CVTron has some prerequisites that need to be installed first, including TensorFlow , numpy and matplotlib. For GPU support CUDA and cuDNN are required.

If you run into any trouble, please check the TensorFlow installation instructions which cover installing the TensorFlow for a range of operating systems including Mac OX, Linux and Windows, or ask for help on CVTron@gmail.com or FQA.

CVTron is build on the top of Python-version TensorFlow, so please install Python first.

We highly recommend python3 instead of python2 for the sake of future.

Python includes pip command for installing additional modules is recommended. Besides, a virtual environment via virtualenv can help you to manage python packages.

Take Python3 on Ubuntu for example, to install Python includes pip, run the following commands:

sudo apt-get install python3 sudo apt-get install python3-pip sudo pip3 install virtualenv

To build a virtual environment and install dependencies into it, run the following commands: (You can also skip to Step 3, automatically install the prerequisites by CVTron)

virtualenv env env/bin/pip install matplotlib env/bin/pip install numpy env/bin/pip install scipy env/bin/pip install scikit-image

To check the installed packages, run the following command:

env/bin/pip list

After that, you can run python script by using the virtual python as follow.

env/bin/python *.py

The installation instructions of TensorFlow are written to be very detailed on TensorFlow website. However, there are something need to be considered. For example, TensorFlow officially supports GPU acceleration for Linux, Mac OX and Windows at present.

For ARM processor architecture, you need to install TensorFlow from source.

The simplest way to install CVTron is as follow, it will also install the numpy and matplotlib automatically.

[stable version] pip install CVTron [master version] pip install git+https://github.com/zsdonghao/CVTron.git

However, if you want to modify or extend CVTron, you can download the repository from Github and install it as follow.

cd to the root of the git tree pip install -e .

This command will run the setup.py to install CVTron. The -e reflects editable, then you can edit the source code in CVTron folder, and import the edited CVTron.

Thanks to NVIDIA supports, training a fully connected network on a GPU, which may be 10 to 20 times faster than training them on a CPU. For convolutional network, may have 50 times faster. This requires an NVIDIA GPU with CUDA and cuDNN support.

The TensorFlow website also teach how to install the CUDA and cuDNN, please see TensorFlow GPU Support.

Download and install the latest CUDA is available from NVIDIA website:

After installation, make sure /usr/local/cuda/bin is in your PATH (use echo #PATH to check), and nvcc --version works. Also ensure /usr/local/cuda/lib64 is in your LD_LIBRARY_PATH, so the CUDA libraries can be found.

If CUDA is set up correctly, the following command should print some GPU information on the terminal:

python -c “import tensorflow”

Apart from CUDA, NVIDIA also provides a library for common neural network operations that especially speeds up Convolutional Neural Networks (CNNs). Again, it can be obtained from NVIDIA after registering as a developer (it take a while):

Download and install the latest cuDNN is available from NVIDIA website:

To install it, copy the *.h files to /usr/local/cuda/include and the lib* files to /usr/local/cuda/lib64.

CVTron is built on the top of Python-version TensorFlow, so please install Python first. Note：We highly recommend installing Anaconda. The lowest version requirements of Python is py35.

Anaconda download

Thanks to NVIDIA supports, training a fully connected network on a GPU, which may be 10 to 20 times faster than training them on a CPU. For convolutional network, may have 50 times faster. This requires an NVIDIA GPU with CUDA and cuDNN support.

You should preinstall Microsoft Visual Studio (VS) before installing CUDA. The lowest version requirements is VS2010. We recommend installing VS2015 or VS2013. CUDA7.5 supports VS2010, VS2012 and VS2013. CUDA8.0 also supports VS2015.

Download and install the latest CUDA is available from NVIDIA website:

CUDA download

We do not recommend modifying the default installation directory.

The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. Download and extract the latest cuDNN is available from NVIDIA website:

cuDNN download

After extracting cuDNN, you will get three folders (bin, lib, include). Then these folders should be copied to CUDA installation. (The default installation directory is C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0)

You can easily install CVTron using pip in CMD：

pip install tensorflow #CPU version pip install tensorflow-gpu #GPU version (GPU version and CPU version just choose one) pip install CVTron #Install CVTron

Enter “python” in CMD. Then:

import CVTron

If there is no error and the following output is displayed, the GPU version is successfully installed.

successfully opened CUDA library cublas64_80.dll locally successfully opened CUDA library cuDNN64_5.dll locally successfully opened CUDA library cufft64_80.dll locally successfully opened CUDA library nvcuda.dll locally successfully opened CUDA library curand64_80.dll locally

If there is no error, the CPU version is successfully installed.

If you get the following output when import CVTron, please read FQA.

_tkinter.TclError: no display name and no $DISPLAY environment variable