[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)])exceptRuntimeErrorase:# 可在程序启动后设置设备的配置print(e)# 验证设置print("已设置 GPU 内存限制:")forgpuingpus:print(gpu) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12
os.environ["CUDA_DEVICE_ORDER"] ="PCI_BUS_ID"os.environ["CUDA_VISIBLE_DEVICES"] ="0"#使用第一块GPU fromtensorflow.python.clientimportdevice_libprint(device_lib.list_local_devices()) [name: "/device:CPU:0" device_type: "CPU" memory_limit: 268435456 locality { } incarnation: 1222567532145...
[tf.config.LogicalDeviceConfiguration(memory_limit=1024), tf.config.LogicalDeviceConfiguration(memory_limit=1024)]) 1. 2. 3. 4. 上述代码是3.2的代码的扩充,目的是从实际GPU0资源中,分配两块GPU资源区,可以视为产生了两块虚拟GPU,虚拟GPU的生存周期是当前运行的tensorflow程序进程。 使用单机多卡训练MirroredS...
The second method is to configure a virtual GPU device with tf.config.experimental.set_virtual_device_configuration and set a hard limit on the total memory to allocate on the GPU. This is useful if you want to truly bound the amount of GPU memory available to the TensorFlow process. This ...
name: GeForce GT 730 major: 3 minor: 5 memoryClockRate(GHz): 0.9015 pciBusID: 0000:01:00.0 totalMemory: 2.00GiB freeMemory: 1.66GiB 2018-11-06 16:27:41.442557: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1484] Adding visible gpu devices: 0 ...
TensorFlow提供了tf.config.experimental.set_memory_growth函数,可以动态地分配GPU内存,根据需要增加或减少内存的使用量。 可以使用tf.config.experimental.set_virtual_device_configuration函数来限制GPU内存的使用量,以确保不超过设定的阈值。 CPU限制: TensorFlow提供了tf.config.threading.set_inter_op_parallelism_threads...
这些数据的文件使用数据集对象类被加载到 TensorFlow 图中，这样可以让 TensorFlow 在加载、预处理和载入单批数据时效率更高，节省 CPU 和 GPU 内存负载。数据集对象中数据字段的示例如下所示：class DataSet:def __init__(self , txt_files, thread_count, batch_size, numcep, numcontext):# ...def from_...
gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: # Create 2 virtual GPUs with 1GB memory each try: tf.config.experimental.set_virtual_device_configuration( gpus[0], [ tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024), ...
W tensorflow/stream_executor/stream_executor_pimpl.cc:490] Not enough memory to allocate 31614597888 on device 0 within provided limit. [used=0, limit=1073741824] This looks like only 1GB should be used at most for the tests due to that limit (although I'm unsure where this limit is set...
GPUOptions(per_process_gpu_memory_fraction=0.200) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) sess.run(init) # Restore only the CONV weights (From AutoEncoder) saver_load_autoencoder.restore(sess, "/tmp/cae_cnn/model.ckpt-34") # Add some tensors to observe on ...