Како користити више ГПУ-а са ТенсорФлов-ом (не захтевају се промене кода)

Sadržaj Pregled Постављање Преглед Логирање уређаја Ручно постављање уређаја Ограничавање раста ГПУ меморије Користите једну ГПУ на мулти-ГПУ систему Коришћење више ГПУ TensorFlow код, и Модели ће транспарентно радити на једном ГПУ-у без потребе за промјенама кода. tf.keras Napomena: Koristite tačku tf.config.list_physical_devices ('GPU') da biste potvrdili da TensorFlow koristi GPU. Napomena: Koristite tačku tf.config.list_physical_devices ('GPU') da biste potvrdili da TensorFlow koristi GPU. Најједноставнији начин за покретање на више ГПУ-ја, на једној или више машина, јесте коришћење стратегија дистрибуције. Овај водич је за кориснике који су пробали ове приступе и утврдили да им је потребна фина контрола о томе како ТенсорФлов користи ГПУ. Водич . Optimizacija performansi TensorFlow GPU-ja Постављање Уверите се да имате најновију TensorFlow gpu верзију инсталиран. import tensorflow as tf print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) 2024-08-15 02:53:40.344028: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-08-15 02:53:40.365851: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-08-15 02:53:40.372242: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered Num GPUs Available: 4 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1723690422.944962 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.948934 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.952655 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.955880 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.967120 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.970596 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.973980 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.976984 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.979869 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.983344 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.986754 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723690422.989690 162671 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 Преглед TensorFlow подржава покретање обрачуна на различитим типовима уређаја, укључујући ЦПУ и ГПУ. Они су представљени идентификаторима низова, на пример: "/device:CPU:0": Процесор ваше машине "/GPU:0": Краткоручна нотација за прву ГПУ ваше машине која је видљива за TensorFlow. "/job:localhost/replica:0/task:0/device:GPU:1": Потпуно квалификовано име другог ГПУ ваше машине који је видљив за TensorFlow. Ako TensorFlow operacija ima i CPU i GPU implementacije, podrazumevano, GPU uređaj je prioritet kada je operacija dodeljena. има и ЦПУ и ГПУ језгре и на систему са уређајима и , о уређај је изабран за покретање осим ако експлицитно затражите да га покренете на другом уређају. tf.matmul CPU:0 GPU:0 GPU:0 tf.matmul Ако операција TensorFlow нема одговарајућу имплементацију ГПУ-а, онда се операција враћа на уређај ЦПУ. само има ЦПУ језгро, на систему са уређајима и , о уређај је изабран за покретање Чак и ако се тражи да се трчи на уређаја . tf.cast CPU:0 GPU:0 CPU:0 tf.cast GPU:0 Логирање уређаја Да бисте сазнали на које уређаје су ваше операције и тензори додељени, ставите Omogućavanje izveštavanja o lokaciji uređaja izaziva štampanje bilo kakvih Tensor alokacija ili operacija. 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) Горе наведени код ће штампати индикацију о Op je izvršen na . MatMul GPU:0 Ручно постављање уређаја Ако желите да се одређена операција покрене на уређају по вашем избору уместо онога што је аутоматски изабрано за вас, можете користити Da biste kreirali kontekst uređaja, sve operacije u tom kontekstu će se izvesti na istom imenovanom uređaju. 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) Видећете то сада и Oni su dodeljeni Пошто уређај није експлицитно назначен за операција, ТензорФлов ће изабрати један на основу операције и доступних уређаја ( у овом примеру) и аутоматски копирати тензоре између уређаја ако је потребно. a b CPU:0 MatMul GPU:0 Ограничавање раста ГПУ меморије Подразумевано, TensorFlow мапира скоро сву GPU меморију свих GPU-а (под условом да Ово се ради како би се ефикасније искористили релативно драгоцени ресурси меморије ГПУ-а на уређајима тако што ће се смањити фрагментација меморије. Метод је . 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 U nekim slučajevima, poželjno je da proces dodeli samo podskupinu dostupne memorije, ili da samo poveća upotrebu memorije po potrebi procesa. Прва опција је укључивање раста меморије позивањем , који покушава да издвоји само онолико ГПУ меморије колико је потребно за расподелу покретног времена: почиње да издвоји врло мало меморије, а како програм ради и више ГПУ меморије је потребно, ГПУ меморијска област се проширује за процес ТенсорФлоу. 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 Други начин да омогућите ову опцију је да подесите променљиву животну средину два Ова конфигурација је специфична за платформу. TF_FORCE_GPU_ALLOW_GROWTH true Други метод је конфигурисање виртуелног ГПУ уређаја са и поставите тврду границу за укупну меморију коју ћете издвојити на ГПУ. 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 Ovo je korisno ako želite da stvarno ograničite količinu GPU memorije dostupne na proces TensorFlow.To je uobičajena praksa za lokalni razvoj kada se GPU deli sa drugim aplikacijama kao što je GUI radne stanice. Користите једну ГПУ на мулти-ГПУ систему Ако имате више од једног ГПУ-а у вашем систему, ГПУ са најнижим ИД-ом ће бити изабран подразумевано. 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 Ako uređaj koji ste naveli ne postoji, dobićete • : . RuntimeError .../device:GPU:2 unknown device Ако желите да TensorFlow аутоматски изабере постојећи и подржани уређај за покретање операција у случају да наведени не постоји, можете позвати . 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) Коришћење више ГПУ-а Развијање за више ГПУ-ја ће омогућити да се модел скалира са додатним ресурсима. Ако се развијате на систему са једним ГПУ-ом, можете симулирати више ГПУ-ја са виртуелним уређајима. Ово омогућава једноставно тестирање мулти-ГПУ подешавања без потребе за додатним ресурсима. 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 Када је више логичких ГПУ-ја доступно за време покретања, можете користити више ГПУ-ја са Или са ручним постављањем. tf.distribute.Strategy са tf.distribute.Strategy tf.distribute.Strategija Најбоља пракса за коришћење више ГПУ-а је коришћење Ево једноставног примера: 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 Овај програм ће покренути копију вашег модела на сваком ГПУ-у, раздвајајући улазне податке између њих, такође познате као " » » Паралелност података За више информација о стратегијама дистрибуције, погледајте водич . ovde Ручно постављање radi ispod poklopca tako što replikuje računanje preko uređaja. Replikaciju možete ručno implementirati tako što ćete izgraditi svoj model na svakoj GPU. Na primer: 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) Првобитно објављен на сајту ТенсорФлоу, овај чланак се овде појављује под новим насловом и лиценциран је под ЦЦ БИ 4.0. Првобитно објављен на сајту ТенсорФлоу, овај чланак се овде појављује под новим насловом и лиценциран је под ЦЦ БИ 4.0. TensorFlow TensorFlow