How to optimize for speed

The following gives some practical guidelines to help you write efficient code for the scikit-learn project.


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 A sample algorithmic trick: warm restarts gives an example of such a trick.

Python, Cython or C/C++?

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:

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 svm.LinearSVC, svm.SVC and 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 linear_model.ElasticNet and 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

$ python build_ext -i $ python install

to generate C files. You are responsible for adding .c/.cpp extensions along with build parameters in each submodule

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

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 Recognizing hand-written digits example:

In [1]: from sklearn.decomposition import NMF

In [2]: from sklearn.datasets import load_digits

In [3]: X = load_digits().data

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(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 <''>

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
       36    0.609    0.017    1.499    0.042
     1263    0.157    0.000    0.157    0.000
        1    0.053    0.053    1.681    1.681
      673    0.008    0.000    0.057    0.000
        1    0.006    0.006    0.047    0.047
       36    0.001    0.000    0.010    0.000
       30    0.001    0.000    0.001    0.000
        1    0.000    0.000    0.000    0.000
        1    0.000    0.000    1.681    1.681

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 that restricts the output to lines that contains the “” 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 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 {}
       46    0.651    0.014    1.636    0.036
     1397    0.171    0.000    0.171    0.000
     2780    0.167    0.000    0.167    0.000 {method 'sum' of 'numpy.ndarray' objects}
        1    0.064    0.064    1.840    1.840
     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
        2    0.010    0.005    0.010    0.005 {numpy.linalg.lapack_lite.dgesdd}
      748    0.009    0.000    0.065    0.000

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:

$ pip install line_profiler
  • Under IPython 0.13+, first create a configuration profile:

    $ ipython profile create

    Then register the line_profiler extension in ~/.ipython/profile_default/


    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.nmf import _nls_subproblem, NMF

In [3]: X = load_digits().data

In [4]: %lprun -f _nls_subproblem NMF(n_components=16, tol=1e-2).fit(X)
Timer unit: 1e-06 s

File: sklearn/decomposition/
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.")
   174        48          139      2.9      0.0      H = H_init
   175        48       112141   2336.3      5.8      WtV =, V)
   176        48        16144    336.3      0.8      WtW =, W)
   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 xrange(1, max_iter + 1):
   182       638       195133    305.9     10.2          grad =, 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
   187      1474         4474      3.0      0.2          for inner_iter in xrange(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(, 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. First, install the latest version:

$ 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:

    $ ipython profile create

    Then register the extension in ~/.ipython/profile_default/ alongside the line 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()

Line #    Mem usage  Increment   Line Contents
     3                           @profile
     4      5.97 MB    0.00 MB   def my_func():
     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 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…

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 google-perftools

Easy profiling without special compilation options use yep:


google-perftools provides a nice ‘line by line’ report mode that can be triggered with the --lines option. However this does not seem to work correctly at the time of writing. This issue can be tracked on the project issue tracker.

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


Multi-core parallelism using joblib.Parallel

TODO: give a simple teaser example here.

Checkout the official joblib documentation:

A sample algorithmic trick: warm restarts

See the glossary entry for warm_start