Difference between revisions of "Installing TensorFlow"

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Line 128: Line 128:
  python3.6 -c "import tensorflow as tf; print(tf.__version__);sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))"
  python3.6 -c "import tensorflow as tf; print(tf.__version__);sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))"

Revision as of 23:25, 12 July 2018


Currently installed with Anaconda Python 3.



with cpu support only


need to logoff other users via server manager


New (by Wei and Minh): Tensorflow 1.9.0 with GPU Installation Log

Important note: 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.

NVIDIA configuration

(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 Installer with the Runfile option. The toolkit is in

for the toolkit. Did NOT install NVDIA accelerated Graphics Driver for Linux-x86_64 384.81 (We believe we have a different graphic driver. we have a much Newer version(396.26)). Installed the CUDA 9.0 samples in

  • 2. Installed Patch 1, 2 and 3. The command to install was
sudo sh cuda_9.0.176.2_linux.run # ( for patch 1 and for patch 3)
  • 3. Set up the environment variables:

The PATH variable needs to include /usr/local/cuda-9.0/bin To add this path to the PATH variable:

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

In addition, when using the runfile installation method, the LD_LIBRARY_PATH variable needs to contain /usr/local/cuda-9.0/lib64 on a 64-bit system To change the environment variables for 64-bit operating systems:

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.
To accomplish this:

nano /home/mcnair/.bashrc


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


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

your cuDNN download path is referred to as


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.tgz

c. 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

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-9-0

but 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

1. In usr/local/ we found files 'CUDA-9.2' and 'CUDA-8.0'. These were probably installed in the past.
2. When execute the following command in a terminal, it returns 'PATH: command not found'.

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

3. If installed correctly, type nvcc- V should verify installation. But currently it returns 'the program nvcc is currently not installed'.
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/lib64

Upon 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

To install tensorflow, follow this instruction here: https://www.tensorflow.org/install/install_linux#InstallingVirtualenv and install tensorflow.

Install Tensorflow using the Virtual Environment

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


  • 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__)"


Testing Tensorflow with GPU in virtual environment

Create a python file with the following:

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Run it in the virtual environment. TensorflowGPU.png

Installing Tensorflow 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

  • 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

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))"