def avaiable_gpus(detect_time=3, cpu_ratio=0.5, mem_ratio=0.5):
"""
get avaiable gpu device ids
:param detect_time: seconds for detect
:param use_ratio: ratio lower bound of cpu and mem usage
:return: a list of avaiable gpu ids
"""
assert type(detect_time) == int and cpu_ratio <= 1. and mem_ratio <= 1.
# print('detecting valid gpus in %d seconds' % detect_time)
# 1.使用正则表达式获取单GPU总内存(所有显卡相同的情况)
total_mem = int(
re.findall(r'([0-9]+)MiB \|',
os.popen('nvidia-smi -i 0').readlines()[8])[0])
# 2.检测符合资源要求的GPU
pids, pcpu, mem, gpu_id = gi.get_info()
for i in range(detect_time - 1):
time.sleep(1)
pids, _pcpu, _mem, gpu_id = gi.get_info()
_pcpu, _mem = np.asarray(_pcpu), np.asarray(_mem)
pcpu = pcpu + _pcpu
mem = mem + _mem
pcpu, mem = np.asarray(pcpu) / detect_time, np.asarray(mem) / detect_time
valid_gpus = np.argwhere((pcpu <= cpu_ratio * 100) & (
mem <= mem_ratio * total_mem)).reshape(-1).tolist()
valid_gpus = sorted(valid_gpus, key=lambda x: mem[x] * 100 + pcpu[x])
# 3. 打印信息
# info = ['GPU%d: %d%%-%.1fG' % (x[0], x[1], x[2] / 1024) for x in zip(valid_gpus, pcpu[valid_gpus], mem[valid_gpus])]
# print('valid gpus: | '.join(info))
return valid_gpus
def __init__(self):
self.op_system_name = os.name
self.user_name = getpass.getuser()
# self.total, self.used, self.free = shutil.disk_usage("/")
simp_list = shutil.disk_usage("/")
self.total = simp_list[0] // (2**30)
self.used = simp_list[1] // (2**30)
self.free = simp_list[2] // (2**30)
self.Gpu_is_empty = GPUInfo.get_info()
self.Gpu_info = GPUInfo.get_info()
self.mem = psutil.virtual_memory()
self.mem_total = self.mem.total
self.Virtual_memory = self.mem_total / (1024**3)
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 get_pids():
# Gets all PIDs on all GPUs as a dictionary
# Each key is a GPU ID
info = GPUInfo.get_info()
pids = info[0]
pids = {value[0]: key for key, value in pids.items()}
return pids
print("Please install GPU version of TF")
from model.YOLOv4 import YOLOv4
import numpy as np
import gc
from tqdm import tqdm
from gpuinfo import GPUInfo
x = 320
y = 320
inp = np.ones((4, x, y, 3), dtype=np.float64)
network = YOLOv4(side=x)
network.compile(loss="mse", optimizer="sgd", metrics="acc")
small, medium, large = network(inp)
print(GPUInfo.get_info())
print((x, y), " work and give :", small.shape, medium.shape, large.shape)
network.summary()
print(network.layers[-1].output_shape)
from dataset import Dataset
dataset = Dataset(4,
network.layers[-1].output_shape,
shape=network.layers[0].input_shape[1:3])
print("input shape is :", dataset.shape)
image_path = dataset.get_dir("test")
labels = dataset.get_label_data("test")
print(np.array(image_path).shape)
print(labels[0])