Not all the parameters are + required for every resource type. Resources like GPUs are supported and have same limitations + as using the global spark configs spark.executor.resource.gpu.*. The amount, discoveryScript, + and vendor parameters for resources are all the same parameters a user would specify through the + configs: spark.executor.resource.{resourceName}.{amount, discoveryScript, vendor}. + + For instance, a user wants to allocate an Executor with GPU resources on YARN. The user has + to specify the resource name (gpu), the amount or number of GPUs per Executor, + the discovery script would be specified so that when the Executor starts up it can + discovery what GPU addresses are available for it to use because YARN doesn't tell + Spark that, then vendor would not be used because its specific for Kubernetes. + + See the configuration and cluster specific docs for more details. + + Use ExecutorResourceRequests class as a convenience API. Review comment: I would link it properly by, for example, using `` :class:`pyspark.ExecutorResourceRequests` `` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
