tf.config.experimental.get_memory_info
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Get memory info for the chosen device, as a dict.
tf.config.experimental.get_memory_info(
device
)
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.
More keys may be added in the future, including device-specific keys.
Currently raises an exception for the CPU.
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.
Returns |
|---|
A dict with keys 'current' and 'peak', specifying the current and peak
memory usage respectively.
|
Raises |
|---|
ValueError
|
Non-existent or CPU device specified.
|
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Last updated 2021-05-14 UTC.
[null,null,["Last updated 2021-05-14 UTC."],[],[],null,["# tf.config.experimental.get_memory_info\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.5.0/tensorflow/python/framework/config.py#L526-L565) |\n\nGet memory info for the chosen device, as a dict.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.config.experimental.get_memory_info`](https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_memory_info)\n\n\u003cbr /\u003e\n\n tf.config.experimental.get_memory_info(\n device\n )\n\nThis function returns a dict containing information about the device's memory\nusage. For example: \n\n if tf.config.list_physical_devices('GPU'):\n # Returns a dict in the form {'current': \u003ccurrent mem usage\u003e,\n # 'peak': \u003cpeak mem usage\u003e}\n tf.config.experimental.get_memory_info('GPU:0')\n\nCurrently returns the following keys:\n`'current'`: The current memory used by the device, in bytes.\n`'peak'`: The peak memory used by the device across the run of the program,\nin bytes.\n\nMore keys may be added in the future, including device-specific keys.\n\nCurrently raises an exception for the CPU.\n\nFor GPUs, TensorFlow will allocate all the memory by default, unless changed\nwith [`tf.config.experimental.set_memory_growth`](../../../tf/config/experimental/set_memory_growth). The dict specifies only the\ncurrent and peak memory that TensorFlow is actually using, not the memory that\nTensorFlow has allocated on the GPU.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------|------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `device` | Device string to get the memory information for, e.g. `\"GPU:0\"`. See \u003chttps://www.tensorflow.org/api_docs/python/tf/device\u003e for specifying device strings. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A dict with keys `'current'` and `'peak'`, specifying the current and peak memory usage respectively. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|---------------------------------------|\n| `ValueError` | Non-existent or CPU device specified. |\n\n\u003cbr /\u003e"]]
View source on GitHub