Python can be used to write and test code quickly because it is an interpreted language that types dynamically. However, these are also the reasons it is slow when performing simple tasks repeatedly, for example in loops.
When developing code, there can often be trade-offs between different implementations. However, at the beginning of the development of an algorithm, it is usually counterproductive to worry about the efficiency of the code.
«We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.»[1]
| [1] | Donald Knuth, founder of Literate Programming, in Computer Programming as an Art (1974) |
.. seealso::
* `Speed up your data science and scientific computing code
<https://pythonspeed.com/datascience/>`_
In the following, I will provide examples of the k-means algorithm algorithm, which is used to form a predefined number of clusters from a set of objects. This can be achieved using MacQueen’s algorithm in the following three steps:
- Choose the first :samp:`k` elements as cluster centres
- Assign each new element to the cluster with the least increase in variance.
- Update the cluster centre
Steps 2 and 3 are repeated until the assignments no longer change.
A possible implementation with pure Python could look like this:
.. literalinclude:: py_kmeans.py :caption: py_kmeans.py :name: py_kmeans.py :lines: 6-
We can create sample data with:
from sklearn.datasets import make_blobs
points, labels_true = make_blobs(
n_samples=1000, centers=3, random_state=0, cluster_std=0.60
)And finally, we can perform the calculation with:
kmeans(points, 10)Once you have worked with your code, it can be useful to examine its efficiency more closely. :doc:`cProfile <tracing>`, :doc:`ipython-profiler`, :doc:`scalene`, :doc:`tprof` or :doc:`memray` can be used for this. So far, I usually carry out the following steps:
:doc:`cProfile <tracing>`, :doc:`ipython-profiler`, :doc:`scalene` or :doc:`tprof` can be used for this. So far, I usually carry out the following steps:
- I profile the entire programme with :doc:`cProfile <tracing>` or py-spy to find slow functions.
- If necessary, I can use the line_profiler to identify the slow sections within the function
- If the slow function is computationally intensive, I try one of the following optimisations; however, if the application is data-intensive (dictionaries, strings, I/O), I take a closer look at the architecture.
- Then I optimise a slow function.
- Finally, I create a new profile and filter out the result of my optimised version so that I can compare the results.
.. versionadded:: Python3.15
:pep:`799` will provide a special profiling module that organises the
profiling tools integrated in Python under a uniform namespace. This module
contains:
:mod:`profiling.tracing`
deterministic function call tracing, which has been moved from
:doc:`cProfile <tracing>`.
:mod:`profiling.sampling`
the new statistical sampling profiler :doc:`tachyon`.
.. seealso::
* `airspeed velocity (asv) <https://asv.readthedocs.io/en/stable/>`_
is a tool for benchmarking Python packages during their lifetime. Runtime,
memory consumption and even user-defined values can be recorded and
displayed in an interactive web frontend.
* `Python Speed Center <https://speed.python.org/>`_
* `Tracing the Python GIL
<https://www.maartenbreddels.com/perf/jupyter/python/tracing/gil/2021/01/14/Tracing-the-Python-GIL.html>`_
.. toctree::
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:maxdepth: 0
tracing
ipython-profiler.ipynb
scalene.ipynb
tprof
memray
tachyon
You should not try to reinvent the wheel: If there are existing implementations, you should use them. There are even two implementations for the k-means algorithm:
-
from sklearn.cluster import KMeans KMeans(10).fit_predict(points)
-
from dask_ml.cluster import KMeans KMeans(10).fit(points).predict(points)
The best that could be said against these existing solutions is that they could create a considerable overhead in your project if you are not already using scikit-learn or Dask-ML elsewhere. In the following, I will therefore show you further possibilities to optimise your own code.
Then you can use :doc:`perflint` to search your code for the most common performance anti-patterns in Python.
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perflint
.. seealso:: * `Effective Python <https://effectivepython.com>`_
:doc:`../workspace/numpy/index` moves repetitive operations into a statically typed compiled layer, combining the fast development time of Python with the fast execution time of C.
| Version | Spectral-norm | vs 3.14x |
|---|---|---|
| CPython 3.14 – Basis | 14,046ms | |
| NumPy | 27ms | 520x |
You may be able to use :doc:`../workspace/numpy/ufunc`, :doc:`vectorisation <../workspace/numpy/vectorisation>`, :doc:`../workspace/numpy/indexing-slicing` in various combinations to move repetitive operations into compiled code and thus avoid slow loops, for example:
.. literalinclude:: np_kmeans.py :caption: np_kmeans.py :name: np_kmeans.py :lines: 5-12
The advantages of NumPy are that the Python overhead only occurs per array and not per array element. However, because NumPy uses a specific language for array operations, it also requires a different mindset when writing code. Finally, the batch operations can also lead to excessive memory consumption.
.. seealso::
* `Speeding up NumPy with parallelism
<https://pythonspeed.com/articles/numpy-parallelism/>`_ by Itamar
Turner-Trauring
- :doc:`../workspace/pandas/index`
- for SQL-like :doc:`../workspace/pandas/group-operations` and
- :doc:`../workspace/pandas/aggregation`.
This way you can also bypass the loop in the
compute_centersmethod:.. literalinclude:: pd_kmeans.py :caption: pd_kmeans.py :name: pd_kmeans.py :lines: 5-8, 16-19
- scipy.spatial
for spatial queries like distances, nearest neighbours, k-Means :abbr:`etc (et cetera)`.
