MultiGPU Training Example (#387)

* fix ml intro

* fix ml intro

* add multi gpu example

* add multi gpu example

README.md

@@ -13,40 +13,42 @@ It is suitable for beginners who want to find clear and concise examples about T
 - [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb).
 
 #### 1 - Introduction
-- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow 2.0.
-- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow 2.0 basic operations.
+- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow 2.0+.
+- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow 2.0+ basic operations.
 
 #### 2 - Basic Models
-- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0.
-- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0.
-- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0.
+- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0+.
+- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0+.
+- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0+.
 - **GBDT (Gradient Boosted Decision Trees)** ([notebooks](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb)). Implement a Gradient Boosted Decision Trees with TensorFlow 2.0+ to predict house value using Boston Housing dataset.
 
 #### 3 - Neural Networks
 ##### Supervised
 
 - **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset.
 - **Simple Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)). Raw implementation of a simple neural network to classify MNIST digits dataset.
-- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset.
+- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0+ 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset.
 - **Convolutional Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)). Raw implementation of a convolutional neural network to classify MNIST digits dataset.
 - **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API.
-- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API.
-- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0 'layers' and 'model' API.
+- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0+ 'layers' and 'model' API.
+- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0+ 'layers' and 'model' API.
 
 ##### Unsupervised
 - **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb)). Build an auto-encoder to encode an image to a lower dimension and re-construct it.
 - **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise.
 
 #### 4 - Utilities
-- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0.
-- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models.
+- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0+.
+- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0+ Models.
 - **Tensorboard** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb)). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard.
 
 #### 5 - Data Management
 - **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...).
-- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0.
-- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training.
+- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0+.
+- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0+, to generate distorted images for training.
 
+#### 6 - Hardware
+ **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset.
 
 ## TensorFlow v1
 

tensorflow_v2/README.md

@@ -41,6 +41,9 @@
 - **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0.
 - **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training.
 
+#### 6 - Hardware
+ **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset.
+
 ## Installation
 
 To install TensorFlow 2.0, simply run: