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tf.config.experimental.get_memory_info

Get memory info for the chosen device, as a dict.

This function returns a dict containing information about the device's memory usage. For example:

if tf.config.list_physical_devices('GPU'):
  # Returns a dict in the form {'current': <current mem usage>,
  #                             'peak': <peak mem usage>}
  tf.config.experimental.get_memory_info('GPU:0')

Currently returns the following keys:

  • 'current': The current memory used by the device, in bytes.
  • 'peak': The peak memory used by the device across the run of the program, in bytes. Can be reset with tf.config.experimental.reset_memory_stats.

More keys may be added in the future, including device-specific keys.

Currently only supports GPU and TPU. If called on a CPU device, an exception will be raised.

For GPUs, TensorFlow will allocate all the memory by default, unless changed with tf.config.experimental.set_memory_growth. The dict specifies only the current and peak memory that TensorFlow is actually using, not the memory that TensorFlow has allocated on the GPU.

device Device string to get the memory information for, e.g. "GPU:0", "TPU:0". See https://www.tensorflow.org/api_docs/python/tf/device for specifying device strings.

A dict with keys 'current' and 'peak', specifying the current and peak memory usage respectively.

ValueError No device found with the device name, like '"nonexistent"'.
ValueError Invalid device name, like '"GPU"', '"CPU:GPU"', '"CPU:"'.
ValueError Multiple devices matched with the device name.
ValueError Memory statistics not tracked, like '"CPU:0"'.