Changes

=New (Tensorflow 1.9.0 with GPU Installation Log='''Important note:'''<br> Install the version of software/packages strictly according to the instructions provided by Tensorflow. A different version of software, for example CUDA toolkit 9.2 instead of 9.0, might lead to failure in tensorflow. When upgrading tensorflow, do it very carefully. As of July 2018, Tensorflow is [https://github.com/tensorflow/tensorflow/issues/17629 notoriously easy to break] with careless installation. DO NOT attempt to install Tensorflow under your user account. Tensorflow has been installed for all users, and a new local install will interfere with it. ==Synopsis==Tensorflow was previously installed. In 2018 Summer, a new piece of graphics card was installed on DB Server. Wei and Minhhence-force installed and configured '''tensorflow-gpu 1.9.0 for Python3.6''' for all users of DB Server. ==Using Tensorflow==It is important to know that, on DB Server, Tensorflow-gpu 1.9.0 is installed for ''python3.6'', instead of either the default ''python3'' which is Python 3.5, or the default ''python'' which is Python 2.7 . In case that the system default ''python3'' might be changed, type in terminal to find out: which python3and which python3.6  A quick test of whether tensorflow-gpu is working for ''python3.6'', type the following into a terminal: python3.6 -c "import tensorflow as tf; sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)=)" This will report back which CPU and GPU devices the tensorflow is using. If there is no information for the GPU device, there is something wrong. 

(In progress) Before installing tensorflow with GPU, configure the NVIDIA® software by following instruction: https://www.tensorflow.org/install/install_linux#NVIDIARequirements===Install CUDA Toolkit 9.0===*1. Installed CUDA Toolkit 9.0 Base Installerwith the Runfile option. The toolkit is in

*3. This was supposed to be what to do next: ''Set up the environment variables:

$ export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}

$ export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64\${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}Note that the above paths change when using a custom install path with the runfile installation method.<br>To accomplish this: nano /home/mcnair/.bashrcAdd export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}Save and exit. Close and open the terminal (or source .bashrc). *4. To verify CUDA Toolkit 9.0 is installed, type nvcc -V[[File:nvcc.png]] ===Install cuDNN v7.1.4===*5. Downloaded cuDNN v7.1.4 for CUDA 9.0: In order to download cuDNN, ensure you are registered for the NVIDIA Developer Program. Then Go to: NVIDIA cuDNN home page. -> Click Download. -> Complete the short survey and click Submit. -> Accept the Terms and Conditions. A list of available download versions of cuDNN displays. -> Select the cuDNN version you want to install. Chose the tar file.*6. Install cuDNN: your CUDA directory path is referred to as /usr/local/cuda/your cuDNN download path is referred to as <cudnnpath>Follow these commands: a. Navigate to your <cudnnpath> directory containing the cuDNN Tar file.b. Unzip the cuDNN package. $ tar -xzvf cudnn-9.0-linux-x64-v7.tgzc. Copy the following files into the CUDA Toolkit directory. $ sudo cp cuda/include/cudnn.h /usr/local/cuda/include $ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 $ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*===Install GPU drivers===*7. Did not need to install the GPU drivers because we already had the correct version.===Install libcupti-dev library===*8.Tried to install the libcupti-dev library with: sudo apt-get install cuda-command-line-tools-9-0but apparently it was already installed. (How surprising!)===LD-LIBRARY_PATH environment variable modification===*9. Added the following path to the LD-LIBRARY_PATH environment variable by accessing bash as per above: export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}/usr/local/cuda/extras/CUPTI/lib64''===Install TensorRT 3.0 (optional)===*'''10.Did not install TensorRT 3.0 ===Problem encountered''':===

2. <s>When execute the following command in a terminal, it returns 'PATH: command not found'. $ export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}</s><br>3. <s>If installed correctly, type nvcc- V should verify installation. But currently it returns 'the program nvcc is currently not installed'.</s><br>4. When adding libcupti-dev library, after adding the path: export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}/usr/local/cuda/extras/CUPTI/lib64Upon source .bashrc, it returns the following: -bash: export: `:/usr/local/cuda-9.0/lib64:/usr/local/cuda-9.0/extras/CUPTI/lib64': not a valid identifier So far it does not affect the functionality of tensorflow, but it will probably affect libcupti-dev library. ==Tensorflow with GPU support Installation in a virtual environment==We followed this instruction here: https://www.tensorflow.org/install/install_linux#InstallingVirtualenv ===Installation===Install on DBServer under the user McNair. Password: askEd*1.install virtualenv:Surprise again! Someone already installed it! Did not install virtualenv again.*2. Create a directory for the virtual environment and choose python 3 interpreter mkdir ~/tensorflow # somewhere to work out of cd ~/tensorflow # Choose one of the following Python environments for the ./venv directory: virtualenv --system-site-packages -p python3 venv # Use Python 3.n '''NOTE: python2 DOES NOT WORK WITH GPU''' *3. Activate the Virtualenv environment: source ~/tensorflow/venv/bin/activate # bash*4. Upgrade pip: pip install -U pip*5. Install TensorFlow in the virtual environment: within pip install -U tensorflow-gpu*Validate the installation with: (venv)$ python -c "import tensorflow as tf; print(tf.__version__)" [[FILE:tensorflow.png]] ===Testing Tensorflow with GPU in virtual environment===Create a python file with the following: import tensorflow as tf sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))Run it in the virtual environment. [[FILE:TensorflowGPU.png]] ==Tensorflow with GPU support Installation as root for All users=='''Important note: Currently on DB Server, pip/pip3 is working with Python3.6 rather than Python3. Hence the following installs a copy of tensorflow-gpu 1.9.0 for Python3.6 for all users''' ===Installation===We followed instructions here: https://www.tensorflow.org/install/install_linux#InstallingNativePip *0. Deleted previously installed tensorflow with CPU support: sudo pip3 uninstall tensorflow*1. Used this command to install tensorflow-gpu: sudo pip3 install -U tensorflow-gpu ===Path variable (crucial)===If you logged on as a user using tensorflow for the first time, you need to set the CUDA Toolkit 9.0 environment variables. Type into terminal nano .bashrc Add the following: export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} Save and exit (CTRL + O and CTRL + X). Type source .bashrc  ===Testing Tensorflow with GPU as (non-root) user===After ssh onto DB Server, type the following command into a terminal: python3.6 -c "import tensorflow as tf; print(tf.__version__);sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))"

==Tensorflow Installation==*(TODO) To install tensorflow, follow this instruction here[[FILE: https://www.tensorflow.org/install/install_linux#InstallingVirtualenv and install tensorflow with Wei. Specific is belowTensorflowGPUGlobal.png]]