add criteo example

examples/criteo/README.md

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+# Learning Click-Through Rate at Scale with Tensorflow on Spark
+
+## Introduction 
+This project consists of learning a click-throughrate model at scale using TensorflowOnSpark technology.
+Criteo released a 1TB dataset: http://labs.criteo.com/2013/12/download-terabyte-click-logs/
+In order to promote Google cloud technology, Google published a solution to train a model at scale using there 
+proprietary platform : https://cloud.google.com/blog/big-data/2017/02/using-google-cloud-machine-learning-to-predict-clicks-at-scale 
+
+Instead, we propose a solution based on open source technology that can be leveraged on any cloud, 
+or private cluster relying on spark.
+
+We demonstrate how Tensorflow on Spark (https://github.com/yahoo/TensorFlowOnSpark) can be used to reach the state of the art when it comes to predicting the proba of click at scale.
+Notice that the goal here is not to produce the best pCTR predictor, but rather establish a open method that still reaches the best performance published so far on this dataset.
+Hence, our solutions remains very simple, and rely solely on basic feature extraction, cross-features and hashing, the all trained on logistic regression.
+
+## Install and test TF on spark
+Before making use of this code, please make sure you can install TF on spark on your cluster and
+run the mnist example as illustrated here:
+https://github.com/yahoo/TensorFlowOnSpark/wiki/GetStarted_YARN
+By so doing, you should make sure that did set up the following variables correctly:
+
+```
+export JAVA_HOME=
+export HADOOP_HOME=
+export SPARK_HOME=
+export HADOOP_HDFS_HOME=
+export SPARK_HOME=
+export PYTHON_ROOT=./Python
+export PATH=${PATH}:${HADOOP_HOME}/bin:${SPARK_HOME}/bin:${HADOOP_HDFS_HOME}/bin:${SPARK_HOME}/bin:${PYTHON_ROOT}/bin
+export PYSPARK_PYTHON=${PYTHON_ROOT}/bin/python
+export SPARK_YARN_USER_ENV="PYSPARK_PYTHON=/usr/bin/python"
+export QUEUE=default
+export LIB_HDFS=
+export LIB_JVM=
+```
+
+## Data set
+
+The raw data can be accessed here: http://labs.criteo.com/2013/12/download-terabyte-click-logs/
+
+### Download the data set
+```
+for i in 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23; do
+	curl -O http://azuremlsampleexperiments.blob.core.windows.net/criteo/day_${i}.gz
+	aws s3 mv  day_${i}.gz s3://criteo-display-ctr-dataset/released/
+done
+```
+
+### Upload training data on your AWS s3 using Pig
+
+```
+%declare awskey yourkey
+%declare awssecretkey yoursecretkey
+SET mapred.output.compress 'true';
+SET mapred.output.compression.codec 'org.apache.hadoop.io.compress.BZip2Codec';
+train_data = load 's3n://${awskey}:${awssecretkey}@criteo-display-ctr-dataset/released/day_{[0-9],1[0-9],2[0-2]}.gz ';
+train_data = FOREACH (GROUP train_data BY ROUND(10000* RANDOM()) PARALLEL 10000) GENERATE FLATTEN(train_data); 
+store train_data into 's3n://${awskey}:${awssecretkey}@criteo-display-ctr-dataset/data/training/' using PigStorage();
+```
+We here divide the training data in 10000 chunks, which will allow TFonSpark to reduce its memory usage.
+
+### Upload validation data on your AWS s3 using Pig
+```
+%declare awskey yourkey
+%declare awssecretkey yoursecretkey
+SET mapred.output.compress 'true';
+SET mapred.output.compression.codec 'org.apache.hadoop.io.compress.BZip2Codec';
+train_data = load 's3n://${awskey}:${awssecretkey}@criteo-display-ctr-dataset/released/day_23.gz';
+train_data = FOREACH (GROUP train_data BY ROUND(100* RANDOM()) PARALLEL 100) GENERATE FLATTEN(train_data); 
+store train_data into 's3n://${awskey}:${awssecretkey}@criteo-display-ctr-dataset/data/validation' using PigStorage();
+```
+
+
+
+
+
+
+## Running the example
+
+Set up task variables 
+```
+export TRAINING_DATA=hdfs_path_to_training_data_directory
+export VALIDATION_DATA=hdfs_path_to_validation_data_directory
+export MODEL_OUTPUT=hdfs://default/tmp/criteo_ctr_prediction
+```
+Run command:
+
+```
+${SPARK_HOME}/bin/spark-submit \
+--master yarn \
+--deploy-mode cluster \
+--queue ${QUEUE} \
+--num-executors 12 \
+--executor-memory 27G \
+--py-files TensorFlowOnSpark/tfspark.zip,TensorFlowOnSpark/examples/criteo/spark/criteo_dist.py \
+--conf spark.dynamicAllocation.enabled=false \
+--conf spark.yarn.maxAppAttempts=1 \
+--archives hdfs:///user/${USER}/Python.zip#Python \
+--conf spark.executorEnv.LD_LIBRARY_PATH="$LIB_HDFS:$LIB_JVM" \
+--conf spark.executorEnv.HADOOP_HDFS_HOME="$HADOOP_HDFS_HOME" \
+--conf spark.executorEnv.CLASSPATH="$($HADOOP_HOME/bin/hadoop classpath --glob):${CLASSPATH}" \
+TensorFlowOnSpark/examples/criteo/spark/criteo_spark.py \
+--mode train \
+--data ${TRAINING_DATA} \
+--validation ${VALIDATION_DATA} \
+--steps 1000000 \
+--model ${MODEL_OUTPUT} --tensorboard \
+--tensorboardlogdir ${MODEL_OUTPUT}
+```
+## Tensorboard tracking:
+
+By connecting to the Web UI tracker of your application,
+you be able to retrieve the tensorboard URL in the stdout of the driver:
+
+```
+ TensorBoard running at:       http://10.4.112.234:36911
+```
+
+You can then track the training loss, and validation loss:
+
+
+![Alt Text](resources/data/TensorBoard-TFonSpark-Criteo-04.png)
+
+