Как использовать несколько GPU с TensorFlow (не требуются изменения кода)

Обзор контента Настройка Обзор Логирование устройства размещения Ручная установка устройства Ограничение роста памяти GPU Использование одного GPU на мульти-GPU-системе Использование множества GPU Код TensorFlow и Модели будут прозрачно работать на одном GPU без изменений кода. tf.keras Примечание: Используйте tf.config.list_physical_devices ('GPU') для подтверждения того, что TensorFlow использует GPU. Примечание: Используйте tf.config.list_physical_devices ('GPU') для подтверждения того, что TensorFlow использует GPU. Самый простой способ запуска на нескольких GPU, на одном или нескольких машинах, — это использование стратегий распределения. Это руководство предназначено для пользователей, которые пробовали эти подходы и обнаружили, что им необходим тонкий контроль над тем, как TensorFlow использует GPU. Руководство . Оптимизация производительности TensorFlow GPU Настройка Убедитесь, что у вас установлена последняя версия TensorFlow gpu. 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 Обзор TensorFlow поддерживает выполнение вычислений на различных типах устройств, включая CPU и GPU. Они представлены идентификаторами строк, например: «/device:CPU:0»: Процессор вашего компьютера. «/GPU:0»: короткометражная нотация первого GPU вашего компьютера, который виден для TensorFlow. "/job:localhost/replica:0/task:0/device:GPU:1": полностью квалифицированное название второго GPU вашего компьютера, которое видимо для TensorFlow. Если операция TensorFlow имеет как имплементации CPU, так и GPU, по умолчанию устройство GPU присваивается приоритет при назначении операции. имеет как ядра CPU, так и GPU, и на системе с устройствами и , The Устройство выбрано для запуска если вы явно не попросите его запустить на другом устройстве. tf.matmul CPU:0 GPU:0 GPU:0 tf.matmul Если операция TensorFlow не имеет соответствующей имплементации GPU, то операция возвращается к процессору. имеет только ядро CPU, на системе с устройствами и , The Устройство выбрано для запуска Даже если вы просите бегать на Устройство tf.cast CPU:0 GPU:0 CPU:0 tf.cast GPU:0 Логирование устройства размещения Чтобы узнать, на какие устройства присвоены ваши операции и тензоры, Разрешение записи местоположения устройства приводит к печати любых распределений или операций 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) Вышеуказанный код напечатает указание на был казнён на . MatMul GPU:0 Ручная установка устройства Если вы хотите, чтобы определенная операция выполнялась на устройстве по вашему выбору вместо того, что автоматически выбрано для вас, вы можете использовать для создания контекста устройства, и все операции в этом контексте будут выполняться на одном и том же назначенном устройстве. 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) Теперь вы увидите это и Они предназначены для Поскольку устройство не было специально указано для Операционная система TensorFlow будет выбирать одну операционную систему в зависимости от работы и доступных устройств ( в данном примере) и автоматически копировать тензоры между устройствами, если это необходимо. a b CPU:0 MatMul GPU:0 Ограничение роста памяти GPU По умолчанию TensorFlow отображает почти всю память GPU всех GPU (при условии Это делается для более эффективного использования относительно ценных ресурсов памяти GPU на устройствах путем уменьшения фрагментации памяти. Метод 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 некоторых случаях желательно, чтобы процесс выделил только подгруппу имеющейся памяти или только увеличил использование памяти в соответствии с потребностями процесса. Первый вариант — включить рост памяти путем вызова , который пытается выделить только столько GPU-памяти, сколько необходимо для распределения времени запуска: он начинает выделять очень мало памяти, и по мере запуска программы и требуется больше GPU-памяти, область памяти GPU расширяется для процесса 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 Другим способом включения этого варианта является установка экологической переменной. Два Эта конфигурация специфична для платформы. TF_FORCE_GPU_ALLOW_GROWTH true Второй способ — настроить виртуальное GPU-устройство с и установить жесткий предел общей памяти для распределения на 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 Это полезно, если вы хотите действительно связать количество доступной памяти GPU с процессом TensorFlow.Это обычная практика для локального разработки, когда GPU разделяется с другими приложениями, такими как GUI рабочей станции. Использование одного GPU на мульти-GPU-системе Если у вас есть более одного GPU в вашей системе, графический процессор с самым низким ID будет выбран по умолчанию. 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 Если указанное вами устройство не существует, вы получите : → . RuntimeError .../device:GPU:2 unknown device Если вы хотите, чтобы TensorFlow автоматически выбирал существующее и поддерживаемое устройство для выполнения операций в случае, если указанное не существует, вы можете позвонить . 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) Использование нескольких ГПУ Разработка для нескольких GPU позволит модели масштабироваться с дополнительными ресурсами.Если вы разрабатываете систему с одним GPU, вы можете симулировать несколько GPU с помощью виртуальных устройств.Это позволяет легко тестировать установки нескольких GPU без необходимости дополнительных ресурсов. 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 Как только для запуска есть несколько логических GPU, вы можете использовать несколько GPU с Или с ручной установкой. tf.distribute.Strategy с tf.distribute.Стратегия tf.distribute.Стратегия Лучшей практикой для использования нескольких GPU является использование Вот простой 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 Эта программа будет запускать копию вашей модели на каждом GPU, разделяя входные данные между ними, также известные как " «» Параллелизм данных Для получения дополнительной информации о стратегиях распределения, ознакомьтесь с руководством . здесь Ручное размещение Работает под капотом, реплицируя вычисления на разных устройствах. Вы можете вручную реализовать репликацию, построив свою модель на каждом 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) Первоначально опубликованная на веб-сайте TensorFlow, эта статья появляется здесь под новым заголовком и лицензирована под CC BY 4.0. Первоначально опубликованная на веб-сайте TensorFlow, эта статья появляется здесь под новым заголовком и лицензирована под CC BY 4.0. TensorFlow TensorFlow