tensorflow

Introduction:

TENSORFLOW is an interface for expressing machine learning
algorithms, and an implementation for executing such algorithms.

A computation expressed using TENSORFLOW can be
executed with little or no change on a wide variety of heterogeneous
systems, ranging from mobile devices such as phones
and tablets up to large-scale distributed systems of hundreds
of machines and thousands of computational devices such as
GPU cards. The system is flexible and can be used to express
a vast assortment of algorithms, including training and inference
algorithms for deep neural network models.

TENSORFLOW has been used for research and machine learning in areas such as

  • speech recognition;
  • computer vision;
  • robotics;
  • information retrieval;
  • natural language processing;
  • geographic information extraction;
  • computational drug discovery.

Web site:

The home page at tensorflow.org:

https://www.tensorflow.org/

Reference:

Usage:

TENSORFLOW 1.8 is available as a part of Anaconda/5.1.0. To use TENSORFLOW 1.8 you need to load the following modules on Cascades V100 nodes or Newriver P100 nodes:

      module purge
      module load Anaconda/5.1.0
      module load cuda/9.0.176 
      module load cudnn/7.1

For older versions, users will need to install them inside a virtual environment. Directions for doing so on the ARC clusters Cascades and NewRiver are given below.

Cascades Installation:

In this example, the user has decided to install TENSORFLOW 1.2 for Python 2.7. The user should log in interactively to Cascades and issue the following commands:

      module purge
      module load Anaconda/2.3.0
      module load cuda/8.0.44 
      module load cudnn/5.1

      # Type y and hit enter when prompted with "Proceed ([y]/n)?"
      conda create -n tfcascades anaconda

      source activate tfcascades

      # ignore the warning messages that will follow the next command...
     
      pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp27-none-linux_x86_64.whl

      source deactivate

Cascades Usage:

Assume TENSORFLOW has been installed as in the above example.
Suppose that the following simple Python code is to be executed:

      from __future__ import print_function
      import tensorflow as tf
      hello = tf.constant ( 'Hello, TensorFlow!' )
      sess = tf.Session ( )               # Start the tf session
      print ( sess.run ( hello ) )        # Run the session

Then a typical batch job to run the code under TENSORFLOW might be:

    #! /bin/bash
    #PBS -l procs=1,gpus=1
    #PBS -l walltime=00:02:00
    #PBS -q v100_normal_q
    #PBS -A yourallocationnamehere
    #PBS -W group_list=cascades
    #PBS -M youremail@vt.edu
    #PBS -m bea

    cd $PBS_O_WORKDIR

    module purge
    module load Anaconda/2.3.0
    module load cuda/8.0.44 
    module load cudnn/5.1

    source activate tfcascades
    python test.py

NewRiver Installation:

In this example, the user has decided to install TENSORFLOW 1.2 for Python 3.6. The user should log in interactively to Newriver and issue the following commands:

      module purge
      module load Anaconda/4.2.0
      module load cuda/8.0.44 
      module load cudnn/5.1

      # Type y and hit enter when prompted with "Proceed ([y]/n)?"
      conda create -n tfnewriver anaconda

      source activate tfnewriver

      # ignore the warning message that will follow the next command...

      pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp36-cp36m-linux_x86_64.whl

      source deactivate

NewRiver Usage:

Assume TENSORFLOW has been installed as in the above example.
Suppose that the following simple Python code is to be executed:

      from __future__ import print_function
      import tensorflow as tf
      hello = tf.constant ( 'Hello, TensorFlow!' )
      sess = tf.Session ( )               # Start the tf session
      print ( sess.run ( hello ) )        # Run the session

Then a typical batch job to run the code under TENSORFLOW might be:

    #! /bin/bash
    #PBS -l procs=1,gpus=1
    #PBS -l walltime=00:02:00
    #PBS -q p100_normal_q
    #PBS -A yourallocationnamehere
    #PBS -W group_list=newriver
    #PBS -M youremail@vt.edu
    #PBS -m bea

    cd $PBS_O_WORKDIR

    module purge
    module load Anaconda/4.2.0
    module load cuda/8.0.44 
    module load cudnn/5.1

    source activate tfnewriver
    python test.py

KERAS

Keras 2.1.6, https://keras.io/ is available as a part of Anaconda/5.1.0 on both Newriver and Cascades.

A complete set of files to carry out a similar process are available in
tensorflow_example.tar