def set_GPU(num_of_GPUs):
current_memory_gpu = GPUInfo.gpu_usage()[1]
list_available_gpu = np.where(
np.array(current_memory_gpu) < 1500)[0].astype('str').tolist()
current_available_gpu = ",".join(list_available_gpu)
if len(list_available_gpu) < num_of_GPUs:
print("==============Warning==============")
print("Your process had been terminated")
print("Please decrease number of gpus you using")
print(
f"number of Devices available:\t{len(list_available_gpu)} gpu(s)")
print(f"number of Device will use:\t{num_of_GPUs} gpu(s)")
sys.exit()
elif len(list_available_gpu) > num_of_GPUs:
redundant_gpu = len(list_available_gpu) - num_of_GPUs
list_available_gpu = list_available_gpu[:redundant_gpu]
current_available_gpu = ",".join(list_available_gpu)
print("[DEBUG]***********************************************")
print(f"[DEBUG]You are using GPU(s): {current_available_gpu}")
print("[DEBUG]***********************************************")
os.environ["CUDA_VISIBLE_DEVICES"] = current_available_gpu
else:
print("[DEBUG]***********************************************")
print(f"[DEBUG]You are using GPU(s): {current_available_gpu}")
print("[DEBUG]***********************************************")
os.environ["CUDA_VISIBLE_DEVICES"] = current_available_gpu
def __main__():
op_system_name = os.name
op_user = getpass.getuser()
print(op_user)
if op_system_name == 'posix':
print("This operating System is a Mac")
if op_system_name == 'nt':
print("this operating System is a Windows")
if op_system_name == "java":
print("this operating System is Java")
total, used, free = shutil.disk_usage("/")
print("Total Storage: %d GB" % (total // (2**30)))
print("Used Storage: %d GB" % (used // (2**30)))
print("Free Storage: %d GB" % (free // (2**30)))
print("cpu percentage = {}\nVirtual Storage = {}".format(
psutil.cpu_percent(), psutil.virtual_memory()))
for each in psutil.virtual_memory():
print(each)
print('memory % used:', psutil.virtual_memory()[2])
print(GPUInfo.check_empty())
print(GPUInfo.get_info())
print(GPUInfo.gpu_usage())
mem = psutil.virtual_memory()
print(mem.total)
print("Ram = {}".format(mem.total / 1024.**3))
op_check = OpSysChecker()
print("-----------------\n{}".format(op_check.return_info()))
def set_GPU(num_of_GPUs, log, memory_restraint=0):
try:
from gpuinfo import GPUInfo
current_memory_gpu = GPUInfo.gpu_usage()[1]
if not memory_restraint:
list_available_gpu = np.where(
np.array(current_memory_gpu))[0].astype('str').tolist()
else:
list_available_gpu = np.where(
np.array(current_memory_gpu) < memory_restraint)[0].astype(
'str').tolist()
except:
log.write_log(
"Cannot find nvidia-smi, please include it into Environment Variables",
message_type=0)
print(
"[INFO] Cannot find nvidia-smi, please include it into Environment Variables"
)
if torch.cuda.is_available():
list_available_gpu = [str(i) for i in range(num_of_GPUs)]
else:
list_available_gpu = []
list_gpu_using = list_available_gpu[:num_of_GPUs]
if len(list_available_gpu) < num_of_GPUs and len(list_available_gpu) > 0:
print("==============Warning==============")
print("Your process had been terminated")
print("Please decrease number of gpus you using")
print(
f"number of Devices available:\t{len(list_available_gpu)} gpu(s)")
print(f"number of Device will use:\t{num_of_GPUs} gpu(s)")
log.write_log(
f"number of Devices available:\t{len(list_available_gpu)} gpu(s) < number of Device will use:\t{num_of_GPUs} gpu(s)",
message_type=2)
sys.exit()
elif num_of_GPUs <= len(list_available_gpu) and num_of_GPUs != 0:
current_available_gpu = ",".join(list_gpu_using)
elif num_of_GPUs == 0 or len(list_available_gpu) == 0:
current_available_gpu = "-1"
print("[INFO] ***********************************************")
if len(list_gpu_using) > 0:
tmp_message = f"[INFO] You are using GPU(s): {current_available_gpu}"
else:
tmp_message = "[INFO] You are using CPU !"
