Windows Installation Instructions¶
Warning
If you want to install the bleeding-edge or development version of Theano from GitHub, please make sure you are reading the latest version of this page.
Requirements¶
Note
We only support the installation of the requirements through conda.
- Python == 2.7* or ( >= 3.3 and < 3.6 )
- The conda distribution is highly recommended. Python 2.4 was supported up to and including the release 0.6. Python 2.6 was supported up to and including the release 0.8.2. Python 3 is supported past the 3.3 release.
- NumPy >= 1.9.1 <= 1.12
- Earlier versions could work, but we don’t test it.
- SciPy >= 0.14 < 0.17.1
- Only currently required for sparse matrix and special functions support, but highly recommended. SciPy >=0.8 could work, but earlier versions have known bugs with sparse matrices.
- BLAS installation (with Level 3 functionality)
- Recommended: MKL, which is free through Conda with
mkl-service
package.- Alternatively, we suggest to install OpenBLAS, with the development headers (
-dev
,-devel
, depending on your Linux distribution).
Optional requirements
- GCC compiler with
g++
(version >=4.2.*
), and Python development files- Highly recommended. Theano can fall back on a NumPy-based Python execution model, but a C compiler allows for vastly faster execution.
- nose >= 1.3.0
- Recommended, to run Theano’s test-suite.
- Sphinx >= 0.5.1, pygments
- For building the documentation. LaTeX and dvipng are also necessary for math to show up as images.
- pydot-ng
- To handle large picture for gif/images.
- NVIDIA CUDA drivers and SDK
- Highly recommended Required for GPU code generation/execution on NVIDIA gpus. See instruction below.
- libgpuarray
- Required for GPU/CPU code generation on CUDA and OpenCL devices (see: GpuArray Backend).
- pycuda and skcuda
- Required for some extra operations on the GPU like fft and solvers. We use them to wrap cufft and cusolver. Quick install
pip install pycuda scikit-cuda
. For cuda 8, the dev version of skcuda (will be released as 0.5.2) is needed for cusolver:pip install pycuda; pip install git+https://github.com/lebedov/scikit-cuda.git#egg=scikit-cuda
.
Requirements installation through Conda (recommended)¶
Install Miniconda¶
Follow this link to install Miniconda.
Note
If you want fast compiled code (recommended), make sure you have g++
installed.
Install requirements and optional packages¶
conda install numpy scipy mkl-service libpython <m2w64-toolchain> <nose> <nose-parameterized> <sphinx> <pydot-ng>
Note
- Arguments between <...> are optional.
m2w64-toolchain
package provides a fully-compatible version of GCC and is then highly recommended.
Installation¶
Stable Installation¶
With conda
¶
If you use conda, you can directly install both theano and pygpu. Libgpuarray will be automatically installed as a dependency.
conda install theano pygpu
Warning
Last conda packages for theano (0.9) and pygpu (0.6*) currently don’t support Python 3.4 branch.
With pip
¶
If you use pip, you have to install Theano and libgpuarray separately.
theano¶
Install the latest stable version of Theano with:
<sudo> pip install <--user> Theano[test, doc]
- Any argument between <...> is optional.
- Use sudo for a root installation.
- Use user for a user installation without admin rights. It will install Theano in your local site-packages.
- [test] will install the requirements for testing.
- [doc] will install the requirements in order to generate the documentation.
If you encountered any trouble, head to the Troubleshooting page.
The latest stable version of Theano is 0.9.0
(tagged with rel-0.9.0
).
libgpuarray¶
For the stable version of Theano you need a specific version of libgpuarray,
that has been tagged v0.6.2
.
Download it with:
git clone https://github.com/Theano/libgpuarray.git
cd libgpuarray
git checkout tags/v0.6.2 -b v0.6.2
and then follow the Step-by-step instructions.
Bleeding-Edge Installation (recommended)¶
Install the latest, bleeding-edge, development version of Theano with:
<sudo> pip install <--user> <--no-deps> git+https://github.com/Theano/Theano.git#egg=Theano
- Any argument between <...> is optional.
- Use sudo for a root installation.
- Use user for a user installation without admin rights. It will install Theano in your local site-packages.
- Use no-deps when you don’t want the dependencies of Theano to be installed through pip. This is important when they have already been installed as system packages.
If you encountered any trouble, head to the Troubleshooting page.
libgpuarray¶
Install the latest, development version of libgpuarray following the Step-by-step instructions.
Developer Installation¶
Install the developer version of Theano with:
git clone git://github.com/Theano/Theano.git
cd Theano
<sudo> pip install <--user> <--no-deps> -e .
- Any argument between <...> is optional.
- Use sudo for a root installation.
- Use user for a user installation without admin rights. It will install Theano in your local site-packages.
- Use no-deps when you don’t want the dependencies of Theano to be installed through pip. This is important when they have already been installed as system packages.
- -e makes your installation editable, i.e., it links it to your source directory.
If you encountered any trouble, head to the Troubleshooting page.
libgpuarray¶
Install the latest, development version of libgpuarray following the Step-by-step instructions.
Instructions for other Python distributions (not recommended)¶
If you plan to use Theano with other Python distributions, these are generic guidelines to get a working environment:
Look for the mandatory requirements in the package manager’s repositories of your distribution. Many distributions come with
pip
package manager which use PyPI repository. The required modules are Python (of course), NumPy, SciPy and a BLAS implementation (MKL or OpenBLAS). Use the versions recommended at the top of this documentation.If the package manager provide a GCC compiler with the recommended version (see at top), install it. If not, you could use the build TDM GCC which is provided for both 32- and 64-bit platforms. A few caveats to watch for during installation:
- Install to a directory without spaces (we have placed it in
C:\SciSoft\TDM-GCC-64
)- If you don’t want to clutter your system PATH un-check
add to path
option.- Enable OpenMP support by checking the option
openmp support option
.Install CUDA with the same instructions as above.
Install the latest, development version of libgpuarray following the Step-by-step instructions.