gfs.py

from atomicwrites import atomic_write
from subprocess import check_output, CalledProcessError

class Utilization:
    def __init__(self, gpu, memory):
        self.gpu    = float(gpu)
        self.memory = float(memory)
        if self.gpu < 2:
            self.gpu = 2
    def set_gpu(self, gpu):
        self.gpu    = gpu
    def set_mem(self, memory):
        self.memory = memory
    def set_id(self, id):
        self.id     = id
    def set_cap(self, wa, wb):
        self.cap    = wa*self.gpu + wb*self.memory

output_gpu = check_output('nvidia-smi --query-gpu=utilization.gpu --format=csv', shell=True)
output_gpu_split = output_gpu.split('\n')
device_num = len(output_gpu_split) - 2

d_gpu = []
for i in range(device_num):
    d_gpu.append(filter(str.isdigit, output_gpu_split[i+1]))
    #print d_gpu[i]

output_memory = check_output('nvidia-smi --query-gpu=memory.used --format=csv', shell=True)
output_memory_split = output_memory.split('\n')

d_memory = []
for i in range(device_num):
    d_memory.append(filter(str.isdigit, output_memory_split[i+1]))
    #print d_memory[i]


output_memory = check_output('nvidia-smi --query-gpu=memory.total --format=csv', shell=True)
output_memory_split = output_memory.split('\n')

for i in range(device_num):
    d_memory[i] = float(d_memory[i]) / float(filter(str.isdigit, output_memory_split[i+1]))
    #print d_memory[i]

Wa = 0.5
Wb = 0.5
device_obj=[]
for i in range(device_num):
    device_obj.append(Utilization(d_gpu[i], d_memory[i]))
    device_obj[i].set_cap(Wa, Wb)
    device_obj[i].set_id(i)
    #print device_obj[i].gpu
    #print device_obj[i].memory
    #print device_obj[i].cap
    #print device_obj[i].id

v_gpu = []
f_gpu = []
file = open('/home/coldfunction/qCUDA_0.2/qCUDA/.gpu_info', 'r')
for i in range(device_num):
    line = file.readline()
    num = float(line)
    
    v_gpu.append(num)
    f_gpu.append(num)
#    if num == 0:
#        print(num)
file.close()


for i in range(device_num):
    if v_gpu[i] == 0:
        v_gpu[i] = 1.0 
   
    device_obj[i].cap = (v_gpu[i] * device_obj[i].cap)
    #print (device_obj[i].cap)

    #print(v_gpu[i])
    

#print
device_obj.sort(key=lambda i: i.cap) 

id = device_obj[0].id

f_gpu[id] = device_obj[0].cap

with atomic_write('/home/coldfunction/qCUDA_0.2/qCUDA/.gpu_info', overwrite=True) as f:
    for i in range(device_num):
        f.write(str(f_gpu[i]))
        f.write('\n')

print id




#f = open(".select_g", 'w')
#s = str(device_obj[0].id)
#f.write(s)