Installation

Setup

Anaconda

A comfortable way to setup a python environment with common scientific python packages is the Anaconda distribution by Continuum (make sure to use the Python 2.7 version). If you don’t want to install the whole Anaconda distribution you can use Miniconda instead.

• Install ELEKTRONN using conda (via conda-forge):

  conda config --add channels conda-forge
  conda install elektronn
  

  Note

  This resolves all dependencies, but you still need to configure theano (which will be installed as a dependency) as described below

• After installation you can optionally create a user configuration file by editing the file examples/config_template.py and put it into your home directory as elektronn.config (see Configuration of Parameters)

• Try out one of the examples to confirm everything works

pip

If you don’t want to use Anaconda you can use pip instead. Then you must take care of the dependencies yourself to some degree:

• Install ELEKTRONN using python pip (options can specify target locations, editable installs etc., see python2 -m pip help install):

  python2 -m pip install [options] elektronn
  

  If you want to install ELEKTRONN from source (or the sdist from PyPI), you should install Cython first. For example in Ubuntu:

  sudo apt install cython
  

The full dependencies are listed in the file requirements.txt

Note

If python2 -m pip install <package> fails due to a permission error, try python2 -m pip install --user <package>.

• Configure theano as explained below
• After installation you can optionally create a user configuration file by editing the file examples/config_template.py and putting it into your home as elektronn.config (see Configuration of Parameters)
• Try out one of the examples to confirm everything works

Theano

If you let our setup install theano, you nonetheless have to do the configuration steps below and install CUDA to use the GPU (more details at theano’s installation instructions):

• Install Nvidia’s CUDA toolkit, e.g. apt-get install nvidia-cuda-toolkit or manually from the Nvidia website. The next two points assume for illustration purpose that you have installed the toolkit in the path /usr/local/cuda-7.5.

• Set the paths to the toolkit, e.g. by adding to you .bashrc-file:

  export PATH=/usr/local/cuda-7.5/bin:$PATH
  export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH
  

• Configure your .theanorc-file. E.g. create a new file named ~/.theanorc and put the following into it:

  [global]
  floatX = float32
  device = gpu # or gpu0, gpu1,...
  exception_verbosity=high
  linker = cvm_nogc
  
  [nvcc]
  fastmath = True
  
  [cuda]
  root = /usr/local/cuda-7.5/
  

  Note

  1. If you want to use the command line option of elektronn-train to select a GPU device you can leave out setting a device value here
  2. The linker option disables garbage collection. This increases GPU-RAM usage but gives a significant performance boost. If you run out of GPU-RAM, remove this option (or set it to cvm).

• You might be interested into using cuDNN which is an optimised CUDA library for CNNs (theano’s instructions).