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]))