Our
find_labelsmethod can then be written more specifically:.. literalinclude:: sp_kmeans.py :caption: sp_kmeans.py :name: sp_kmeans.py :lines: 5-13
- scipy.sparse
- sparse matrices for 2-dimensional structured data.
- Sparse
- for N-diemensional structured data.
- scipy.sparse.csgraph
- for graph-like problems, for example searching for shortest paths.
- Xarray
- for grouping across multiple dimensions.
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parallelise-pandas
At PyCon US in May 2021, Guido van Rossum presented Faster CPython, a project that aims to double the speed of Python 3.11. The cooperation with the other Python core developers is regulated in :pep:`PEP 659 – Specializing Adaptive Interpreter <659>`. There is also an open issue tracker and various tools for collecting bytecode statistics. CPU-intensive Python code in particular is likely to benefit from the changes; code already written in C, I/O-heavy processes and multithreaded code, on the other hand, are unlikely to benefit.
And indeed, the cPython versions have become significantly more efficient since then:
| Version | |
|---|---|
| CPython 3.10.4 | 1.422x |
| CPython 3.12.0 | 1.093x |
| CPython 3.13.0 | 1.024x |
| CPython 3.15.0a0 – Basis |
.. seealso::
* `Faster CPython
<https://web.archive.org/web/20221007175548/https://faster-cpython.readthedocs.io/>`__
* `Faster CPython Benchmark Infrastructure
<https://github.com/faster-cpython/benchmarking-public?tab=readme-ov-file>`_
Free-threaded Python was also included in another comparison:
| Version | N-body | vs 3.14 | Spectral-norm | vs 3.14x |
|---|---|---|---|---|
| CPython 3.10 | 1,663ms | 0.75x | 16,826ms | 0.83x |
| CPython 3.11 | 1,200ms | 1.04x | 13,430ms | 1.05x |
| CPython 3.13 | 1,134ms | 1.10x | 13,637ms | 1.03x |
| CPython 3.14 – Basis | 1,242ms | 14,046ms | ||
| CPython 3.14t | 1,513ms | 0.82x | 14,551ms | 0.97x |
– Surce: The Optimization Ladder
If you don’t want to wait with your project until the release of Python 3.11 in the final version probably on 24 October 2022, you can also have a look at the following Python interpreters:
.. versionadded:: Python3.15
The `JIT <https://en.wikipedia.org/wiki/Just-in-time_compilation>`_ compiler
in Python 3.15 shows an average performance increase of 3–4% compared to the
standard CPython interpreter, with performance measurements varying between
-20 and over 100% on x86-64 Linux and AArch64 macOS systems.
.. seealso::
* `The JIT Compiler
<https://github.com/python/cpython/blob/main/Tools/jit/README.md>`_
* :ref:`whatsnew315-jit`
| Version | |
|---|---|
| CPython 3.15.0a0 (JIT) | 1.001x |
| CPython 3.15.0a0 – Basis |
For intensive numerical operations, Python can be very slow, even if you have avoided all anti-patterns and used vectorisations with NumPy. In this case, translating code into Cython can be helpful.
| Version | N-body | vs 3.14 | Spectral-norm | vs 3.14x |
|---|---|---|---|---|
| CPython 3.14 – Basis | 1,242ms | 14,046ms | ||
| Cython | 10ms | 124x | 142ms | 99x |
Unfortunately, the code often has to be restructured and thus increases in complexity. Explicit type annotations and the provision of code also become more cumbersome.
Our example could then look like this:
.. literalinclude:: cy_kmeans.pyx :caption: cy_kmeans.pyx :name: cy_kmeans.pyx :lines: 5-32
.. seealso::
* `Cython Tutorials
<https://cython.readthedocs.io/en/latest/src/tutorial/>`_
Numba is a JIT compiler that translates mainly scientific Python and NumPy code into fast machine code, for example:
.. literalinclude:: nb_kmeans.py :caption: nb_kmeans.py :name: nb_kmeans.py :lines: 5-29
However, Numba requires LLVM and some Python constructs are not supported.
| Version | N-body | vs 3.14 | Spectral-norm | vs 3.14x |
|---|---|---|---|---|
| CPython 3.14 – Basis | 1,242ms | 14,046ms | ||
| Numba | 22ms | 56x | 104ms | 135x |
:doc:`jupyter-tutorial:hub/ipyparallel/index`, :doc:`dask` and Ray can distribute tasks in a cluster. In doing so, they have different focuses:
ipyparallelsimply integrates into a :doc:`jupyter-tutorial:hub/index`.- :doc:`dask` imitates pandas, NumPy iterators, Toolz und PySpark when it distributes their tasks.
- Ray provides a simple, universal API for building distributed applications.
- RLlib will scale reinforcement learning in particular.
- A backend for joblib supports distributed scikit-learn programs.
- XGBoost-Ray is a backend for distributed XGBoost.
- LightGBM-Ray is a backend for distributed LightGBM.
- Collective Communication Lib offers a set of native collective primitives for Gloo and the NVIDIA Collective Communication Library (NCCL).
Our example could look like this with Dask:
.. literalinclude:: ds_kmeans.py :caption: ds_kmeans.py :name: ds_kmeans.py :lines: 5-
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dask.ipynb
After a brief :doc:`overview <multiprocessing-threading-async>`, three examples of :doc:`threading <threading-example>`, :doc:`multiprocessing <multiprocessing>` and :doc:`async <asyncio-example>` illustrate the rules and best practices.
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multiprocessing-threading-async
threading-example.ipynb
multiprocessing.ipynb
threading-forking-combined.ipynb
asyncio-example.ipynb