print(tmp_message)
if log is not None:
log.write_log(tmp_message, message_type=0)
print("[INFO] ***********************************************")
os.environ["CUDA_VISIBLE_DEVICES"] = current_available_gpu
def gpu_usage():
total_memory = 32000
available_device = GPUInfo.check_empty()
percent, memory = GPUInfo.gpu_usage()
for i in range(len(memory)):
memory[i] = float(memory[i]) / total_memory
print(memory)
return memory
def get_gpu_info(self):
print("GPUINFO: Start loop")
while self.gpu_thread:
self.thread_lock.acquire()
self.gpu_percent, self.gpu_memory = GPUInfo.gpu_usage()
self.thread_lock.release()
time.sleep(2)
print("GPUINFO: End loop")
return
def __getitem__(self, index):
vid_dir, label, frame_count, class_name = self.data_list[index]
buffer = 0 #np.empty((self.clip_len, self.resize_height, self.resize_width, 3), np.dtype('float32'))
text = 0
try:
if 'text_only' != self.method:
buffer = self.load_frames(vid_dir, frame_count)
if self.do_crop:
buffer = self.spatial_crop(buffer, self.crop_size)
buffer = self.normalize(buffer)
buffer = self.to_tensor(buffer)
if 'joint' == self.method or 'text_only' == self.method:
key=(vid_dir.split('/')[-1]).split('.')[0]
text = get_text_description(self.dictionary_pickle, key)
text=text.astype('float32')
#text=text
except Exception as e:
print("Error ", e)
from gpuinfo import GPUInfo
print(GPUInfo.gpu_usage())
#sys.exit()
return buffer, label, text
file = open("RendimientoAgrupados.txt", "w")
tiempo = int(input("Ingrese tiempo para tomar datos (segundos)[ejem 120s]: "))
intervalo = float(
input("Ingrese intervalo de tiempo entre muestra (segundos)[ejem 0.5s]: "))
muestras = int(tiempo / intervalo)
while True:
selecOption = input("realizar Experimento[y / n]: ")
if selecOption == "n":
break
else:
label = input("Nombre del Experimento [ejem: 320x240]: ")
for x in range(muestras):
cpu_per = psutil.cpu_percent(interval=intervalo) # % de uso CPU
aux = psutil.virtual_memory()
ram = aux[2] # % de uso Ram
gpu_percent, gpu_memory = GPUInfo.gpu_usage()
gpu = gpu_percent[0] + round(random(), 1) # % de uso GPU
gpu_vram = round((gpu_memory[0] * 100) / vram, 1) # % uso RAM GPU
file.write(str(cpu_per) + " ")
file.write(str(ram) + " ")
file.write(str(gpu) + " ")
file.write(str(gpu_vram) + " ")
file.write(label + "\n")
file.close()
print("TiempoTranscurrido(s)=" + str(tiempo) + " NúmeroMuestras=" +
str(muestras))
time.sleep(8)
dest = '/home/storms/Progress/Images/gauges/temp_gaugej_br.png'
src = '/home/storms/Progress/Images/gauges/bottom_right/temp_gaugej_br_' + mem_str + '.png'
copyfile(src, dest)
gpu_temp = int(
subprocess.check_output(["nvidia-settings -query [gpu:0]/GPUCoreTemp -t"],
shell=True).decode("utf-8"))
gpu_temp = str(14 + 2 * round(gpu_temp / 2))
src = '/home/storms/Progress/Images/gauges/top_right/temp_gaugej_tr_' + gpu_temp + '.png'
dest = '/home/storms/Progress/Images/gauges/temp_gaugej_tr.png'
copyfile(src, dest)
use_stats = GPUInfo.gpu_usage()
gpu_usage = use_stats[0][0]
vram_usage = round(use_stats[1][0] / 1000)
dest = '/home/storms/Progress/Images/gauges/zotac_symbol.png'
src = '/home/storms/Progress/Images/gauges/zotac_body/zotac_gaming_logo' + str(
gpu_usage) + '-01.png'
copyfile(src, dest)
dest = '/home/storms/Progress/Images/gauges/Zotac_Wings.png'
src = '/home/storms/Progress/Images/gauges/Zotac_Wings/zotac_wings' + str(
vram_usage) + '.png'
