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GPUtil.py
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GPUtil.py
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# 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
import os
import numpy as np
import time
__version__ = '1.3.0'
class GPU:
def __init__(self, ID, uuid, load, memoryTotal, memoryUsed, memoryFree, driver, gpu_name, serial, display_mode, display_active):
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
def safeFloatCast(strNumber):
try:
number = float(strNumber)
except ValueError:
number = float('nan')
return number
def getGPUs():
# Get ID, processing and memory utilization for all GPUs
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", "--format=csv,noheader,nounits"], stdout=PIPE)
output = p.stdout.read().decode('UTF-8')
# output = output[2:-1] # Remove b' and ' from string added by python
#print(output)
## Parse output
# Split on line break
lines = output.split(os.linesep)
#print(lines)
numDevices = len(lines)-1
deviceIds = np.empty(numDevices,dtype=int)
gpuUtil = np.empty(numDevices,dtype=float)
memTotal = np.empty(numDevices,dtype=float)
memUsed = np.empty(numDevices,dtype=float)
memFree = np.empty(numDevices,dtype=float)
driver = []
GPUs = []
for g in range(numDevices):
line = lines[g]
#print(line)
vals = line.split(', ')
#print(vals)
for i in range(11):
# print(vals[i])
if (i == 0):
deviceIds[g] = int(vals[i])
elif (i == 1):
uuid = vals[i]
elif (i == 2):
gpuUtil[g] = safeFloatCast(vals[i])/100
elif (i == 3):
memTotal[g] = safeFloatCast(vals[i])
elif (i == 4):
memUsed[g] = safeFloatCast(vals[i])
elif (i == 5):
memFree[g] = 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]
GPUs.append(GPU(deviceIds[g], uuid, gpuUtil[g], memTotal[g], memUsed[g], memFree[g], driver, gpu_name, serial, display_mode, display_active))
return GPUs # (deviceIds, gpuUtil, memUtil)
def getAvailable(order = 'first', limit=1, maxLoad=0.5, maxMemory=0.5, 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
# lowest --> select the GPU with the lowest load
# 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 = np.array(getAvailability(GPUs, maxLoad=maxLoad, maxMemory=maxMemory, includeNan=includeNan, excludeID=excludeID, excludeUUID=excludeUUID))
availAbleGPUindex = np.where(GPUavailability == 1)[0]
# 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: np.Inf if np.isnan(x.id) else x.id, reverse=False)
elif (order == 'last'):
GPUs.sort(key=lambda x: -np.Inf if np.isnan(x.id) else x.id, reverse=True)
elif (order == 'random'):
GPUs = [GPUs[g] for g in np.random.permutation(range(len(GPUs)))]
elif (order == 'load'):
GPUs.sort(key=lambda x: np.Inf if np.isnan(x.load) else x.load, reverse=False)
elif (order == 'memory'):
GPUs.sort(key=lambda x: np.Inf if np.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, includeNan=False, excludeID=[], excludeUUID=[]):
# Determine, which GPUs are available
GPUavailability = [1 if (gpu.load < maxLoad or (includeNan and np.isnan(gpu.load))) and (gpu.memoryUtil < maxMemory or (includeNan and np.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':'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,np.int64)):
attrStr = ('{0:d}').format(attr)
elif (isinstance(attr,str)):
attrStr = attr;
elif (isinstance(attr,unicode)):
attrStr = attr.encode('ascii','ignore')
else:
raise TypeError('Unhandled object type (' + str(type(attr)) + ') for attribute \'' + attrDict['name'] + '\'')
attrStr += attrSuffix
minWidth = np.maximum(minWidth,len(attrStr))
headerString += ' '*np.maximum(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,np.int64)):
attrStr = ('{0:' + minWidthStr + 'd}').format(attr)
elif (isinstance(attr,str)):
attrStr = ('{0:' + minWidthStr + 's}').format(attr);
elif (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)