#@@ -1,43 +1,43 @@ # GPUtil - GPU utilization # # A Python module for programmically getting the GPU utilization from NVIDA GPUs using nvidia-smi # # Author: Anders Krogh Mortensen (anderskm) # Date: 16 January 2017 # Web: https://github.com/anderskm/gputil # # LICENSE # # MIT License # # Copyright (c) 2017 anderskm # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from subprocess import Popen, PIPE from distutils import spawn import os import math import random import time import sys import platform import subprocess import numpy as np __version__ = '1.4.0' class GPU: def __init__(self, ID, uuid, load, memoryTotal, memoryUsed, memoryFree, driver, gpu_name, serial, display_mode, display_active, temp_gpu): self.id = ID self.uuid = uuid self.load = load self.memoryUtil = float(memoryUsed)/float(memoryTotal) self.memoryTotal = memoryTotal self.memoryUsed = memoryUsed self.memoryFree = memoryFree self.driver = driver self.name = gpu_name self.serial = serial self.display_mode = display_mode self.display_active = display_active self.temperature = temp_gpu def __str__(self): return str(self.__dict__) class GPUProcess: def __init__(self, pid, processName, gpuId, gpuUuid, gpuName, usedMemory, uid, uname): self.pid = pid self.processName = processName self.gpuId = gpuId self.gpuUuid = gpuUuid self.gpuName = gpuName self.usedMemory = usedMemory self.uid = uid self.uname = uname def __str__(self): return str(self.__dict__) def safeFloatCast(strNumber): try: number = float(strNumber) except ValueError: number = float('nan') return number #def getGPUs(): def getNvidiaSmiCmd(): if platform.system() == "Windows": # If the platform is Windows and nvidia-smi # could not be found from the environment path, #@@ -75,57 +94,97 @@ def getGPUs(): nvidia_smi = "%s\\Program Files\\NVIDIA Corporation\\NVSMI\\nvidia-smi.exe" % os.environ['systemdrive'] else: nvidia_smi = "nvidia-smi" return nvidia_smi def getGPUs(): # Get ID, processing and memory utilization for all GPUs nvidia_smi = getNvidiaSmiCmd() try: p = Popen([nvidia_smi,"--query-gpu=index,uuid,utilization.gpu,memory.total,memory.used,memory.free,driver_version,name,gpu_serial,display_active,display_mode,temperature.gpu", "--format=csv,noheader,nounits"], stdout=PIPE) stdout, stderror = p.communicate() p = subprocess.run([ nvidia_smi, "--query-gpu=index,uuid,utilization.gpu,memory.total,memory.used,memory.free,driver_version,name,gpu_serial,display_active,display_mode,temperature.gpu", "--format=csv,noheader,nounits" ], stdout=subprocess.PIPE, encoding='utf8') stdout, stderror = p.stdout, p.stderr except: return [] output = stdout;#output = stdout.decode('UTF-8') # output = output[2:-1] # Remove b' and ' from string added by python #print(output) output = stdout ## Parse output # Split on line break lines = output.split(os.linesep) #print(lines) numDevices = len(lines)-1 GPUs = [] for g in range(numDevices): line = lines[g] #print(line) vals = line.split(', ') #print(vals) for i in range(12): # print(vals[i]) if (i == 0): deviceIds = int(vals[i]) elif (i == 1): uuid = vals[i] elif (i == 2): gpuUtil = safeFloatCast(vals[i])/100 elif (i == 3): memTotal = safeFloatCast(vals[i]) elif (i == 4): memUsed = safeFloatCast(vals[i]) elif (i == 5): memFree = safeFloatCast(vals[i]) elif (i == 6): driver = vals[i] elif (i == 7): gpu_name = vals[i] elif (i == 8): serial = vals[i] elif (i == 9): display_active = vals[i] elif (i == 10): display_mode = vals[i] elif (i == 11): temp_gpu = safeFloatCast(vals[i]); deviceIds = int(vals[0]) uuid = vals[1] gpuUtil = safeFloatCast(vals[2]) / 100 memTotal = safeFloatCast(vals[3]) memUsed = safeFloatCast(vals[4]) memFree = safeFloatCast(vals[5]) driver = vals[6] gpu_name = vals[7] serial = vals[8] display_active = vals[9] display_mode = vals[10] temp_gpu = safeFloatCast(vals[11]); GPUs.append(GPU(deviceIds, uuid, gpuUtil, memTotal, memUsed, memFree, driver, gpu_name, serial, display_mode, display_active, temp_gpu)) return