This method replace the old ProfileMode. Do not use ProfileMode
This method replaces the old `ProfileMode`. Do not use `ProfileMode`
anymore.
Besides checking for errors, another important task is to profile your
code in terms of speed and/or memory usage.
Besides checking for errors, another important task is to profile your code in
terms of speed and/or memory usage. You can profile your functions using either
of the following two options:
You can profile your
functions using either of the following two options:
1. Use Aesara flag :attr:`config.profile` to enable profiling.
1. Use the Aesara flag :attr:`config.profile` to enable profiling.
- To enable the memory profiler use the Aesara flag:
:attr:`config.profile_memory` in addition to :attr:`config.profile`.
- Moreover, to enable the profiling of Aesara optimization phase,
- Moreover, to enable the profiling of Aesara optimization phases,
use the Aesara flag: :attr:`config.profile_optimizer` in addition
to :attr:`config.profile`.
- You can also use the Aesara flags :attr:`profiling__n_apply`,
:attr:`profiling__n_ops` and :attr:`profiling__min_memory_size`
to modify the quantity of information printed.
2. Pass the argument :attr:`profile=True` to the function :func:`aesara.function <function.function>`. And then call :attr:`f.profile.summary()` for a single function.
2. Pass the argument :attr:`profile=True` to the function :func:`aesara.function
<function.function>` and then call :attr:`f.profile.summary()` for a single
function.
- Use this option when you want to profile not all the
functions but one or more specific function(s).
- You can also combine the profile of many functions:
functions but only one or more specific function(s).
- You can also combine the profile results of many functions:
.. doctest::
:hide:
...
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@@ -43,48 +42,45 @@ functions using either of the following two options:
The profiler will output one profile per Aesara function and profile
that is the sum of the printed profiles. Each profile contains 4
sections: global info, class info, Ops info and Apply node info.
The profiler will output one profile per Aesara function and produce a profile
that is the sum of the printed profiles. Each profile contains four sections:
global info, class info, `Op`\s info and `Apply` node info.
In the global section, the "Message" is the name of the Aesara
function. aesara.function() has an optional parameter ``name`` that
defaults to None. Change it to something else to help you profile many
function. :func:`aesara.function` has an optional parameter ``name`` that
defaults to ``None``. Change it to something else to help profile many
Aesara functions. In that section, we also see the number of times the
function was called (1) and the total time spent in all those
calls. The time spent in :meth:`Function.vm.__call__` and in thunks is useful
to understand Aesara's overhead.
Also, we see the time spent in the two parts of the compilation
process: optimization (modify the graph to make it more stable/faster)
and the linking (compile c code and make the Python callable returned
by function).
Also, we see the time spent in the two parts of the compilation process:
optimization (i.e. modifying the graph to make it more stable/faster) and the
linking (i.e. compile C code and make the Python callable returned by
:func:`aesara.function`).
The class, Ops and Apply nodes sections are the same information:
information about the Apply node that ran. The Ops section takes the
information from the Apply section and merge the Apply nodes that have
exactly the same op. If two Apply nodes in the graph have two Ops that
compare equal, they will be merged. Some Ops like Elemwise, will not
compare equal, if their parameters differ (the scalar being
executed). So the class section will merge more Apply nodes then the
Ops section.
The class, `Op`\s and `Apply` nodes sections have the same information: i.e.
information about the `Apply` nodes that ran. The `Op`\s section takes the
information from the `Apply` section and merges it with the `Apply` nodes that have
exactly the same `Op`. If two `Apply` nodes in the graph have two `Op`\s that
compare equal, they will be merged. Some `Op`\s, like `Elemwise`, will not
compare equal if their parameters differ, so the class section will merge more
`Apply` nodes than the `Op`\s section.
Note that the profile also shows which Ops were running a c implementation.
Note that the profile also shows which `Op`\s were run with C
implementation.
Developers wishing to optimize the performance of their graph should
focus on the worst offending Ops and Apply nodes – either by optimizing
focus on the worst offending `Op`\s and `Apply` nodes--either by optimizing
an implementation, providing a missing C implementation, or by writing
a graph optimization that eliminates the offending Op altogether.
You should strongly consider emailing one of our lists about your
issue before spending too much time on this.
a graph optimization that eliminates the offending `Op` altogether.
Here is an example output when we disable some Aesara optimizations to
give you a better idea of the difference between sections. With all
optimizations enabled, there would be only one op left in the graph.
Here is some example output when some Aesara optimizations are disabled. With
all optimizations enabled, there would be only one `Op` left in the graph.
