tf.debugging.set_log_device_placement  |  TensorFlow v2.16.1 (original) (raw)

tf.debugging.set_log_device_placement

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Turns logging for device placement decisions on or off.

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Compat aliases for migration

SeeMigration guide for more details.

tf.compat.v1.debugging.set_log_device_placement

tf.debugging.set_log_device_placement(
    enabled
)

Used in the notebooks

Operations execute on a particular device, producing and consuming tensors on that device. This may change the performance of the operation or require TensorFlow to copy data to or from an accelerator, so knowing where operations execute is useful for debugging performance issues.

For more advanced profiling, use the TensorFlow profiler.

Device placement for operations is typically controlled by a tf.devicescope, but there are exceptions, for example operations on a tf.Variablewhich follow the initial placement of the variable. Turning off soft device placement (with tf.config.set_soft_device_placement) provides more explicit control.

tf.debugging.set_log_device_placement(True) tf.ones([]) # [...] op Fill in device /job:localhost/replica:0/task:0/device:GPU:0 with tf.device("CPU"): tf.ones([]) # [...] op Fill in device /job:localhost/replica:0/task:0/device:CPU:0 tf.debugging.set_log_device_placement(False)

Turning on tf.debugging.set_log_device_placement also logs the placement of ops inside tf.function when the function is called.