add readme for examples of running mnist using dataset and pipeline

examples/mnist/tf/README.md

@@ -0,0 +1,107 @@
+## Running distributed MNIST training / inference
+
+### _using Dataset_
+```bash
+# for CPU mode:
+# export QUEUE=default
+# remove references to $LIB_CUDA
+
+# hdfs dfs -rm -r mnist_model
+# hdfs dfs -rm -r predictions
+
+${SPARK_HOME}/bin/spark-submit \
+--master yarn \
+--deploy-mode cluster \
+--queue ${QUEUE} \
+--num-executors 4 \
+--executor-memory 27G \
+--py-files TensorFlowOnSpark/tfspark.zip,TensorFlowOnSpark/examples/mnist/tf/mnist_dist_dataset.py \
+--conf spark.dynamicAllocation.enabled=false \
+--conf spark.yarn.maxAppAttempts=1 \
+--archives hdfs:///user/${USER}/Python.zip#Python \
+--conf spark.executorEnv.LD_LIBRARY_PATH=$LIB_CUDA:$LIB_JVM:$LIB_HDFS \
+--driver-library-path=$LIB_CUDA \
+TensorFlowOnSpark/examples/mnist/tf/mnist_spark_dataset.py \
+${TF_ROOT}/${TF_VERSION}/examples/mnist/tf/mnist_spark_dataset.py \
+--images_labels mnist/csv2/train \
+--format csv2 \  
+--mode train \
+--model mnist_model
+
+# to use inference mode, change `--mode train` to `--mode inference` and add `--output predictions`
+# one item in csv2 format is `image | label`, to use input data in TFRecord format, change `--format csv` to `--format tfr`
+# to use infiniband, add `--rdma`
+```
+
+### _using QueueRunners_
+```bash
+# for CPU mode:
+# export QUEUE=default
+# remove references to $LIB_CUDA
+
+# hdfs dfs -rm -r mnist_model
+# hdfs dfs -rm -r predictions
+
+${SPARK_HOME}/bin/spark-submit \
+--master yarn \
+--deploy-mode cluster \
+--queue ${QUEUE} \
+--num-executors 4 \
+--executor-memory 27G \
+--py-files TensorFlowOnSpark/tfspark.zip,TensorFlowOnSpark/examples/mnist/tf/mnist_dist.py \
+--conf spark.dynamicAllocation.enabled=false \
+--conf spark.yarn.maxAppAttempts=1 \
+--archives hdfs:///user/${USER}/Python.zip#Python \
+--conf spark.executorEnv.LD_LIBRARY_PATH=$LIB_CUDA:$LIB_JVM:$LIB_HDFS \
+--driver-library-path=$LIB_CUDA \
+TensorFlowOnSpark/examples/mnist/tf/mnist_spark.py \
+--images mnist/tfr/train/images \
+--labels mnist/tfr/train/labels \
+--format csv \
+--mode train \
+--model mnist_model
+
+# to use inference mode, change `--mode train` to `--mode inference` and add `--output predictions`
+# to use input data in TFRecord format, change `--format csv` to `--format tfr`
+# to use infiniband, add `--rdma`
+```
+
+### _using Spark ML Pipeline_
+```bash
+# for CPU mode:
+# export QUEUE=default
+# remove references to $LIB_CUDA
+
+# hdfs dfs -rm -r mnist_model
+# hdfs dfs -rm -r mnist_export
+# hdfs dfs -rm -r tfrecords
+# hdfs dfs -rm -r predictions
+
+${SPARK_HOME}/bin/spark-submit \
+--master yarn \
+--deploy-mode cluster \
+--queue ${QUEUE} \
+--num-executors 4 \
+--executor-memory 27G \
+--jars hdfs:///user/${USER}/tensorflow-hadoop-1.0-SNAPSHOT.jar \  
+--py-files TensorFlowOnSpark/tfspark.zip,TensorFlowOnSpark/examples/mnist/tf/mnist_dist_pipeline.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_CUDA:$LIB_JVM:$LIB_HDFS \
+--driver-library-path=$LIB_CUDA \
+TensorFlowOnSpark/examples/mnist/tf/mnist_spark_pipeline.py \
+--images mnist/csv/train/images \
+--labels mnist/csv/train/labels \
+--tfrecord_dir tfrecords \
+--format csv \
+--model_dir mnist_model \
+--export_dir mnist_export \
+--train \
+--inference_mode signature \
+--inference_output predictions
+
+# to use input data in TFRecord format, change `--format csv` to `--format tfr`
+# tensorflow-hadoop-1.0-SNAPSHOT.jar is needed for transforming csv input to TFRecord 
+# `--tfrecord_dir` is needed for temporarily saving dataframe to TFRecord on hdfs
+```