There are many ways to configure BLAS for Theano. This is done with the Theano
flags ``blas.ldflags`` (:ref:`libdoc_config`). The default is to use the BLAS
installation information in NumPy, accessible via
``numpy.distutils.__config__.show()``. You can tell theano to use a different
version of BLAS, in case you didn't compile numpy with a fast BLAS or if numpy
version of BLAS, in case you did not compile numpy with a fast BLAS or if numpy
was compiled with a static library of BLAS (the latter is not supported in
Theano).
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@@ -250,43 +267,55 @@ configuration options that you can put there, see :ref:`libdoc_config`.
Here are some different way to configure BLAS:
0) Do nothing and use the default config, which is to link against the same BLAS against which NumPy was built. This doesn't work in the case NumPy was compiled with a static library (e.g. ATLAS is compiled by default only as a static library).
1) Disable the usage of BLAS and fall back on NumPy for dot products. To do this, set the value of ``blas.ldflags`` as the empty string (ex: ``export THEANO_FLAGS=blas.ldflags=``). Depending on the kind of matrix operations your Theano code performs, this might slow some things down (vs. linking with BLAS directly).
2) You can install the default (reference) version of BLAS if the NumPy version (against which Theano links) doesn't work. If you have root or sudo access in fedora you can do ``sudo yum install blas blas-devel``. Under Ubuntu/Debian ``sudo apt-get install libblas-dev``. Then use the Theano flags ``blas.ldflags=-lblas``. Not that the default version of blas is not optimized. Using an optimized version can give up to 10x speedups in the BLAS functions that we use.
3) Install the ATLAS library. ATLAS is an open source optimized version of BLAS. You can install a precompiled version on most OSes, but if you're willing to invest the time, you can compile it to have a faster version (we have seen speed-ups of up to 3x, especialy on more recent computers, against the precompiled one). On Fedora, ``sudo yum install atlas-devel``. Under Ubuntu, ``sudo apt-get install libatlas-base-dev libatlas-base`` or ``libatlas3gf-sse2`` if your CPU supports SSE2 instructions. Then set the Theano flags ``blas.ldflags`` to ``-lf77blas -latlas -lgfortran``. Note that these flags are sometimes OS dependent.
4) Use a faster version like MKL, GOTO, ... You are on your own to install it. See the doc of that software and set the Theano flags ``blas.ldflags`` correctly (for example, for MKL this might be ``-lmkl -lguide -lpthread`` or ``-lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -lguide -liomp5 -lmkl_mc -lpthread``).
0) Do nothing and use the default config, which is to link against the same
BLAS against which NumPy was built. This does not work in the case NumPy was
compiled with a static library (e.g. ATLAS is compiled by default only as a
static library).
1) Disable the usage of BLAS and fall back on NumPy for dot products. To do
this, set the value of ``blas.ldflags`` as the empty string (ex: ``export
THEANO_FLAGS=blas.ldflags=``). Depending on the kind of matrix operations your
Theano code performs, this might slow some things down (vs. linking with BLAS
directly).
2) You can install the default (reference) version of BLAS if the NumPy version
(against which Theano links) does not work. If you have root or sudo access in
fedora you can do ``sudo yum install blas blas-devel``. Under Ubuntu/Debian
``sudo apt-get install libblas-dev``. Then use the Theano flags
``blas.ldflags=-lblas``. Not that the default version of blas is not optimized.
Using an optimized version can give up to 10x speedups in the BLAS functions
that we use.
3) Install the ATLAS library. ATLAS is an open source optimized version of
BLAS. You can install a precompiled version on most OSes, but if you're willing
to invest the time, you can compile it to have a faster version (we have seen
speed-ups of up to 3x, especialy on more recent computers, against the
precompiled one). On Fedora, ``sudo yum install atlas-devel``. Under Ubuntu,
``sudo apt-get install libatlas-base-dev libatlas-base`` or
``libatlas3gf-sse2`` if your CPU supports SSE2 instructions. Then set the
Theano flags ``blas.ldflags`` to ``-lf77blas -latlas -lgfortran``. Note that
these flags are sometimes OS-dependent.
4) Use a faster version like MKL, GOTO, ... You are on your own to install it.
See the doc of that software and set the Theano flags ``blas.ldflags``
correctly (for example, for MKL this might be ``-lmkl -lguide -lpthread`` or
