chainer.function_hooks.CupyMemoryProfileHook¶
- class chainer.function_hooks.CupyMemoryProfileHook[source]¶
Function hook for measuring memory usage of functions in cupy memory pool.
Example
Code example:
from chainer.function_hooks import CupyMemoryProfileHook hook = CupyMemoryProfileHook() with hook: trainer.run() hook.print_report()
Output example:
FunctionName UsedBytes AcquiredBytes Occurrence LinearFunction 5.16GB 179.98MB 3900 ReLU 0.99GB 458.97MB 2600 SoftmaxCrossEntropy 0.01GB 5.08MB 1300 Accuracy 0.00GB 0.35MB 700
where FunctionName is the name of function that calls the hook, and UsedBytes is the memory bytes the function used from cupy memory pool, and AcquiredBytes is the actual memory bytes the cupy memory pool acquired from GPU device on the function call, and Occurrence is the number of calls.
- Variables
~CupyMemoryProfileHook.call_history – List of measurement results. It consists of the name of the function that calls this hook, the memory bytes the function used from cupy memory pool, and the memory bytes the cupy memory pool acquired from GPU device on the function call.
Methods
- added(function=None)[source]¶
Callback function invoked when the function hook is registered
- Parameters
function (FunctionNode) – Function object to which the function hook is added.
None
if the function hook is registered globally.
- backward_postprocess(function, in_data, out_grad)[source]¶
Callback function invoked after backward propagation.
- Parameters
function (FunctionNode) – Function object to which the function hook is registered.
in_data (tuple of N-dimensional array) – Input of forward propagation.
out_grad (tuple of N-dimensional array) – Gradient data of backward propagation.
- backward_preprocess(function, in_data, out_grad)[source]¶
Callback function invoked before backward propagation.
- Parameters
function (FunctionNode) – Function object to which the function hook is registered.
in_data (tuple of N-dimensional array) – Input data of forward propagation.
out_grad (tuple of N-dimensional array) – Gradient data of backward propagation.
- deleted(function=None)[source]¶
Callback function invoked when the function hook is unregistered
- Parameters
function (FunctionNode) – Function object from which the function hook is deleted.
None
if the function hook was registered globally.
- forward_postprocess(function, in_data)[source]¶
Callback function invoked after forward propagation.
- Parameters
function (FunctionNode) – Function object to which the function hook is registered.
in_data (tuple of N-dimensional array) – Input data of forward propagation.
- forward_preprocess(function, in_data)[source]¶
Callback function invoked before forward propagation.
- Parameters
function (FunctionNode) – Function object to which the function hook is registered.
in_data (tuple of N-dimensional array) – Input data of forward propagation.
- print_report(unit='auto', file=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>)[source]¶
Prints a summary report of memory profiling in functions.
- Parameters
unit (str) – Supplementary units used for used memories. B, KB, MB, GB, TB, PB, EB, ZB, auto`(default) and `auto_foreach are supported. If auto, units of memories are aligned to the largest values of ‘used_bytes’ and ‘acquired_bytes’. If auto_foreach, units of memories are adjusted for each element.
- summary()[source]¶
Returns a summary of memory profiling in functions.
- Returns
A summarized dictionary whose keys are function names and values are dictionaries of
used_bytes
,acquired_bytes
, andoccurrrence
.
- __eq__(value, /)¶
Return self==value.
- __ne__(value, /)¶
Return self!=value.
- __lt__(value, /)¶
Return self<value.
- __le__(value, /)¶
Return self<=value.
- __gt__(value, /)¶
Return self>value.
- __ge__(value, /)¶
Return self>=value.
Attributes
- name = 'CupyMemoryProfileHook'¶