Installation¶
TensorVision has a couple of prerequisites that need to be installed first, but it is not very picky about versions.
Most of the instructions below assume you are running a Linux or Mac system, but are otherwise very generic.
If you run into any trouble, please check the TensorFlow installation instructions which cover installing the prerequisites for a range of operating systems, or ask for help as a GitHub issue (https://github.com/TensorVision/TensorVision/issues).
Prerequisites¶
Python + pip¶
TensorVision currently requires Python 2.7 or 3.4 to run. Please install Python via the package manager of your operating system if it is not included already.
Python includes pip for installing additional modules that are not shipped with your operating system, or shipped in an old version, and we will make use of it below. We recommend installing these modules into your home directory via --user, or into a virtual environment via virtualenv.
C compiler¶
Numpy/scipy require a compiler if you install them via pip. On Linux, the default compiler is usually gcc, and on Mac OS, it’s clang. Again, please install them via the package manager of your operating system.
numpy/scipy + BLAS¶
TensorVision requires numpy. Numpy/scipy rely on a BLAS library to provide fast linear algebra routines. They will work fine without one, but a lot slower, so it is worth getting this right (but this is less important if you plan to use a GPU).
If you install numpy and scipy via your operating system’s package manager, they should link to the BLAS library installed in your system. If you install numpy and scipy via pip install numpy and pip install scipy, make sure to have development headers for your BLAS library installed (e.g., the libopenblas-dev package on Debian/Ubuntu) while running the installation command. Please refer to the numpy/scipy build instructions if in doubt.
Stable TensorVision release¶
Currently, no stable version is available.
Bleeding-edge version¶
To install the latest version of TensorVision, run the following commands:
pip install --upgrade https://github.com/TensorVision/TensorVision/archive/master.zip
Again, add --user if you want to install to your home directory instead.
Development installation¶
Alternatively, you can install TensorVision from source, in a way that any changes to your local copy of the source tree take effect without requiring a reinstall. This is often referred to as editable or development mode. Firstly, you will need to obtain a copy of the source tree:
git clone https://github.com/TensorVision/TensorVision.git
It will be cloned to a subdirectory called TensorVision. Make sure to place it in some permanent location, as for an editable installation, Python will import the module directly from this directory and not copy over the files. Enter the directory and install the requirements:
cd TensorVision
pip install -r requirements.txt
You should also install the additional development requirements which can be found in requirements-dev.txt.
To install the TensorVision package itself, in editable mode, run:
pip install --editable .
As always, add --user to install it to your home directory instead.
Optional: If you plan to contribute to TensorVision, you will need to fork the TensorVision repository on GitHub. This will create a repository under your user account. Update your local clone to refer to the official repository as upstream, and your personal fork as origin:
git remote rename origin upstream
git remote add origin https://github.com/<your-github-name>/TensorVision.git
If you set up an SSH key, use the SSH clone URL instead: git@github.com:<your-github-name>/TensorVision.git.
You can now use this installation to develop features and send us pull requests on GitHub, see Development!
You can run the tests by
python setup.py test
GPU support¶
Thanks to TensorFlow, TensorVision transparently supports training your networks on a GPU, which may be 10 to 50 times faster than training them on a CPU. Currently, this requires an NVIDIA GPU with CUDA support, and some additional software for TensorFlow to use it.
CUDA¶
Install the latest CUDA Toolkit and possibly the corresponding driver available from NVIDIA: https://developer.nvidia.com/cuda-downloads
Closely follow the Getting Started Guide linked underneath the download table to be sure you don’t mess up your system by installing conflicting drivers.
After installation, make sure /usr/local/cuda/bin is in your PATH, so nvcc --version works. Also make sure /usr/local/cuda/lib64 is in your LD_LIBRARY_PATH, so the toolkit libraries can be found.