.. _performance-howto:
=========================
How to optimize for speed
=========================
The following gives some practical guidelines to help you write efficient
code for the scikit-learn project.
.. note::
While it is always useful to profile your code so as to **check
performance assumptions**, it is also highly recommended
to **review the literature** to ensure that the implemented algorithm
is the state of the art for the task before investing into costly
implementation optimization.
Times and times, hours of efforts invested in optimizing complicated
implementation details have been rendered irrelevant by the subsequent
discovery of simple **algorithmic tricks**, or by using another algorithm
altogether that is better suited to the problem.
The section :ref:`warm-restarts` gives an example of such a trick.
Python, Cython or C/C++?
========================
.. currentmodule:: sklearn
In general, the scikit-learn project emphasizes the **readability** of
the source code to make it easy for the project users to dive into the
source code so as to understand how the algorithm behaves on their data
but also for ease of maintainability (by the developers).
When implementing a new algorithm is thus recommended to **start
implementing it in Python using Numpy and Scipy** by taking care of avoiding
looping code using the vectorized idioms of those libraries. In practice
this means trying to **replace any nested for loops by calls to equivalent
Numpy array methods**. The goal is to avoid the CPU wasting time in the
Python interpreter rather than crunching numbers to fit your statistical
model. It's generally a good idea to consider NumPy and SciPy performance tips:
https://scipy.github.io/old-wiki/pages/PerformanceTips
Sometimes however an algorithm cannot be expressed efficiently in simple
vectorized Numpy code. In this case, the recommended strategy is the
following:
1. **Profile** the Python implementation to find the main bottleneck and
isolate it in a **dedicated module level function**. This function
will be reimplemented as a compiled extension module.
2. If there exists a well maintained BSD or MIT **C/C++** implementation
of the same algorithm that is not too big, you can write a
**Cython wrapper** for it and include a copy of the source code
of the library in the scikit-learn source tree: this strategy is
used for the classes :class:`svm.LinearSVC`, :class:`svm.SVC` and
:class:`linear_model.LogisticRegression` (wrappers for liblinear
and libsvm).
3. Otherwise, write an optimized version of your Python function using
**Cython** directly. This strategy is used
for the :class:`linear_model.ElasticNet` and
:class:`linear_model.SGDClassifier` classes for instance.
4. **Move the Python version of the function in the tests** and use
it to check that the results of the compiled extension are consistent
with the gold standard, easy to debug Python version.
5. Once the code is optimized (not simple bottleneck spottable by
profiling), check whether it is possible to have **coarse grained
parallelism** that is amenable to **multi-processing** by using the
``joblib.Parallel`` class.
When using Cython, use either
.. prompt:: bash $
python setup.py build_ext -i
python setup.py install
to generate C files. You are responsible for adding .c/.cpp extensions along
with build parameters in each submodule ``setup.py``.
C/C++ generated files are embedded in distributed stable packages. The goal is
to make it possible to install scikit-learn stable version
on any machine with Python, Numpy, Scipy and C/C++ compiler.
.. _profiling-python-code:
Profiling Python code
=====================
In order to profile Python code we recommend to write a script that
loads and prepare you data and then use the IPython integrated profiler
for interactively exploring the relevant part for the code.
Suppose we want to profile the Non Negative Matrix Factorization module
of scikit-learn. Let us setup a new IPython session and load the digits
dataset and as in the :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py` example::
In [1]: from sklearn.decomposition import NMF
In [2]: from sklearn.datasets import load_digits
In [3]: X, _ = load_digits(return_X_y=True)
Before starting the profiling session and engaging in tentative
optimization iterations, it is important to measure the total execution
time of the function we want to optimize without any kind of profiler
overhead and save it somewhere for later reference::
In [4]: %timeit NMF(n_components=16, tol=1e-2).fit(X)
1 loops, best of 3: 1.7 s per loop
To have a look at the overall performance profile using the ``%prun``
magic command::
In [5]: %prun -l nmf.py NMF(n_components=16, tol=1e-2).fit(X)
14496 function calls in 1.682 CPU seconds
Ordered by: internal time
List reduced from 90 to 9 due to restriction <'nmf.py'>
ncalls tottime percall cumtime percall filename:lineno(function)
36 0.609 0.017 1.499 0.042 nmf.py:151(_nls_subproblem)
1263 0.157 0.000 0.157 0.000 nmf.py:18(_pos)
1 0.053 0.053 1.681 1.681 nmf.py:352(fit_transform)
673 0.008 0.000 0.057 0.000 nmf.py:28(norm)
1 0.006 0.006 0.047 0.047 nmf.py:42(_initialize_nmf)
36 0.001 0.000 0.010 0.000 nmf.py:36(_sparseness)
30 0.001 0.000 0.001 0.000 nmf.py:23(_neg)
1 0.000 0.000 0.000 0.000 nmf.py:337(__init__)
1 0.000 0.000 1.681 1.681 nmf.py:461(fit)
The ``tottime`` column is the most interesting: it gives to total time spent
executing the code of a given function ignoring the time spent in executing the
sub-functions. The real total time (local code + sub-function calls) is given by
the ``cumtime`` column.
