tf.debugging.set_log_device_placement
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Turns logging for device placement decisions on or off.
tf.debugging.set_log_device_placement(
enabled
)
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.device
scope, but there are exceptions, for example operations on a tf.Variable
which 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.
Args |
enabled
|
Whether to enabled device placement logging.
|
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Last updated 2023-03-17 UTC.
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