Как да използвате няколко GPU с TensorFlow (не се изискват промени в кода)

Общ преглед на съдържанието Настройване Преглед Логиране на устройството Ръчно разположение на устройството Ограничаване на растежа на GPU паметта Използване на един GPU на мулти-GPU система Използване на множество GPU Кодът TensorFlow и Моделите ще се изпълняват прозрачно на един GPU без да се налага промяна на кода. tf.keras Забележка: Използвайте tf.config.list_physical_devices ('GPU') за да потвърдите, че TensorFlow използва GPU. Използвайте за да потвърдите, че TensorFlow използва GPU. Note: tf.config.list_physical_devices('GPU') Най-лесният начин да работите на няколко GPU-та, на една или повече машини, е да използвате стратегии за разпространение. Това ръководство е за потребители, които са опитали тези подходи и са установили, че се нуждаят от фин контрол за това как TensorFlow използва GPU. на ръководството. Оптимизиране на производителността на TensorFlow GPU Настройване Уверете се, че имате най-новата версия на TensorFlow gpu инсталирана. import tensorflow as tf print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) 2024-08-15 02:53:40.344028: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-08-15 02:53:40.365851: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-08-15 02:53:40.372242: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered Num GPUs Available: 4 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1723690422.944962 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.948934 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.952655 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.955880 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.967120 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.970596 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.973980 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.976984 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.979869 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.983344 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.986754 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.989690 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 Преглед TensorFlow поддържа изчисления, които се изпълняват на различни видове устройства, включително CPU и GPU. Те са представени с идентификатори на струни, например: "/device:CPU:0": Процесорът на вашата машина. "/GPU:0": Кратка нотация за първия GPU на вашия компютър, който е видим за TensorFlow. "/job:localhost/replica:0/task:0/device:GPU:1": Пълно квалифицирано име на втория GPU на вашата машина, който е видим за TensorFlow. Ако операцията TensorFlow има както CPU, така и GPU имплементации, по подразбиране GPU устройството е приоритетно, когато операцията е зададена. има както CPU, така и GPU ядра и на система с устройства и , на устройството е избрано за изпълнение освен ако изрично не поискате да го изпълните на друго устройство. tf.matmul CPU:0 GPU:0 GPU:0 tf.matmul Ако операцията TensorFlow няма съответно изпълнение на GPU, операцията се връща към CPU устройството. има само един ядро на CPU, на система с устройства и , на устройството е избрано за изпълнение Дори и да е поискано да се изкачи на устройството си. tf.cast CPU:0 GPU:0 CPU:0 tf.cast GPU:0 Логиране на устройството За да разберете на кои устройства са зададени вашите операции и тензори, поставете Позволяването на записване на местоположението на устройството води до отпечатване на всички разпределения или операции на Tensor. tf.debugging.set_log_device_placement(True) tf.debugging.set_log_device_placement(True) # Create some tensors a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) c = tf.matmul(a, b) print(c) Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 I0000 00:00:1723690424.215487 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.217630 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.219585 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.221664 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.223723 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.225666 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.227528 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.229544 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.231494 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.233433 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.235295 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.237325 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.276919 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.278939 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.280845 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.282884 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.284977 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.286923 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.288779 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.290783 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.292741 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.295170 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.297460 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690424.299854 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0 tf.Tensor( [[22. 28.] [49. 64.]], shape=(2, 2), dtype=float32) Горният код ще отпечата индикация за е екзекутиран на . MatMul GPU:0 Ръчно разположение на устройството Ако искате определена операция да се изпълнява на устройство по ваш избор, вместо това, което е автоматично избрано за вас, можете да използвате да създадете контекст на устройството и всички операции в този контекст ще се изпълняват на едно и също определено устройство. with tf.device tf.debugging.set_log_device_placement(True) # Place tensors on the CPU with tf.device('/CPU:0'): a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) # Run on the GPU c = tf.matmul(a, b) print(c) Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0 tf.Tensor( [[22. 28.] [49. 64.]], shape=(2, 2), dtype=float32) Сега ще видите това и Предназначени са за Тъй като устройството не е изрично посочено за В зависимост от операцията, TensorFlow runtime ще избере един въз основа на операцията и наличните устройства ( в този пример) и автоматично копира тензорите между устройствата, ако е необходимо. a b CPU:0 MatMul GPU:0 Ограничаване на растежа на GPU паметта По подразбиране TensorFlow картографира почти цялата GPU памет на всички GPU (при условие че Това се прави, за да се използват по-ефективно относително ценните ресурси на GPU паметта на устройствата, като се намали фрагментацията на паметта. Методът . CUDA_VISIBLE_DEVICES tf.config.set_visible_devices gpus = tf.config.list_physical_devices('GPU') if gpus: # Restrict TensorFlow to only use the first GPU try: tf.config.set_visible_devices(gpus[0], 'GPU') logical_gpus = tf.config.list_logical_devices('GPU') print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU") except RuntimeError as e: # Visible devices must be set before GPUs have been initialized print(e) Visible devices cannot be modified after being initialized В някои случаи е желателно процесът да разпределя само поднабор от наличната