You install the required Linux packages, configure and build Qt.After an hour or so, your build fails because a package is missing.You must start over again and again Wouldnt it be nice to have a single command that installs all the required Ubuntu packages and save all these needless iterations.
Sudo Apt-Get Y Libgstreamer0.10-Dev Libgstreamer-Plugins-Base0.10-Dev Ubuntu Install The Required![]() If you have a brand new computer with a graphics card and you dont know what libraries to install to start your deep learning journey, this article will help you. We will install CUDA, cuDNN, Python 3, TensorFlow, Pytorch, OpenCV, Dlib along with other Python Machine Learning libraries step-by-step. Note, that if you would like to use TensorFlow with Keras support, there is no need to install Keras package separately, since from TensorFlow2.0 Keras comes as tensorflow.keras submodule. We have tested the instructions on a system with the following configuration: Processor: Intel core i7 6850K with 6 cores and 40 PCIe lines Motherboard: Gigabyte X99P SLI RAM: 32 GB Graphics Card: Zotac GeForce GTX 1080 Ti with 11 GB RAM We will be assuming Ubuntu 16.04 installation. Sudo Apt-Get Y Libgstreamer0.10-Dev Libgstreamer-Plugins-Base0.10-Dev Ubuntu Update The InformationStep 1: Install Prerequisites Before installing anything, let us first update the information about the packages stored on the computer and upgrade the already installed packages to their latest versions. In order to use the graphics card, we need to have CUDA drivers installed on our system. If you do not have a NVIDIA CUDA supported Graphics Card, then you can skip this step. We recommend you download the deb ( local ) version from Installer type as shown in the screenshot below. After downloading the file, go to the folder where you have downloaded the file and run the following commands from the terminal to install the CUDA drivers. Please make sure that the filename used in the command below is the same as the downloaded file. It is a tool used for monitoring the state of the GPU. As a side note, I found that apart from getting better resolution options for display, installing the CUDA driver lowers the power consumption of the graphics card from 71W to 16W for a NVIDIA GTX 1080 Ti GPU attached via PCIe x16. Step 3: Install cuDNN CUDA Deep Neural Network (cuDNN) is a library used for further optimizing neural network computations. Go to official cudnn website and fill out the form for downloading the cuDNN library. After you get to the download link ( sample shown below ), you should download the cuDNN v6.0 Library for Linux from the options. Now, go to the folder where you have downloaded the.tgz file and from the command line execute the following. In a virtual environment, you can install any python library without affecting the global installation or other virtual environments. This way, even if you damage the libraries in one virtual environment, your rest of the projects remain safe. Install the virtual environment wrapper which enables us to create and work on virtual environments in python. We will create virtual environments and install all the deep learning frameworks inside them. We create a separate environment for Python 3: create a virtual environment for python 3. Remove the line WITHCUDAON if you dont have CUDA in your system. Also, it might get stuck for long at some places, but dont worry unless it is stuck for more than an hour. Note the exact path of the cv2.so file. In my system, it is located in dist-packages. But in most systems, it is located in site-packages directory.
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