Installing TensorFlow

Old

Currently installed with Anaconda Python 3.

https://stackoverflow.com/questions/36355073/upgrading-numpy-fails-with-permission-denied-error

https://www.tensorflow.org/install/install_windows

with cpu support only

https://www.tensorflow.org/install/install_linux

need to logoff other users via server manager

https://stackoverflow.com/questions/46499808/pip-throws-typeerror-parse-got-an-unexpected-keyword-argument-transport-enco#_=_

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

/usr/local/cuda-9.0 

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

HOME/MCNAIR/CUDA-SAMPLES.

• 2. Installed Patch 1, 2 and 3. The command to install was

sudo sh cuda_9.0.176.2_linux.run # (9.0.176.1 for patch 1 and 9.0.176.3 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

Add

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

/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.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
/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-9-0

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 Installation Resource

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

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__)"

Testing Tensorflow with GPU

Create a python file with the following:

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

Run it in the virtual environment.

Installing Tensorflow for All users

• 1. Used this command:

 sudo pip3 install -U tensorflow-gpu

Ran into a problem with python3.5 and pip3 compatibility issue. pip3 supported python3.6. We'll look into that tomorrow