copyfile(src, dest)
"""""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """
Section 2: Update Progress Bars
""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """"""
from gpuinfo import GPUInfo available_device=GPUInfo.check_empty() #available_device就是一个含有所有没有任务的gpu编号的列表 print(available_device) percent,memory=GPUInfo.gpu_usage() #获得所有gpu的使用百分比和显存占用量 print(percent, memory) min_percent=percent.index(min([percent[i] for i in available_device])) #未被使用的gpu里percent最小的 print(min_percent) min_memory=memory.index(min([memory[i] for i in available_device])) #未被使用的gpu里显存占用量最少的 print(min_memory)
def compute_features(dataloader, model, N):
if args.verbose:
print('Compute features')
batch_time = AverageMeter()
end = time.time()
model.eval()
print("Before", GPUInfo.gpu_usage())
# discard the label information in the dataloader
try:
for i, (input_tensor, _, text) in enumerate(dataloader):
torch.no_grad()
try:
if text_only:
#text = torch.autograd.Variable(text)#.cuda()) # , volatile=True)
#print(text[0].split()[:512].size)
aux = model.extract_features(
text.cuda()).data.cpu().numpy()
aux = aux.astype('float32')
elif is_joint:
input_var = torch.autograd.Variable(
input_tensor.cuda()) # , volatile=True)
text = text.cuda()
aux = model.module.extract_features(
input_var, text).data.cpu().numpy()
aux = aux.astype('float32')
else:
input_var = torch.autograd.Variable(
input_tensor.cuda()) # , volatile=True)
aux = model.module.extract_features(
input_var).data.cpu().numpy()
aux = aux.astype('float32')
if i == 0:
features = np.zeros((N, aux.shape[1]), dtype='float32')
if i < len(dataloader):
features[i * args.batch:(i + 1) * args.batch] = aux
else:
# special treatment for final batch
features[i * args.batch:] = aux
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if args.verbose and (i % 50) == 0:
print('{0} / {1}\t'
'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})'.
format(i, len(dataloader), batch_time=batch_time))
except Exception as e:
print("RAM Usage: ", str(psutil.virtual_memory().percent))
print(GPUInfo.gpu_usage())
print("failed: ", e)
except RuntimeError:
print("RAM Usage: ", str(psutil.virtual_memory().percent))
print(GPUInfo.gpu_usage())
return features
except Exception as e:
print("Error {}".format(e))
finally:
return features
cpu
) # here the same, but only 1 space. because 85.6 have only 4 characters
else:
cpuStr = str(
cpu
) #100.0 is 5 characters so there is no need to put spaces in before
# CPU Speed
cpufreq = round(
psutil.cpu_freq().current / 1000, 1
) # get cpu frequency, divide by 1000 because 1GHz = 1000MHz, round to one decimal place
cpufreqStr = " " + str(
cpufreq) # convert to string and make 5 spaces in total
# GPU Info
percent, memory = GPUInfo.gpu_usage(
) # get GPU utilization and used memory info
# GPU Utilization
percentNum = percent[0] # obtain number from the percent list
if percentNum < 10: # same as for CPU (make 5 spaces)
percentStr = " " + str(percentNum)
elif percentNum < 100:
percentStr = " " + str(percentNum)
else:
percentStr = " " + str(percentNum)
# GPU Used Memory
memoryNum = round(
memory[0] / 1024,
1) # obtain number from the memory list and round to one decimal place
def gpu_info():
try:
percent, memory = GPUInfo.gpu_usage()
except ValueError:
return "Error when read GPU utilization"
return "precent: %r, memory: %r" % (percent, memory)