# # 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. # from typing import overload, Optional, Dict from py4j.java_gateway import JavaObject, JVMView from pyspark.util import _parse_memory [docs]class ExecutorResourceRequest: """ 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 :py:class:`pyspark.ExecutorResourceRequests` class as a convenience API. .. versionadded:: 3.1.0 Parameters ---------- resourceName : str Name of the resource amount : str Amount requesting discoveryScript : str, optional Optional script used to discover the resources. This is required on some cluster managers that don't tell Spark the addresses of the resources allocated. The script runs on Executors startup to discover the addresses of the resources available. vendor : str, optional Vendor, required for some cluster managers Notes ----- This API is evolving. """ def __init__( self, resourceName: str, amount: int, discoveryScript: str = "", vendor: str = "", ): self._name = resourceName self._amount = amount self._discovery_script = discoveryScript self._vendor = vendor @property def resourceName(self) -> str: return self._name @property def amount(self) -> int: return self._amount @property def discoveryScript(self) -> str: return self._discovery_script @property def vendor(self) -> str: return self._vendor [docs]class ExecutorResourceRequests: """ A set of Executor resource requests. This is used in conjunction with the :class:`pyspark.resource.ResourceProfileBuilder` to programmatically specify the resources needed for an RDD that will be applied at the stage level. .. versionadded:: 3.1.0 Notes ----- This API is evolving. """ _CORES = "cores" _MEMORY = "memory" _OVERHEAD_MEM = "memoryOverhead" _PYSPARK_MEM = "pyspark.memory" _OFFHEAP_MEM = "offHeap" @overload def __init__(self, _jvm: JVMView): ... @overload def __init__( self, _jvm: None = ..., _requests: Optional[Dict[str, ExecutorResourceRequest]] = ..., ): ... def __init__( self, _jvm: Optional[JVMView] = None, _requests: Optional[Dict[str, ExecutorResourceRequest]] = None, ): from pyspark import SparkContext _jvm = _jvm or SparkContext._jvm if _jvm is not None: self._java_executor_resource_requests = ( _jvm.org.apache.spark.resource.ExecutorResourceRequests() ) if _requests is not None: for k, v in _requests.items(): if k == self._MEMORY: self._java_executor_resource_requests.memory(str(v.amount)) elif k == self._OVERHEAD_MEM: self._java_executor_resource_requests.memoryOverhead(str(v.amount)) elif k == self._PYSPARK_MEM: self._java_executor_resource_requests.pysparkMemory(str(v.amount)) elif k == self._CORES: self._java_executor_resource_requests.cores(v.amount) else: self._java_executor_resource_requests.resource( v.resourceName, v.amount, v.discoveryScript, v.vendor ) else: self._java_executor_resource_requests = None self._executor_resources: Dict[str, ExecutorResourceRequest] = {} def memory(self, amount: str) -> "ExecutorResourceRequests": if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.memory(amount) else: self._executor_resources[self._MEMORY] = ExecutorResourceRequest( self._MEMORY, _parse_memory(amount) ) return self def memoryOverhead(self, amount: str) -> "ExecutorResourceRequests": if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.memoryOverhead(amount) else: self._executor_resources[self._OVERHEAD_MEM] = ExecutorResourceRequest( self._OVERHEAD_MEM, _parse_memory(amount) ) return self def pysparkMemory(self, amount: str) -> "ExecutorResourceRequests": if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.pysparkMemory(amount) else: self._executor_resources[self._PYSPARK_MEM] = ExecutorResourceRequest( self._PYSPARK_MEM, _parse_memory(amount) ) return self def offheapMemory(self, amount: str) -> "ExecutorResourceRequests": if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.offHeapMemory(amount) else: self._executor_resources[self._OFFHEAP_MEM] = ExecutorResourceRequest( self._OFFHEAP_MEM, _parse_memory(amount) ) return self def cores(self, amount: int) -> "ExecutorResourceRequests": if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.cores(amount) else: self._executor_resources[self._CORES] = ExecutorResourceRequest(self._CORES, amount) return self def resource( self, resourceName: str, amount: int, discoveryScript: str = "", vendor: str = "", ) -> "ExecutorResourceRequests": if self._java_executor_resource_requests is not None: self._java_executor_resource_requests.resource( resourceName, amount, discoveryScript, vendor ) else: self._executor_resources[resourceName] = ExecutorResourceRequest( resourceName, amount, discoveryScript, vendor ) return self @property def requests(self) -> Dict[str, ExecutorResourceRequest]: if self._java_executor_resource_requests is not None: result = {} execRes = self._java_executor_resource_requests.requestsJMap() for k, v in execRes.items(): result[k] = ExecutorResourceRequest( v.resourceName(), v.amount(), v.discoveryScript(), v.vendor() ) return result else: return self._executor_resources [docs]class TaskResourceRequest: """ A task resource request. This is used in conjunction with the :class:`pyspark.resource.ResourceProfile` to programmatically specify the resources needed for an RDD that will be applied at the stage level. The amount is specified as a Double to allow for saying you want more than 1 task per resource. Valid values are less than or equal to 0.5 or whole numbers. Use :class:`pyspark.resource.TaskResourceRequests` class as a convenience API. Parameters ---------- resourceName : str Name of the resource amount : float Amount requesting as a float to support fractional resource requests. Valid values are less than or equal to 0.5 or whole numbers. .. versionadded:: 3.1.0 Notes ----- This API is evolving. """ def __init__(self, resourceName: str, amount: float): self._name = resourceName self._amount = float(amount) @property def resourceName(self) -> str: return self._name @property def amount(self) -> float: return self._amount [docs]class TaskResourceRequests: """ A set of task resource requests. This is used in conjunction with the :class:`pyspark.resource.ResourceProfileBuilder` to programmatically specify the resources needed for an RDD that will be applied at the stage level. .. versionadded:: 3.1.0 Notes ----- This API is evolving. """ _CPUS = "cpus" @overload def __init__(self, _jvm: JVMView): ... @overload def __init__( self, _jvm: None = ..., _requests: Optional[Dict[str, TaskResourceRequest]] = ..., ): ... def __init__( self, _jvm: Optional[JVMView] = None, _requests: Optional[Dict[str, TaskResourceRequest]] = None, ): from pyspark import SparkContext _jvm = _jvm or SparkContext._jvm if _jvm is not None: self._java_task_resource_requests: Optional[ JavaObject ] = _jvm.org.apache.spark.resource.TaskResourceRequests() if _requests is not None: for k, v in _requests.items(): if k == self._CPUS: self._java_task_resource_requests.cpus(int(v.amount)) else: self._java_task_resource_requests.resource(v.resourceName, v.amount) else: self._java_task_resource_requests = None self._task_resources: Dict[str, TaskResourceRequest] = {} def cpus(self, amount: int) -> "TaskResourceRequests": if self._java_task_resource_requests is not None: self._java_task_resource_requests.cpus(amount) else: self._task_resources[self._CPUS] = TaskResourceRequest(self._CPUS, amount) return self def resource(self, resourceName: str, amount: float) -> "TaskResourceRequests": if self._java_task_resource_requests is not None: self._java_task_resource_requests.resource(resourceName, float(amount)) else: self._task_resources[resourceName] = TaskResourceRequest(resourceName, amount) return self @property def requests(self) -> Dict[str, TaskResourceRequest]: if self._java_task_resource_requests is not None: result = {} taskRes = self._java_task_resource_requests.requestsJMap() for k, v in taskRes.items(): result[k] = TaskResourceRequest(v.resourceName(), v.amount()) return result else: return self._task_resources