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Как использовать несколько графических процессоров с TensorFlow (не требуется изменений кода)

1 августа 2025 г.

Обзор контента

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Обзор
Расположение устройства ведения журнала
Ручное размещение устройства
Ограничение роста памяти графического процессора
Использование одного графического процессора в системе с несколькими GPU
Используя несколько графических процессоров

Tensorflow Code, иtf.kerasМодели будут прозрачно работать на одном графическом процессоре без изменений кода.

Примечание:Использоватьtf.config.list_physical_devices('GPU')Чтобы подтвердить, что TensorFlow использует GPU.

Самый простой способ запуска на нескольких графических процессорах, на одном или многих машинах, - это использование стратегий распределения.

Это руководство предназначено для пользователей, которые пробовали эти подходы и обнаружили, что им нужен мелкозернистый контроль над тем, как Tensorflow использует GPU. Чтобы узнать, как отлаживать проблемы с производительностью для сценариев одиночных и мульти-GPU, см.Оптимизировать производительность графического процессора TensorFlowгид.

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Убедитесь, что у вас установлен последний выпуск GPU TensorFlow.

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 запуск вычислений на различных типах устройств, включая процессор и графический процессор. Например, они представлены с идентификаторами строк:

"/device:CPU:0": ЦП вашей машины.
"/GPU:0": Коротко-ручные обозначения для первого графического процессора вашей машины, который виден для Tensorflow.
"/job:localhost/replica:0/task:0/device:GPU:1": Полностью квалифицированное название второго графического процессора вашей машины, которая видна Tensorflow.

Если операция TensorFlow имеет реализации CPU и GPU, по умолчанию устройство GPU приоритет при назначении операции. Например,tf.matmulИмеет как ядра процессора, так и графического процессора, и на системе с устройствамиCPU:0иGPU:0,GPU:0Устройство выбрано для запускаtf.matmulЕсли вы явно запросите его запустить на другом устройстве.

Если операция TensorFlow не имеет соответствующей реализации GPU, то операция возвращается к устройству процессора. Например, с тех порtf.castТолько ядро процессора на системе с устройствамиCPU:0иGPU:0,CPU:0Устройство выбрано для запускаtf.cast, даже если попросили запустить наGPU:0устройство.

Расположение устройства ведения журнала

Чтобы узнать, какие устройства назначаются ваши операции и тензоры, положитьtf.debugging.set_log_device_placement(True)как первое заявление вашей программы. Включение регистрации размещения устройств приводит к печати любых тензорных распределений или операций.

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)

Приведенный выше код печатает указаниеMatMulOP был выполнен наGPU:0Полем

Ручное размещение устройства

Если вы хотите, чтобы конкретная операция выполнялась на устройстве по вашему выбору вместо того, что автоматически выбрано для вас, вы можете использоватьwith tf.deviceЧтобы создать контекст устройства, и все операции в этом контексте будут работать на одном и том же назначенном устройстве.

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)

Вы увидите это сейчасaиbназначеныCPU:0Полем Поскольку устройство не было явно указано дляMatMulРабота, время выполнения TensorFlow выберет один на основе работы и доступных устройств (GPU:0В этом примере) и автоматически копировать тензоры между устройствами, если это необходимо.

Ограничение роста памяти графического процессора

По умолчанию, TensorFlow отображает почти всю память графических процессоров всех графических процессоров (с учетомCUDA_VISIBLE_DEVICES) видимо для процесса. Это делается для более эффективного использования относительно драгоценных ресурсов памяти графических процессоров на устройствах путем уменьшения фрагментации памяти. Чтобы ограничить тензорфлоу определенным набором графических процессоров, используйтеtf.config.set_visible_devicesметод

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 распределял только подмножество доступной памяти или только увеличить использование памяти, как это необходимо для процесса. Tensorflow предоставляет два метода для управления этим.

Первый вариант - включить рост памяти, вызываяtf.config.experimental.set_memory_growth, который пытается выделить только столько памяти графических процессоров, сколько необходимо для распределения времени выполнения: она начинает выделять очень мало памяти, и по мере того, как программа запускается и требуется больше памяти графического процессора, область памяти графического процессора расширяется для процесса TensorFlow. Память не выпускается, поскольку она может привести к фрагментации памяти. Чтобы включить рост памяти для конкретного графического процессора, используйте следующий код, прежде чем выделять любые тензоры или выполнять какие -либо операции.

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

Другой способ включить эту опцию - установить переменную средыTF_FORCE_GPU_ALLOW_GROWTHкtrueПолем Эта конфигурация зависит от платформы.

Второй метод - настройка виртуального устройства GPU сtf.config.set_logical_device_configurationи установите жесткий ограничение на общую память, чтобы распределить на графический процессор.

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 по -настоящему связать объем памяти GPU, доступный с процессом TensorFlow. Это обычная практика для местного развития, когда графический процессор передается с другими приложениями, такими как графический интерфейс рабочей станции.

Использование одного графического процессора в системе с несколькими GPU

Если в вашей системе есть более одного графического процессора, GPU с самым низким идентификатором будет выбран по умолчанию. Если вы хотите запустить в другом графическом процессоре, вам нужно будет явно указать предпочтения:

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 получитеRuntimeError: .../device:GPU:2 unknown deviceПолем

Если вы хотите, чтобы TensorFlow автоматически выбрал существующее и поддерживаемое устройство для запуска операций в случае указанного не существует, вы можете позвонитьtf.config.set_soft_device_placement(True)Полем

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)

Используя несколько графических процессоров

Разработка для нескольких графических процессоров позволит модели масштабироваться с дополнительными ресурсами. Если вы можете разработать систему с одним графическим процессором, вы можете смоделировать несколько графических процессоров с виртуальными устройствами. Это обеспечивает легкое тестирование настройки с несколькими GPU, не требуя 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

После того, как есть несколько логических графических процессоров, доступных во время выполнения, вы можете использовать несколько графических процессоров сtf.distribute.Strategyили с ручным размещением.

С`tf.distribute.Strategy`

Лучшая практика использования нескольких графических процессоров - это использоватьtf.distribute.StrategyПолем Вот простой пример:

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 графическом процессоре, разделяя входные данные между ними, также известная как »Параллелизм данных".

Для получения дополнительной информации о стратегиях распространения, ознакомьтесь с руководствомздесьПолем

Ручное размещение

tf.distribute.StrategyРаботает под капюшоном, повторяя вычисления на разных устройствах. Вы можете вручную реализовать репликацию, построив свою модель на каждом графическом процессоре. Например:

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 опубликовано наTensorflowВеб -сайт, эта статья появляется здесь под новым заголовком и имеет лицензию в CC на 4.0. Образцы кода, разделенные по лицензии Apache 2.0.

Оригинал

Как использовать несколько графических процессоров с TensorFlow (не требуется изменений кода)

Обзор контента

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Обзор

Расположение устройства ведения журнала

Ручное размещение устройства

Ограничение роста памяти графического процессора

Использование одного графического процессора в системе с несколькими GPU

Используя несколько графических процессоров

С`tf.distribute.Strategy`

Ручное размещение

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Настраивать

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Расположение устройства ведения журнала

Ручное размещение устройства

Ограничение роста памяти графического процессора

Использование одного графического процессора в системе с несколькими GPU

Используя несколько графических процессоров

Сtf.distribute.Strategy

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С`tf.distribute.Strategy`