Requirements

Note

We only support the installation of the requirements through conda.

Python 2 >= 2.6 or Python 3 >= 3.3

The development package (python-dev or python-devel on most Linux distributions) is recommended (see just below).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 < 1.11.1

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.
• Alternatively, we suggest to install OpenBLAS, with the development headers (-dev, -devel, depending on your Linux distribution).

Optional requirements

g++ (Linux and Windows), clang (OS X)

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.)

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++ (Windows/Linux) or Clang (OS X) installed.

Install requirements and optional packages

conda install numpy scipy mkl <nose> <sphinx> <pydot-ng>

• Arguments between <...> are optional.

Install and configure the GPU drivers (recommended)

Warning

OpenCL support is still minimal for now.

1. Install CUDA drivers

   • Follow this link to install the CUDA driver and the CUDA Toolkit.
   • You must reboot the computer after the driver installation.
   • Test that it was loaded correctly after the reboot, executing the command nvidia-smi from the command line.

   Note

   Sanity check: The bin subfolder should contain an nvcc program. This folder is called the cuda root directory.

2. Fix ‘lib’ path

   • Add the ‘lib’ subdirectory (and/or ‘lib64’ subdirectory if you have a 64-bit OS) to your $LD_LIBRARY_PATH environment variable.

3. Set Theano’s config flags

   To use the GPU you need to define the cuda root. You can do it in one of the following ways:

   • Define a $CUDA_ROOT environment variable to equal the cuda root directory, as in CUDA_ROOT=/path/to/cuda/root, or
   • add a cuda.root flag to THEANO_FLAGS, as in THEANO_FLAGS='cuda.root=/path/to/cuda/root', or
   • add a [cuda] section to your .theanorc file containing the option root = /path/to/cuda/root.