GPUs # (deviceIds, gpuUtil, memUtil) def getGPUProcesses(): """Get all gpu compute processes.""" global gpuUuidToIdMap gpuUuidToIdMap = {} try: gpus = getGPUs() for gpu in gpus: gpuUuidToIdMap[gpu.uuid] = gpu.id del gpus except: pass nvidia_smi = getNvidiaSmiCmd() try: p = subprocess.run([ nvidia_smi, "--query-compute-apps=pid,process_name,gpu_uuid,gpu_name,used_memory", "--format=csv,noheader,nounits" ], stdout=subprocess.PIPE, encoding='utf8') stdout, stderror = p.stdout, p.stderr except: return [] output = stdout ## Parse output # Split on line break lines = output.split(os.linesep) numProcesses = len(lines) - 1 processes = [] for g in range(numProcesses): line = lines[g] #print(line) vals = line.split(', ') #print(vals) pid = int(vals[0]) processName = vals[1] gpuUuid = vals[2] gpuName = vals[3] usedMemory = safeFloatCast(vals[4]) gpuId = gpuUuidToIdMap[gpuUuid] if gpuId is None: gpuId = -1 # get uid and uname owner of the pid try: p = subprocess.run(['ps', f'-p{pid}', '-oruid=,ruser='], stdout=subprocess.PIPE, encoding='utf8') uid, uname = p.stdout.split() uid = int(uid) except: uid, uname = -1, '' processes.append(GPUProcess(pid, processName, gpuId, gpuUuid, gpuName, usedMemory, uid, uname)) return processes def getAvailable(order = 'first', limit=1, maxLoad=0.5, maxMemory=0.5, memoryFree=0, includeNan=False, excludeID=[], excludeUUID=[]): # order = first | last | random | load | memory # first --> select the GPU with the lowest ID (DEFAULT) # last --> select the GPU with the highest ID # random --> select a random available GPU # load --> select the GPU with the lowest load # memory --> select the GPU with the most memory available # limit = 1 (DEFAULT), 2, ..., Inf # Limit sets the upper limit for the number of GPUs to return. E.g. if limit = 2, but only one is available, only one is returned. # Get device IDs, load and memory usage GPUs = getGPUs() # Determine, which GPUs are available GPUavailability = getAvailability(GPUs, maxLoad=maxLoad, maxMemory=maxMemory, memoryFree=memoryFree, includeNan=includeNan, excludeID=excludeID, excludeUUID=excludeUUID) availAbleGPUindex = [idx for idx in range(0,len(GPUavailability)) if (GPUavailability[idx] == 1)] # Discard unavailable GPUs GPUs = [GPUs[g] for g in availAbleGPUindex] # Sort available GPUs according to the order argument if (order == 'first'): GPUs.sort(key=lambda x: float('inf') if math.isnan(x.id) else x.id, reverse=False) elif (order == 'last'): GPUs.sort(key=lambda x: float('-inf') if math.isnan(x.id) else x.id, reverse=True) elif (order == 'random'): GPUs = [GPUs[g] for g in random.sample(range(0,len(GPUs)),len(GPUs))] elif (order == 'load'): GPUs.sort(key=lambda x: float('inf') if math.isnan(x.load) else x.load, reverse=False) elif (order == 'memory'): GPUs.sort(key=lambda x: float('inf') if math.isnan(x.memoryUtil) else x.memoryUtil, reverse=False) # Extract the number of desired GPUs, but limited to the total number of available GPUs GPUs = GPUs[0:min(limit, len(GPUs))] # Extract the device IDs from the GPUs and return them deviceIds = [gpu.id for gpu in GPUs] return deviceIds #def getAvailability(GPUs, maxLoad = 0.5, maxMemory = 0.5, includeNan = False): # # Determine, which GPUs are available # GPUavailability = np.zeros(len(GPUs)) # for i in range(len(GPUs)): # if (GPUs[i].load < maxLoad or (includeNan and np.isnan(GPUs[i].load))) and (GPUs[i].memoryUtil < maxMemory or (includeNan and np.isnan(GPUs[i].memoryUtil))): # GPUavailability[i] = 1 def getAvailability(GPUs, maxLoad=0.5, maxMemory=0.5, memoryFree=0, includeNan=False, excludeID=[], excludeUUID=[]): # Determine, which GPUs are available GPUavailability = [1 if (gpu.memoryFree>=memoryFree) and (gpu.load < maxLoad or (includeNan and math.isnan(gpu.load))) and (gpu.memoryUtil < maxMemory or (includeNan and math.isnan(gpu.memoryUtil))) and ((gpu.id not in excludeID) and (gpu.uuid not in excludeUUID)) else 0 for gpu in GPUs] return GPUavailability def getFirstAvailable(order = 'first', maxLoad=0.5, maxMemory=0.5, attempts=1, interval=900, verbose=False, includeNan=False, excludeID=[], excludeUUID=[]): #GPUs = getGPUs() #firstAvailableGPU = np.NaN #for i in range(len(GPUs)): # if (GPUs[i].load < maxLoad) & (GPUs[i].memory < maxMemory): # firstAvailableGPU = GPUs[i].id # break #return firstAvailableGPU for i in range(attempts): if (verbose): print('Attempting (' + str(i+1) + '/' + str(attempts) + ') to locate available GPU.') # Get first available GPU available = getAvailable(order=order, limit=1, maxLoad=maxLoad, maxMemory=maxMemory, includeNan=includeNan, excludeID=excludeID, excludeUUID=excludeUUID) # If an available GPU was found, break for loop. if (available): if (verbose): print('GPU ' + str(available) + ' located!') break # If this is not the last attempt, sleep for 'interval' seconds if (i != attempts-1): time.sleep(interval) # Check if an GPU was found, or if the attempts simply ran out. Throw error, if no GPU was found if (not(available)): raise RuntimeError('Could not find an available GPU after ' + str(attempts) + ' attempts with ' + str(interval) + ' seconds interval.') # Return found GPU return available def showUtilization(all=False, attrList=None, useOldCode=False): GPUs = getGPUs() if (all): if (useOldCode): print(' ID | Name | Serial | UUID || GPU util. | Memory util. || Memory total | Memory used | Memory free || Display mode | Display active |') print('------------------------------------------------------------------------------------------------------------------------------') for gpu in GPUs: print(' {0:2d} | {1:s} | {2:s} | {3:s} || {4:3.0f}% | {5:3.0f}% || {6:.0f}MB | {7:.0f}MB | {8:.0f}MB || {9:s} | {10:s}'.format(gpu.id,gpu.name,gpu.serial,gpu.uuid,gpu.load*100,gpu.memoryUtil*100,gpu.memoryTotal,gpu.memoryUsed,gpu.memoryFree,gpu.display_mode,gpu.display_active)) else: attrList = [[{'attr':'id','name':'ID'}, {'attr':'name','name':'Name'}, {'attr':'serial','name':'Serial'}, {'attr':'uuid','name':'UUID'}], [{'attr':'temperature','name':'GPU temp.','suffix':'C','transform': lambda x: x,'precision':0}, {'attr':'load','name':'GPU util.','suffix':'%','transform': lambda x: x*100,'precision':0}, {'attr':'memoryUtil','name':'Memory util.','suffix':'%','transform': lambda x: x*100,'precision':0}], [{'attr':'memoryTotal','name':'Memory total','suffix':'MB','precision':0}, {'attr':'memoryUsed','name':'Memory used','suffix':'MB','precision':0}, {'attr':'memoryFree','name':'Memory free','suffix':'MB','precision':0}], [{'attr':'display_mode','name':'Display mode'}, {'attr':'display_active','name':'Display active'}]] else: if (useOldCode): print(' ID GPU MEM') print('--------------') for gpu in GPUs: print(' {0:2d} {1:3.0f}% {2:3.0f}%'.format(gpu.id, gpu.load*100, gpu.memoryUtil*100)) else: attrList = [[{'attr':'id','name':'ID'}, {'attr':'load','name':'GPU','suffix':'%','transform': lambda x: x*100,'precision':0}, {'attr':'memoryUtil','name':'MEM','suffix':'%','transform': lambda x: x*100,'precision':0}], ] if (not useOldCode): if (attrList is not None): headerString = '' GPUstrings = ['']*len(GPUs) for attrGroup in attrList: #print(attrGroup) for attrDict in attrGroup: headerString = headerString + '| ' + attrDict['name'] + ' ' headerWidth = len(attrDict['name']) minWidth = len(attrDict['name']) attrPrecision = '.' + str(attrDict['precision']) if ('precision' in attrDict.keys()) else '' attrSuffix = str(attrDict['suffix']) if ('suffix' in attrDict.keys()) else '' attrTransform = attrDict['transform'] if ('transform' in attrDict.keys()) else lambda x : x for gpu in GPUs: attr = getattr(gpu,attrDict['attr']) attr = attrTransform(attr) if (isinstance(attr,float)): attrStr = ('{0:' + attrPrecision + 'f}').format(attr) elif (isinstance(attr,int)): attrStr = ('{0:d}').format(attr) elif (isinstance(attr,str)): attrStr = attr; elif (sys.version_info[0] == 2): if (isinstance(attr,unicode)): attrStr = attr.encode('ascii','ignore') else: raise TypeError('Unhandled object type (' + str(type(attr)) + ') for attribute \'' + attrDict['name'] + '\'') attrStr += attrSuffix minWidth = max(minWidth,len(attrStr)) headerString += ' '*max(0,minWidth-headerWidth) minWidthStr = str(minWidth - len(attrSuffix)) for gpuIdx,gpu in enumerate(GPUs): attr = getattr(gpu,attrDict['attr']) attr = attrTransform(attr) if (isinstance(attr,float)): attrStr = ('{0:'+ minWidthStr + attrPrecision + 'f}').format(attr) elif (isinstance(attr,int)): attrStr = ('{0:' + minWidthStr + 'd}').format(attr) elif (isinstance(attr,str)): attrStr = ('{0:' + minWidthStr + 's}').format(attr); elif (sys.version_info[0] == 2): if (isinstance(attr,unicode)): attrStr = ('{0:' + minWidthStr + 's}').format(attr.encode('ascii','ignore')) else: raise TypeError('Unhandled object type (' + str(type(attr)) + ') for attribute \'' + attrDict['name'] + '\'') attrStr += attrSuffix GPUstrings[gpuIdx] += '| ' + attrStr + ' ' headerString = headerString + '|' for gpuIdx,gpu in enumerate(GPUs): GPUstrings[gpuIdx] += '|' headerSpacingString = '-' * len(headerString) print(headerString) print(headerSpacingString) for GPUstring in GPUstrings: print(GPUstring) # Generate gpu uuid to id map gpuUuidToIdMap = {} try: gpus = getGPUs() for gpu in gpus: gpuUuidToIdMap[gpu.uuid] = gpu.id del gpus except: pass def getGPUInfos(): ###返回gpus：list,一个GPU为一个元素-对象 ###########：有属性，'id','load','memoryFree', ###########：'memoryTotal','memoryUsed','memoryUtil','name','serial''temperature','uuid',process ###其中process：每一个计算进程是一个元素--对象 ############：有属性，'gpuId','gpuName','gpuUuid', ############：'gpuid','pid','processName','uid', 'uname','usedMemory' gpus = getGPUs() gpuUuidToIdMap={} for gpu in gpus: gpuUuidToIdMap[gpu.uuid] = gpu.id gpu.process=[] indexx = [x.id for x in gpus ] process = getGPUProcesses() for pre in process: pre.gpuid = gpuUuidToIdMap[pre.gpuUuid] gpuId = indexx.index(pre.gpuid ) gpus[gpuId].process.append(pre ) return gpus def get_available_gpu(gpuStatus): ##判断是否有空闲的显卡，如果有返回id，没有返回None cuda=None for gpus in gpuStatus: if len(gpus.process) == 0: cuda = gpus.id return str(cuda) return cuda def get_whether_gpuProcess(): ##判断是否有空闲的显卡，如果有返回id，没有返回None gpuStatus=getGPUInfos() gpuProcess=True for gpus in gpuStatus: if len(gpus.process) != 0: gpuProcess = False return gpuProcess def get_offlineProcess_gpu(gpuStatus,pidInfos): gpu_onLine = [] for gpu in gpuStatus: for gpuProcess in gpu.process: pid = gpuProcess.pid if pid in pidInfos.keys(): pidType = pidInfos[pid]['type'] if pidType == 'onLine': gpu_onLine.append(gpu) gpu_offLine = set(gpuStatus) - set(gpu_onLine) return list(gpu_offLine) def arrange_offlineProcess(gpuStatus,pidInfos,modelMemory=1500): cudaArrange=[] gpu_offLine = get_offlineProcess_gpu(gpuStatus,pidInfos) for gpu in gpu_offLine: leftMemory = gpu.memoryTotal*0.9 - gpu.memoryUsed modelCnt = int(leftMemory// modelMemory) cudaArrange.extend( [gpu.id] * modelCnt ) return cudaArrange def get_potential_gpu(gpuStatus,pidInfos): ###所有GPU上都有计算。需要为“在线任务”空出一块显卡。 ###step1：查看所有显卡上是否有“在线任务” gpu_offLine = get_offlineProcess_gpu(gpuStatus,pidInfos) if len(gpu_offLine) == 0 : return False ###step2,找出每张显卡上离线进程的数目 offLineCnt = [ len(gpu.process) for gpu in gpu_offLine ] minCntIndex =offLineCnt.index( min(offLineCnt)) pids = [x.pid for x in gpu_offLine[minCntIndex].process] return {'cuda':gpu_offLine[minCntIndex].id,'pids':pids } if __name__=='__main__': #pres = getGPUProcesses() #print('###line404:',pres) gpus = getGPUs() for gpu in gpus: gpuUuidToIdMap[gpu.uuid] = gpu.id print(gpu) print(gpuUuidToIdMap) pres = getGPUProcesses() print('###line404:',pres) for pre in pres: print('#'*20) for ken in ['gpuName','gpuUuid','pid','processName','uid','uname','usedMemory' ]: print(ken,' ',pre.__getattribute__(ken )) print(' ')