памет или да увеличава използването на паметта само според нуждите на процеса. Първата опция е да включите растежа на паметта, като , който се опитва да разпредели само толкова GPU памет, колкото е необходимо за разпределението на времето за изпълнение: той започва да разпределя много малко памет и тъй като програмата се изпълнява и се изисква повече GPU памет, областта на паметта на GPU се разширява за процеса TensorFlow. tf.config.experimental.set_memory_growth gpus = tf.config.list_physical_devices('GPU') if gpus: try: # Currently, memory growth needs to be the same across GPUs for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) logical_gpus = tf.config.list_logical_devices('GPU') print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs") except RuntimeError as e: # Memory growth must be set before GPUs have been initialized print(e) Physical devices cannot be modified after being initialized Друг начин за активиране на тази опция е да зададете променливата на околната среда. две Тази конфигурация е специфична за платформата. TF_FORCE_GPU_ALLOW_GROWTH true Вторият метод е да конфигурирате виртуално GPU устройство с и задайте твърд лимит за общата памет, която да се разпредели върху GPU. tf.config.set_logical_device_configuration gpus = tf.config.list_physical_devices('GPU') if gpus: # Restrict TensorFlow to only allocate 1GB of memory on the first GPU try: tf.config.set_logical_device_configuration( gpus[0], [tf.config.LogicalDeviceConfiguration(memory_limit=1024)]) logical_gpus = tf.config.list_logical_devices('GPU') print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs") except RuntimeError as e: # Virtual devices must be set before GPUs have been initialized print(e) Virtual devices cannot be modified after being initialized Това е полезно, ако искате наистина да обвържете количеството GPU памет, налична за процеса TensorFlow. Използване на един GPU на мулти-GPU система Ако имате повече от един GPU в системата си, GPU с най-нисък ID ще бъде избран по подразбиране. tf.debugging.set_log_device_placement(True) try: # Specify an invalid GPU device with tf.device('/device:GPU:2'): a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) c = tf.matmul(a, b) except RuntimeError as e: print(e) Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:2 Ако устройството, което сте задали, не съществува, ще получите : на . RuntimeError .../device:GPU:2 unknown device Ако искате TensorFlow автоматично да избере съществуващо и поддържано устройство за изпълнение на операциите в случай, че посоченото не съществува, можете да се обадите на . tf.config.set_soft_device_placement(True) tf.config.set_soft_device_placement(True) tf.debugging.set_log_device_placement(True) # Creates some tensors a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) c = tf.matmul(a, b) print(c) Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0 tf.Tensor( [[22. 28.] [49. 64.]], shape=(2, 2), dtype=float32) Използване на множество GPU Разработването за няколко GPU ще позволи на един модел да скалира с допълнителните ресурси. Ако разработвате на система с един GPU, можете да симулирате няколко GPU с виртуални устройства. gpus = tf.config.list_physical_devices('GPU') if gpus: # Create 2 virtual GPUs with 1GB memory each try: tf.config.set_logical_device_configuration( gpus[0], [tf.config.LogicalDeviceConfiguration(memory_limit=1024), tf.config.LogicalDeviceConfiguration(memory_limit=1024)]) logical_gpus = tf.config.list_logical_devices('GPU') print(len(gpus), "Physical GPU,", len(logical_gpus), "Logical GPUs") except RuntimeError as e: # Virtual devices must be set before GPUs have been initialized print(e) Virtual devices cannot be modified after being initialized След като има няколко логически GPU на разположение за времето за изпълнение, можете да използвате няколко GPU с Или с ръчно поставяне. tf.distribute.Strategy с tf.distribute.Strategy tf.distribute.Стратегия Най-добрата практика за използване на множество GPUs е да използвате Ето един прост пример: tf.distribute.Strategy tf.debugging.set_log_device_placement(True) gpus = tf.config.list_logical_devices('GPU') strategy = tf.distribute.MirroredStrategy(gpus) with strategy.scope(): inputs = tf.keras.layers.Input(shape=(1,)) predictions = tf.keras.layers.Dense(1)(inputs) model = tf.keras.models.Model(inputs=inputs, outputs=predictions) model.compile(loss='mse', optimizer=tf.keras.optimizers.SGD(learning_rate=0.2)) INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3') Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op FloorMod in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Cast in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op StatelessRandomGetKeyCounter in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 a: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 b: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 MatMul: (MatMul): /job:localhost/replica:0/task:0/device:GPU:0 product_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:2 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:2 a: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 b: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 MatMul: (MatMul): /job:localhost/replica:0/task:0/device:GPU:2 product_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:2 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0 input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:1 _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:1 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:1 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:1 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:1 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:1 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:1 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:1 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:2 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:2 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:2 input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:3 _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:3 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:3 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:3 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:3 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:3 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:3 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:3 input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 FloorMod: (FloorMod): /job:localhost/replica:0/task:0/device:GPU:0 z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 Cast: (Cast): /job:localhost/replica:0/task:0/device:GPU:0 y_RetVal: (_DeviceRetval): /job:localhost/replica:0/task:0/device:GPU:0 input: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0 _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 seed: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0 StatelessRandomGetKeyCounter: (StatelessRandomGetKeyCounter): /job:localhost/replica:0/task:0/device:GPU:0 key_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 counter_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0 