p = Process(target=myfuncHyper)
p.start()
p.join()
sizeArr = np.array(sizeArray)
sizeArr.tofile(savePath + "hyperSize.npy")
totalSize = Array("l", [0])
def myfuncTotal():
import pynvml
pynvml.nvmlInit()
deviceCount = pynvml.nvmlDeviceGetCount()
handle = pynvml.nvmlDeviceGetHandleByIndex(
int(os.environ['CUDA_VISIBLE_DEVICES']))
gpuMem = pynvml.nvmlDeviceGetMemoryInfo(handle)
totalMem = gpuMem.total
totalSize[0] = totalMem
p = Process(target=myfuncTotal)
p.start()
p.join()
totalSize = np.array(totalSize)
totalSize.tofile(savePath + "totalSize.npy")
print(datasetName)
print(sizeArr / totalSize[0])
print(np.max(sizeArr / totalSize[0]))
p.start()
p.join()
print(sizeDict)
sizeArr = np.array(sizeArray)
sizeArr.tofile(savePath + "hyperSize" + hyperparamSetName + ".npy")
totalSize = Array("l", [0])
def myfuncTotal():
import pynvml
pynvml.nvmlInit()
deviceCount = pynvml.nvmlDeviceGetCount()
handle = pynvml.nvmlDeviceGetHandleByIndex(
int(os.environ['CUDA_VISIBLE_DEVICES']))
gpuMem = pynvml.nvmlDeviceGetMemoryInfo(handle)
totalMem = gpuMem.total
totalSize[0] = totalMem
p = Process(target=myfuncTotal)
p.start()
p.join()
totalSize = np.array(totalSize)
totalSize.tofile(savePath + "totalSize" + hyperparamSetName + ".npy")
print(datasetName)
print(sizeArr / totalSize[0])
print(np.max(sizeArr / totalSize[0]))
