Easy Installation of an optimized Theano on CentOS 6

Note

It is possible to have a faster installation of Theano than the one these instructions will provide, but this will make the installation more complicated and/or may require that you buy software. This is a simple set of installation instructions that will leave you with a relatively well-optimized version that uses only free software. With more work or by investing money (i.e. buying a license to a proprietary BLAS implementation), it is possible to gain further performance.

Note

If you are behind a proxy, you must do some extra configuration steps before starting the installation. You must set the environment variable http_proxy to the proxy address. Using bash this is accomplished with the command export http_proxy="http://user:pass@my.site:port/" You can also provide the --proxy=[user:pass@]url:port parameter to pip. The [user:pass@] portion is optional.

Note

We use pip for 2 reasons. First, it allows “import module; module.test()” to work correctly. Second, the installation of NumPy 1.6 or 1.6.1 with easy_install raises an ImportError at the end of the installation. To my knowledge we can ignore this error, but this is not completely safe. easy_install with NumPy 1.5.1 does not raise this error.

Installation steps

  1. sudo yum install python-devel python-nose python-setuptools gcc gcc-gfortran gcc-c++ blas-devel lapack-devel atlas-devel
  2. sudo easy_install pip
  3. sudo pip install numpy==1.6.1
  4. sudo pip install scipy==0.10.1
  5. sudo pip install Theano

Test the newly installed packages

  1. NumPy (~30s): python -c "import numpy; numpy.test()"
  2. SciPy (~1m): python -c "import scipy; scipy.test()"
  3. Theano (~30m): python -c "import theano; theano.test()"

Speed test Theano/BLAS

It is recommended to test your Theano/BLAS integration. There are many versions of BLAS that exist and there can be up to 10x speed difference between them. Also, having Theano link directly against BLAS instead of using NumPy/SciPy as an intermediate layer reduces the computational overhead. This is important for BLAS calls to ger, gemv and small gemm operations (automatically called when needed when you use dot()). To run the Theano/BLAS speed test:

python /usr/lib/python2.*/site-packages/theano/misc/check_blas.py

This will print a table with different versions of BLAS/numbers of threads on multiple CPUs and GPUs. It will also print some Theano/NumPy configuration information. Then, it will print the running time of the same benchmarks for your installation. Try to find a CPU similar to yours in the table, and check that the single-threaded timings are roughly the same.

Updating Theano

If you followed these installation instructions, you can execute this command to update only Theano:

sudo pip install --upgrade --no-deps theano

If you want to also update NumPy/SciPy, you can run this:

sudo pip install --upgrade theano

Bleeding edge

Do like in the section “Updating Theano”, but use git+git://github.com/Theano/Theano.git instead of theano.