Note the use of the ``-l nmf.py`` that restricts the output to lines that
contains the "nmf.py" string. This is useful to have a quick look at the hotspot
of the nmf Python module it-self ignoring anything else.
Here is the beginning of the output of the same command without the ``-l nmf.py``
filter::
In [5] %prun NMF(n_components=16, tol=1e-2).fit(X)
16159 function calls in 1.840 CPU seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
2833 0.653 0.000 0.653 0.000 {numpy.core._dotblas.dot}
46 0.651 0.014 1.636 0.036 nmf.py:151(_nls_subproblem)
1397 0.171 0.000 0.171 0.000 nmf.py:18(_pos)
2780 0.167 0.000 0.167 0.000 {method 'sum' of 'numpy.ndarray' objects}
1 0.064 0.064 1.840 1.840 nmf.py:352(fit_transform)
1542 0.043 0.000 0.043 0.000 {method 'flatten' of 'numpy.ndarray' objects}
337 0.019 0.000 0.019 0.000 {method 'all' of 'numpy.ndarray' objects}
2734 0.011 0.000 0.181 0.000 fromnumeric.py:1185(sum)
2 0.010 0.005 0.010 0.005 {numpy.linalg.lapack_lite.dgesdd}
748 0.009 0.000 0.065 0.000 nmf.py:28(norm)
...
The above results show that the execution is largely dominated by
dot products operations (delegated to blas). Hence there is probably
no huge gain to expect by rewriting this code in Cython or C/C++: in
this case out of the 1.7s total execution time, almost 0.7s are spent
in compiled code we can consider optimal. By rewriting the rest of the
Python code and assuming we could achieve a 1000% boost on this portion
(which is highly unlikely given the shallowness of the Python loops),
we would not gain more than a 2.4x speed-up globally.
Hence major improvements can only be achieved by **algorithmic
improvements** in this particular example (e.g. trying to find operation
that are both costly and useless to avoid computing then rather than
trying to optimize their implementation).
It is however still interesting to check what's happening inside the
``_nls_subproblem`` function which is the hotspot if we only consider
Python code: it takes around 100% of the accumulated time of the module. In
order to better understand the profile of this specific function, let
us install ``line_profiler`` and wire it to IPython:
.. prompt:: bash $
pip install line_profiler
- **Under IPython 0.13+**, first create a configuration profile:
.. prompt:: bash $
ipython profile create
Then register the line_profiler extension in
``~/.ipython/profile_default/ipython_config.py``::
c.TerminalIPythonApp.extensions.append('line_profiler')
c.InteractiveShellApp.extensions.append('line_profiler')
This will register the ``%lprun`` magic command in the IPython terminal application and the other frontends such as qtconsole and notebook.
Now restart IPython and let us use this new toy::
In [1]: from sklearn.datasets import load_digits
In [2]: from sklearn.decomposition import NMF
... : from sklearn.decomposition._nmf import _nls_subproblem
In [3]: X, _ = load_digits(return_X_y=True)
In [4]: %lprun -f _nls_subproblem NMF(n_components=16, tol=1e-2).fit(X)
Timer unit: 1e-06 s
File: sklearn/decomposition/nmf.py
Function: _nls_subproblem at line 137
Total time: 1.73153 s
Line # Hits Time Per Hit % Time Line Contents
==============================================================
137 def _nls_subproblem(V, W, H_init, tol, max_iter):
138 """Non-negative least square solver
...
170 """
171 48 5863 122.1 0.3 if (H_init < 0).any():
172 raise ValueError("Negative values in H_init passed to NLS solver.")
173
174 48 139 2.9 0.0 H = H_init
175 48 112141 2336.3 5.8 WtV = np.dot(W.T, V)
176 48 16144 336.3 0.8 WtW = np.dot(W.T, W)
177
178 # values justified in the paper
179 48 144 3.0 0.0 alpha = 1
180 48 113 2.4 0.0 beta = 0.1
181 638 1880 2.9 0.1 for n_iter in range(1, max_iter + 1):
182 638 195133 305.9 10.2 grad = np.dot(WtW, H) - WtV
183 638 495761 777.1 25.9 proj_gradient = norm(grad[np.logical_or(grad < 0, H > 0)])
184 638 2449 3.8 0.1 if proj_gradient < tol:
185 48 130 2.7 0.0 break
186
187 1474 4474 3.0 0.2 for inner_iter in range(1, 20):
188 1474 83833 56.9 4.4 Hn = H - alpha * grad
189 # Hn = np.where(Hn > 0, Hn, 0)
190 1474 194239 131.8 10.1 Hn = _pos(Hn)
191 1474 48858 33.1 2.5 d = Hn - H
192 1474 150407 102.0 7.8 gradd = np.sum(grad * d)
193 1474 515390 349.7 26.9 dQd = np.sum(np.dot(WtW, d) * d)
...
By looking at the top values of the ``% Time`` column it is really easy to
pin-point the most expensive expressions that would deserve additional care.
