Tensorflow Note - 1

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

Prerequisites

• You are using Ubuntu 16
• Your machine has Nvidia GPU card installed
• If you are using Ubuntu 14, the installation of CUDA and cuDNN will be a bit different. Please check Google’s instructions.

Install python3 and pip3

• Please find instructions here

Install virtualenv via pip3

pip3 install virtualenv

Create two tensorflow virtualenvs.

mkdir ~/.envs
virtualenv --system-site-packages ~/.envs/tf  ### CPU only
virtualenv --system-site-packages ~/.envs/tfgpu   ### GPU enabled

Install tensorflow for different virtualenvs

source ~/.envs/tf/bin/activate
source ~/.envs/tfgpu/bin/activate

pip3 install tensorflow ### CPU only
pip3 install tensorflow-gpu ### GPU enabled

Install CUDA and cuDNN for tensorflow-gpu

• Use following command to check you GPU information

  lspic -nn | grep '\[03' ]
  lshw -numeric -C display
  
  ### GPU info sample
  ### NVIDIA Corporation GM107M [GeForce GTX 850M]
  

• Download and install Nvidia driver based on above GPU info

  chmod +x NVIDIA-Linux-xxxx.run
  sudo ./NVIDIA-Linux-xxxx.run
  

• Download and install CUDA from NVIDIA

  sudo dpkg -i cuda-repo-xxxxx.deb
  sudo apt-get udpate
  sudo apt-get install cuda
  

• Setup CUDA_HOME

  ### CUDA
  export CUDA_HOME=/usr/local/cuda-8.0 
  export LD_LIBRARY_PATH=${CUDA_HOME}/lib64
  

• Download and install cuDNN for CUDA

  ### extra the cuDNN tar ball
  tar -xvf cudnn-8.0
  cd cuda 
  sudo cp lib64/* /usr/local/cuda-8.0/lib64
  sudo cp include/* /usr/local/cuda-8.0/include
  

Use sample code to test Tensorflow

• Save code below to file test.py

  import numpy as np
  import tensorflow as tf
  
  ### Model parameters
  W = tf.Variable([.3], tf.float32)
  b = tf.Variable([-.3], tf.float32)
  ### Model input and output
  x = tf.placeholder(tf.float32)
  linear_model = W * x + b
  y = tf.placeholder(tf.float32)
  ### loss
  loss = tf.reduce_sum(tf.square(linear_model - y)) ### sum of the squares
  ### optimizer
  optimizer = tf.train.GradientDescentOptimizer(0.01)
  train = optimizer.minimize(loss)
  ### training data
  x_train = [1,2,3,4]
  y_train = [0,-1,-2,-3]
  ### training loop
  init = tf.global_variables_initializer()
  sess = tf.Session()
  sess.run(init) ### reset values to wrong
  for i in range(1000):
  sess.run(train, {x:x_train, y:y_train})
  
  ### evaluate training accuracy
  curr_W, curr_b, curr_loss  = sess.run([W, b, loss], {x:x_train, y:y_train})
  print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))
  

• Test with tensorflow-gpu (GPU enabled)

  source ~/.envs/tfgpu/bin/activate
  python3 test.py
  
  ## You will probably see the result as follow 
  ## ....
  ## name: GeForce GTX 850M
  ## major: 5 minor: 0 memoryClockRate (GHz) 0.9015
  ## pciBusID 0000:0a:00.0
  ## Total memory: 3.95GiB
  ## Free memory: 3.58GiB
  ## 2017-04-25 10:25:59.640621: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 
  ## 2017-04-25 10:25:59.640626: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y 
  ## 2017-04-25 10:25:59.640640: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] 
  ### Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 850M, pci ## bus id: 0000:0a:00.0)
  ## W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11