For Ubuntu 11.10 through 14.04:
sudo apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ libopenblas-dev git sudo pip install Theano
For Ubuntu 11.04:
sudo apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ git libatlas3gf-base libatlas-dev sudo pip install Theano
If you have error that contain “gfortran” in it, like this one:
ImportError: (‘/home/Nick/.theano/compiledir_Linux-2.6.35-31-generic-x86_64-with-Ubuntu-10.10-maverick–2.6.6/tmpIhWJaI/0c99c52c82f7ddc775109a06ca04b360.so: undefined symbol: _gfortran_st_write_done’
The problem is probably that NumPy is linked with a different blas then then one currently available (probably ATLAS). There is 2 possible fixes:
1) is better as OpenBLAS is faster then ATLAS and NumPy is probably already linked with it. So you won’t need any other change in Theano files or Theano configuration.
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:firstname.lastname@example.org:port/" You can also provide the --proxy=[user:pass@]url:port parameter to pip. The [user:pass@] portion is optional.
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.
This page describes how to install Theano for Python 2. If you have installed Python 3 on your system, maybe you need to change the command pip to pip-2.7 to specify to install it for Python 2, as sometimes the pip command refers to the Python 3 version.
The development version of Theano supports Python 3.3 and probably supports Python 3.2, but we do not test on it.
If you would like, instead, to install the bleeding edge Theano (from github) such that you can edit and contribute to Theano, replace the pip install Theano command with:
git clone git://github.com/Theano/Theano.git cd Theano python setup.py develop --user cd ..
If you would like to install Theano in a VirtualEnv, you will want to pass the –system-site-packages flag when creating the VirtualEnv so that it will pick up the system-provided Numpy and SciPy.
virtualenv --system-site-packages -p python2.7 theano-env source theano-env/bin/activate pip install Theano
- NumPy (~30s): python -c "import numpy; numpy.test()"
- SciPy (~1m): python -c "import scipy; scipy.test()"
- Theano (~30m): python -c "import theano; theano.test()"
NumPy 1.6.2, 1.7.0 and 1.7.1, have a bug where it marks some ndarrays as not aligned. Theano does not support unaligned arrays, and raises an Exception when that happens. This can cause one test to fail with an unaligned error with those versions of NumPy. You can ignore that test error as at worst, your code will crash. If this happens, you can install another NumPy version to fix this problem. NumPy 1.6.2 is used in Ubuntu 12.10 and NumPy 1.7.1 is used in Ubuntu 13.04.
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 `python -c "import os, theano; print os.path.dirname(theano.__file__)"`/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.
Theano should link to a parallel version of Blas and use all cores when possible. By default it should use all cores. Set the environment variable “OMP_NUM_THREADS=N” to specify to use N threads.
It is possible to have a faster installation of Theano than the one these instructions 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.
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 installed NumPy/SciPy with pip instead of the system package, you can run this:
sudo pip install --upgrade theano
Change to the Theano directory and run:
The openblas included in some older Ubuntu version is limited to 2 threads. Ubuntu 14.04 do not have this limit. If you want to use more cores at the same time, you will need to compile it yourself. Here is some code that will help you.
# remove openblas if you installed it sudo apt-get remove libopenblas-base # Download the development version of OpenBLAS git clone git://github.com/xianyi/OpenBLAS cd OpenBLAS make FC=gfortran sudo make PREFIX=/usr/local/ install # Tell Theano to use OpenBLAS. # This works only for the current user. # Each Theano user on that computer should run that line. echo -e "\n[blas]\nldflags = -lopenblas\n" >> ~/.theanorc
Basic configuration for the GPU Using the GPU.
Ubuntu 11.10/12.04 (probably work on 11.04 too):
sudo apt-add-repository ppa:ubuntu-x-swat/x-updates sudo apt-get update sudo apt-get install nvidia-current
Then you need to fetch latest CUDA tool kit (download ubuntu 11.04 32/64bit package) from here.
sudo apt-get install nvidia-current sudo apt-get install nvidia-cuda-toolkit # As of October 31th, 2014, provide cuda 5.5, not the latest cuda 6.5
If you want cuda 6.5, you can download packages from nvidia for Ubuntu 14.04.
If you downloaded the run package (the only one available for CUDA 5.0 and older), you install it like this:
chmod a+x XXX.sh sudo ./XXX.sh
Since CUDA 5.5, Nvidia provide a DEB package. If you don’t know how to intall it, just double click on it from the graphical interface. It should ask if you want to install it. On Ubuntu 14.04, you need to run this in your terminal:
sudo apt-get update sudo apt-get install cuda
You must reboot the computer after the driver installation. To test that it was loaded correctly after the reboot, run the command nvidia-smi from the command line.
You probably need to change the default version of gcc as explained by Benjamin J. McCann if the package you downloaded is for another Ubuntu version:
sudo apt-get install nvidia-cuda-toolkit g++-4.4 gcc-4.4 # On Ubuntu 11.10 and 12.04, you probably need to change gcc-4.5 to gcc-4.6 on the next line. sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.5 40 --slave /usr/bin/g++ g++ /usr/bin/g++-4.5 sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.4 60 --slave /usr/bin/g++ g++ /usr/bin/g++-4.4 sudo update-alternatives --config gcc
THEANO_FLAGS=floatX=float32,device=gpu python /usr/lib/python2.*/site-packages/theano/misc/check_blas.py
Ubuntu 10.04 LTS: default gcc version 4.4.3. gcc 4.1.2, 4.3.4 available.
Ubuntu 11.04: default gcc version 4.5.2. gcc 4.4.5 available.
Ubuntu 11.10: default gcc version 4.6.1. gcc 4.4.6 and 4.5.3 available.
Ubuntu 12.04 LTS: default gcc version 4.6.3. gcc 4.4.7 and 4.5.3 available.
Ubuntu 12.10: default gcc version 4.7.2. gcc 4.4.7, 4.5.4 and 4.6.3 available.
Ubuntu 13.10: default gcc version 4.8.1. gcc 4.4.7, 4.6.4 and 4.7.3 available.
Ubuntu 14.04: default gcc version 4.8.2, gcc 4.4.7,, 4.6.4, and 4.7.3 available.