Update README.md

centos7.6_tensorflow-gpu/README.md

@@ -124,19 +124,33 @@ export LIBRARY_PATH=/usr/local/cuda/lib64/:$LIBRARY_PATH
 # 3. Install TensorFlow-GPU
 
 ## 3.1 Install and Update Python
+1. Download the latest Anaconda from the [Anaconda website](https://www.anaconda.com/download/).
+`$ wget https://repo.anaconda.com/archive/Anaconda3-5.3.1-Linux-x86_64.sh`
 
+2. Install Anaconda with the downloaded binary.
+`$ bash Anaconda3-5.3.1-Linux-x86_64.sh `
+
+3. Downgrade python to 3.6 for compatability:
+```
+$ conda install python=3.6
+$ conda update --all
+```
 
 ## 3.2 Install tensorflow-gpu
 
+With all packages and libraries installed, this might be the easiest step:
+`$ pip install tensorflow-gpu`
 
 ## 3.3 Testing
 
-https://www.tensorflow.org/guide/using_gpu
+Finally, let's test whether the whole configuration works. According to the [documentation of tensorflow](
+https://www.tensorflow.org/guide/using_gpu), we can test whether the GPU works with a few lines of codes.
 
-
-Logging Device placement
+## Logging Device placement
 To find out which devices your operations and tensors are assigned to, create the session with log_device_placement configuration option set to True.
+
 ```
+import tensorflow as tf
 # Creates a graph.
 a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
 b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
@@ -145,8 +159,11 @@ c = tf.matmul(a, b)
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
 # Runs the op.
 print(sess.run(c))
+```
+
 You should see the following output:
 
+```
 Device mapping:
 /job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla K40c, pci bus
 id: 0000:05:00.0
@@ -155,4 +172,4 @@ a: /job:localhost/replica:0/task:0/device:GPU:0
 MatMul: /job:localhost/replica:0/task:0/device:GPU:0
 [[ 22.  28.]
  [ 49.  64.]]
-```
\ No newline at end of file
+```