gpus = tf.config.list_physical_devices("GPU") if gpus: try: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) print("tensorflow will use experimental.set_memory_growth(True)") except RuntimeE
该函数可以将GPU内存分配设置为按需增长,以便在需要时分配所需的内存,并在不使用时释放内存。 以下是在TensorFlow 2.0中清理GPU内存的步骤: 导入TensorFlow库: 代码语言:txt 复制 import tensorflow as tf 获取物理GPU设备列表: 代码语言:txt 复制 gpus = tf.config.experimental.list_physical_devices('GPU') 针对...
config.list_physical_devices('GPU') if len(physical_devices) > 0: tf.config.experimental.set_memory_growth(physical_devices[0], True) 1 2 3 4 5 6 或 config = tf.ConfigProto()#tf.ConfigProto()函数在创建session的时候,用来对session进行参数配置 config.gpu_options.allow_growth = True ...
>>> physical_gpus = tf.config.list_physical_devices("GPU")>>> physical_gpus[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] 管理GPU 内存 默认情况下,TensorFlow 在第一次运行计算时会自动占用几乎所有可用 GPU 的 RAM,以限制 GPU RAM 的碎片化。这意味着如果您尝试启动第二个 T...
config.get_visible_devices 9: config.list_logical_devices 10: config.list_physical_devices 11: config.run_functions_eagerly 12: config.set_logical_device_configuration 13: config.set_soft_device_placement 14: config.set_visible_devices 27: data 28: debugging 1: debugging.Assert 2: debugging.ass...
gpus = tf.config.list_physical_devices("GPU") if gpus: gpu0 = gpus[0] #如果有多个GPU,仅使用第0个GPU tf.config.experimental.set_memory_growth(gpu0, True) #设置GPU显存用量按需使用 # 或者也可以设置GPU显存为固定使用量(例如:4G) #tf.config.experimental.set_virtual_device_configuration(gpu0,...
_physical_devices = None self._physical_device_to_index = None self._visible_device_list = [] self._memory_growth_map = None self._virtual_device_map = {} # Values set after construction self._optimizer_jit = None self._intra_op_parallelism_threads = None self._inter_op_parallelism_...
I had a similar problem with 10.2 usingtf.config.experimental.list_physical_devices('GPU'). Cuda v10.2 was installed using this command after installing the ubuntu 18.04cudaandnvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.debrepos per the TensorFlow documetation athttps://www.tensorflow...
in device_lib.list_local_devices(): printdevice.name,'memory_limit', str(round(devicememory_limit/1024/1024)+'M', device.physicaldevice_desc) print('===') print_gpu_info() DATA_PATH "/Volumes/Cloud/DataSet" mnist= tflearn.datasets.mnist._data_sets(DATA_PATH+"/mnist...
self._physical_devices =Noneself._physical_device_to_index =Noneself._visible_device_list = [] self._memory_growth_map =Noneself._virtual_device_map = {}# Values set after constructionself._optimizer_jit =Noneself._intra_op_parallelism_threads =Noneself._inter_op_parallelism_threads =Nonese...