def set_devices():
if (cn.__version__ != '2.2'):
raise Exception('Invalid CNTK Version')
all_devices = device.all_devices()
if all_devices[0].type() == device.DeviceKind.GPU:
print('You can use the GPU of your computer!!!')
device.try_set_default_device(device.gpu(0))
else:
print('Sorry, your computer only has a slow CPU')
device.try_set_default_device(device.cpu())
def set_devices():
if (cn.__version__ != '2.4'):
raise Exception('[ERROR]: Invalid CNTK Version')
all_devices = device.all_devices()
if all_devices[0].type() == device.DeviceKind.GPU:
print('[INFO]: You computer' 's GPU does suport CUDA acceleration')
device.try_set_default_device(device.gpu(0))
else:
print('[WARNING]: You computer'
's GPU does not suport CUDA acceleration')
device.try_set_default_device(device.cpu())
log("СКРИПТ ОБУЧЕНИЯ " + prediction_algorithm_name + " ЗАПУЩЕН...")
# секундомер
import time
tempTime = time.time()
def getTime():
global tempTime
offset = time.time() - tempTime
tempTime = time.time()
return str(offset)[0:5] + " сек."
#####################################
# попытка задать GPU, как устройство для ускорения вычислений
from cntk.device import try_set_default_device, gpu
import cntk.device as C
log("Все вычислительные устройства: " + str(C.all_devices()))
try:
log("Попытка установить GPU как устройство по умолчанию: " + str(C.try_set_default_device(C.gpu(0))))
except Exception as e:
log(str(e))
#log(C.use_default_device())
###################################################
# загрузка библиотек
import numpy
import json
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, Conv1D, GlobalAveragePooling1D, MaxPooling1D, Flatten
from keras import optimizers
#####################################################################
log("> время загрузки библиотек : " + getTime())
communicator.barrier()
# train in parallel
error = cifar_resnet_distributed(data_path,
load_model_filename=start_model,
communicator=communicator,
run_test=True,
num_epochs=num_parallel_epochs)
distributed.Communicator.finalize()
return error
if __name__ == '__main__':
# check if we have multiple-GPU, and fallback to 1 GPU if not
devices = device.all_devices()
gpu_count = 0
for dev in devices:
gpu_count += (1 if dev.type() == DeviceKind_GPU else 0)
print("Found {} GPUs".format(gpu_count))
if gpu_count == 0:
print("No GPU found, exiting")
quit()
data_path = os.path.abspath(
os.path.normpath(
os.path.join(
*"../../../../Examples/Image/DataSets/CIFAR-10/".split("/"))))
os.chdir(data_path)
prediction_algorithm_name = 'Easy'
print("СКРИПТ ПОТОЧНОГО ПРОГНОЗИРОВАНИЯ " + prediction_algorithm_name +
" ЗАПУЩЕН...")
import random
random.seed()
session = random.getrandbits(16)
print("session = " + (str)(session))
from cntk.device import try_set_default_device, gpu
import cntk.device as C
print(C.all_devices())
print(C.try_set_default_device(C.gpu(0)))
print(C.use_default_device())
import time
import sys
import argparse
import numpy
from datetime import datetime
from keras.models import load_model
import json
#print(sys.platform)
def createParser():
parser = argparse.ArgumentParser()
#parser.add_argument('--json_file_path',type=str,default='D:\Anton\Desktop\MAIN\Экспертная система\Экспертная система\Алгоритмы прогнозирования\LSTM 1\h.json')
parser.add_argument('--json_file_path', type=str)
return parser
# training the start model only in one worker
if communicator.current_worker().global_rank == 0:
cifar_resnet_distributed(data_path, save_model_filename=start_model, communicator=None, run_test=False, num_epochs=num_start_epochs)
communicator.barrier()
# train in parallel
error = cifar_resnet_distributed(data_path, load_model_filename=start_model, communicator=communicator, run_test=True, num_epochs=num_parallel_epochs)
distributed.Communicator.finalize()
return error
if __name__ == '__main__':
# check if we have multiple-GPU, and fallback to 1 GPU if not
devices = device.all_devices()
gpu_count = 0
for dev in devices:
gpu_count += (1 if dev.type() == DeviceKind_GPU else 0)
print("Found {} GPUs".format(gpu_count))
if gpu_count == 0:
print("No GPU found, exiting")
quit()
data_path = os.path.abspath(os.path.normpath(os.path.join(
*"../../../../Examples/Image/DataSets/CIFAR-10/".split("/"))))
os.chdir(data_path)
total_epochs = 11