Memory usage profiling
======================
You can analyze in detail the memory usage of any Python code with the help of
`memory_profiler <https://pypi.org/project/memory_profiler/>`_. First,
install the latest version:
.. prompt:: bash $
pip install -U memory_profiler
Then, setup the magics in a manner similar to ``line_profiler``.
- **Under IPython 0.11+**, first create a configuration profile:
.. prompt:: bash $
ipython profile create
Then register the extension in
``~/.ipython/profile_default/ipython_config.py``
alongside the line profiler::
c.TerminalIPythonApp.extensions.append('memory_profiler')
c.InteractiveShellApp.extensions.append('memory_profiler')
This will register the ``%memit`` and ``%mprun`` magic commands in the
IPython terminal application and the other frontends such as qtconsole and notebook.
``%mprun`` is useful to examine, line-by-line, the memory usage of key
functions in your program. It is very similar to ``%lprun``, discussed in the
previous section. For example, from the ``memory_profiler`` ``examples``
directory::
In [1] from example import my_func
In [2] %mprun -f my_func my_func()
Filename: example.py
Line # Mem usage Increment Line Contents
==============================================
3 @profile
4 5.97 MB 0.00 MB def my_func():
5 13.61 MB 7.64 MB a = [1] * (10 ** 6)
6 166.20 MB 152.59 MB b = [2] * (2 * 10 ** 7)
7 13.61 MB -152.59 MB del b
8 13.61 MB 0.00 MB return a
Another useful magic that ``memory_profiler`` defines is ``%memit``, which is
analogous to ``%timeit``. It can be used as follows::
In [1]: import numpy as np
In [2]: %memit np.zeros(1e7)
maximum of 3: 76.402344 MB per loop
For more details, see the docstrings of the magics, using ``%memit?`` and
``%mprun?``.
Performance tips for the Cython developer
=========================================
If profiling of the Python code reveals that the Python interpreter
overhead is larger by one order of magnitude or more than the cost of the
actual numerical computation (e.g. ``for`` loops over vector components,
nested evaluation of conditional expression, scalar arithmetic...), it
is probably adequate to extract the hotspot portion of the code as a
standalone function in a ``.pyx`` file, add static type declarations and
then use Cython to generate a C program suitable to be compiled as a
Python extension module.
The official documentation available at http://docs.cython.org/ contains
a tutorial and reference guide for developing such a module. In the
following we will just highlight a couple of tricks that we found
important in practice on the existing cython codebase in the scikit-learn
project.
TODO: html report, type declarations, bound checks, division by zero checks,
memory alignment, direct blas calls...
- https://www.youtube.com/watch?v=gMvkiQ-gOW8
- https://conference.scipy.org/proceedings/SciPy2009/paper_1/
- https://conference.scipy.org/proceedings/SciPy2009/paper_2/
Using OpenMP
------------
Since scikit-learn can be built without OpenMP, it's necessary to protect each
direct call to OpenMP.
There are some helpers in
`sklearn/utils/_openmp_helpers.pyx <https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/utils/_openmp_helpers.pyx>`_
that you can reuse for the main useful functionalities and already have the
necessary protection to be built without OpenMP.
If the helpers are not enough, you need to protect your OpenMP code using the
following syntax::
# importing OpenMP
IF SKLEARN_OPENMP_PARALLELISM_ENABLED:
cimport openmp
# calling OpenMP
IF SKLEARN_OPENMP_PARALLELISM_ENABLED:
max_threads = openmp.omp_get_max_threads()
ELSE:
max_threads = 1
.. note::
Protecting the parallel loop, ``prange``, is already done by cython.
.. _profiling-compiled-extension:
Profiling compiled extensions
=============================
When working with compiled extensions (written in C/C++ with a wrapper or
directly as Cython extension), the default Python profiler is useless:
we need a dedicated tool to introspect what's happening inside the
compiled extension it-self.
Using yep and gperftools
------------------------
Easy profiling without special compilation options use yep:
- https://pypi.org/project/yep/
- https://fa.bianp.net/blog/2011/a-profiler-for-python-extensions
Using gprof
-----------
In order to profile compiled Python extensions one could use ``gprof``
after having recompiled the project with ``gcc -pg`` and using the
``python-dbg`` variant of the interpreter on debian / ubuntu: however
this approach requires to also have ``numpy`` and ``scipy`` recompiled
with ``-pg`` which is rather complicated to get working.
Fortunately there exist two alternative profilers that don't require you to
recompile everything.
Using valgrind / callgrind / kcachegrind
----------------------------------------
kcachegrind
~~~~~~~~~~~
``yep`` can be used to create a profiling report.
``kcachegrind`` provides a graphical environment to visualize this report:
.. prompt:: bash $
# Run yep to profile some python script
python -m yep -c my_file.py
.. prompt:: bash $
# open my_file.py.callgrin with kcachegrind
kcachegrind my_file.py.prof
.. note::
``yep`` can be executed with the argument ``--lines`` or ``-l`` to compile
a profiling report 'line by line'.
Multi-core parallelism using ``joblib.Parallel``
================================================
See `joblib documentation <https://joblib.readthedocs.io>`_
.. _warm-restarts:
A simple algorithmic trick: warm restarts
=========================================
See the glossary entry for :term:`warm_start`