key: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 counter: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 alg: (_DeviceArg): /job:localhost/replica:0/taExecuting op StatelessRandomUniformV2 in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Sub in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op AddV2 in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Fill in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:1 sk:0/device:CPU:0 StatelessRandomUniformV2: (StatelessRandomUniformV2): /job:localhost/replica:0/task:0/device:GPU:0 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 Sub: (Sub): /job:localhost/replica:0/task:0/device:GPU:0 z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 Mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0 z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 AddV2: (AddV2): /job:localhost/replica:0/task:0/device:GPU:0 z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 ReadVariableOp: (ReadVariableOp): /job:localhost/replica:0/task:0/device:GPU:0 value_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:1 Identity: (Identity): /job:localhost/replica:0/task:0/device:GPU:1 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:1 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:1 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:1 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:1 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:1 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:1 input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 Identity: (Identity): /job:localhost/replica:0/task:0/device:GPU:2 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:2 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:2 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:2 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:2 input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:3 Identity: (Identity): /job:localhost/replica:0/task:0/device:GPU:3 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:3 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:3 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:3 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:3 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:3 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:3 NoOp: (NoOp): /job:localhost/replica:0/task:0/device:GPU:0 dims: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 Fill: (Fill): /job:localhost/replica:0/task:0/device:GPU:0 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 ReadVariableOp: (ReadVariableOp): /job:localhost/replica:0/task:0/device:GPU:0 value_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:1 VarHandleOp: (VarHandleOp): /job:lExecuting op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Fill in device /job:localhost/replica:0/task:0/device:GPU:0 ocalhost/replica:0/task:0/device:GPU:1 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:1 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:1 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:1 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:2 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:2 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:2 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:3 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:3 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:3 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:3 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:3 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 ReadVariableOp: (ReadVariableOp): /job:localhost/replica:0/task:0/device:GPU:0 value_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:1 Identity: (Identity): /job:localhost/replica:0/task:0/device:GPU:1 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:1 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:1 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:1 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:1 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:1 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:1 input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 Identity: (Identity): /job:localhost/replica:0/task:0/device:GPU:2 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:2 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:2 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:2 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:2 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:2 input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:3 Identity: (Identity): /job:localhost/replica:0/task:0/device:GPU:3 output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:3 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:3 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:3 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:3 value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:3 AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:3 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0 resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0 ReadVariableOp: (ReadVariableOp): /job:localhost/replica:0/task:0/device:GPU:0 value_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:1 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:1 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:2 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:2 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:3 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:3 resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0 VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/deviExecuting op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Fill in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op NoOp in device /job:localhost/replica:0/task:0/device:GPU:0 Тази програма ще изпълнява копие от вашия модел на всеки GPU, като разделя входните данни между тях, известни също като " „ Паралелност на данните За повече информация относно стратегиите за дистрибуция вижте ръководството . тук Ръчно разположение Работи под капака чрез възпроизвеждане на изчисления на различни устройства. Можете ръчно да реализирате възпроизвеждане, като изградите своя модел на всеки GPU. Например: tf.distribute.Strategy tf.debugging.set_log_device_placement(True) gpus = tf.config.list_logical_devices('GPU') if gpus: # Replicate your computation on multiple GPUs c = [] for gpu in gpus: with tf.device(gpu.name): a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) c.append(tf.matmul(a, b)) with tf.device('/CPU:0'): matmul_sum = tf.add_n(c) print(matmul_sum) Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:1 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:2 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:3 Executing op AddN in device /job:localhost/replica:0/task:0/device:CPU:0 tf.Tensor( [[ 88. 112.] [196. 256.]], shape=(2, 2), dtype=float32) Първоначално публикувана на уебсайта на TensorFlow, тази статия се появява тук под ново заглавие и е лицензирана под CC BY 4.0. Първоначално публикувана на уебсайта на TensorFlow, тази статия се появява тук под ново заглавие и е лицензирана под CC BY 4.0. TensorFlow TensorFlow