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[MRG+1] Clean up the advanced installation doc to remove python < 3.5 parts #13064

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Jan 30, 2019
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[MRG+1] Clean up the advanced installation doc to remove python < 3.5 parts #13064

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4503bb5
remove python <= 3.4 from advanced installation doc
jeremiedbb Jan 29, 2019
ff7f7e5
min versions
jeremiedbb Jan 29, 2019
b757c94
remove python < 3.5 multiprocessing warning
jeremiedbb Jan 29, 2019
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70 changes: 7 additions & 63 deletions doc/developers/advanced_installation.rst
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Expand Up @@ -35,8 +35,8 @@ Building from source
Scikit-learn requires:

- Python (>= 3.5),
- NumPy (>= 1.8.2),
- SciPy (>= 0.13.3).
- NumPy (>= 1.11),
- SciPy (>= 0.17).

.. note::

Expand All @@ -46,7 +46,7 @@ Scikit-learn requires:

Building Scikit-learn also requires

- Cython >=0.23
- Cython >=0.28.5

Running tests requires

Expand Down Expand Up @@ -165,25 +165,16 @@ Windows
To build scikit-learn on Windows you need a working C/C++ compiler in
addition to numpy, scipy and setuptools.

Picking the right compiler depends on the version of Python (2 or 3)
and the architecture of the Python interpreter, 32-bit or 64-bit.
You can check the Python version by running the following in ``cmd`` or
``powershell`` console::

python --version

and the architecture with::
The building command depends on the architecture of the Python interpreter,
32-bit or 64-bit. You can check the architecture by running the following in
``cmd`` or ``powershell`` console::

python -c "import struct; print(struct.calcsize('P') * 8)"

The above commands assume that you have the Python installation folder in your
PATH environment variable.


Python >= 3.5
-------------

For Python versions as of 3.5, you need `Build Tools for Visual Studio 2017
You will need `Build Tools for Visual Studio 2017
<https://visualstudio.microsoft.com/de/downloads/>`_.

For 64-bit Python, configure the build environment with::
Expand All @@ -198,53 +189,6 @@ And build scikit-learn from this environment::
Replace ``x64`` by ``x86`` to build for 32-bit Python.


32-bit Python (<= 3.4)
----------------------

For 32-bit Python versions up to 3.4 use Microsoft Visual C++ Express 2010.

Once installed you should be able to build scikit-learn without any
particular configuration by running the following command in the scikit-learn
folder::

python setup.py install


64-bit Python (<= 3.4)
----------------------

For 64-bit Python versions up to 3.4, you either need the full Visual Studio or
the free Windows SDKs that can be downloaded from the links below.

The Windows SDKs include the MSVC compilers both for 32 and 64-bit
architectures. They come as a ``GRMSDKX_EN_DVD.iso`` file that can be mounted
as a new drive with a ``setup.exe`` installer in it.

- For Python you need SDK **v7.1**: `MS Windows SDK for Windows 7 and .NET
Framework 4
<https://www.microsoft.com/en-us/download/details.aspx?id=8442>`_

Both SDKs can be installed in parallel on the same host. To use the Windows
SDKs, you need to setup the environment of a ``cmd`` console launched with the
following flags ::

cmd /E:ON /V:ON /K

Then configure the build environment with::

SET DISTUTILS_USE_SDK=1
SET MSSdk=1
"C:\Program Files\Microsoft SDKs\Windows\v7.1\Setup\WindowsSdkVer.exe" -q -version:v7.1
"C:\Program Files\Microsoft SDKs\Windows\v7.1\Bin\SetEnv.cmd" /x64 /release

Finally you can build scikit-learn in the same ``cmd`` console::

python setup.py install

Replace ``/x64`` by ``/x86`` to build for 32-bit Python instead of 64-bit
Python.


Building binary packages and installers
---------------------------------------

Expand Down
18 changes: 0 additions & 18 deletions doc/modules/computing.rst
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Expand Up @@ -430,24 +430,6 @@ and in this
from Daniel Nouri which has some nice step by step install instructions for
Debian / Ubuntu.

.. warning::

Multithreaded BLAS libraries sometimes conflict with Python's
``multiprocessing`` module, which is used by e.g. ``GridSearchCV`` and
most other estimators that take an ``n_jobs`` argument (with the exception
of ``SGDClassifier``, ``SGDRegressor``, ``Perceptron``,
``PassiveAggressiveClassifier`` and tree-based methods such as random
forests). This is true of Apple's Accelerate and OpenBLAS when built with
OpenMP support.

Besides scikit-learn, NumPy and SciPy also use BLAS internally, as
explained earlier.

If you experience hanging subprocesses with ``n_jobs>1`` or ``n_jobs=-1``,
make sure you have a single-threaded BLAS library, or set ``n_jobs=1``,
or upgrade to Python 3.4 which has a new version of ``multiprocessing``
that should be immune to this problem.

.. _working_memory:

Limiting Working Memory
Expand Down