1 files changed, 80 insertions, 3 deletions
diff --git a/sample-apps/blog-tutorial-shared/README.md b/sample-apps/blog-tutorial-shared/README.md
index 09ac61e6b56..846156908c3 100644
--- a/sample-apps/blog-tutorial-shared/README.md
+++ b/sample-apps/blog-tutorial-shared/README.md
@@ -1,6 +1,8 @@
 # Vespa tutorial utility scripts
 
-## From raw JSON to Vespa Feeding format
+## Vespa Tutorial pt. 1
+
+### From raw JSON to Vespa Feeding format
 
     $ python parse.py trainPosts.json > somefile.json
 
@@ -10,10 +12,85 @@ Parses JSON from the file trainPosts.json downloaded from Kaggle during the [blo
     
 Give it the flag "-p" or "--popularity", and the script also calculates and adds the field `popularity`, as introduced [in the tutorial](https://git.corp.yahoo.com/pages/vespa/documentation/documentation/tutorials/blog-search.html#blog-popularity-signal).
 
-## Building and running the Spark script for calculating latent factors
+## Vespa Tutorial pt. 2
+
+### Building and running the Spark script for calculating latent factors
 
 1. Install the latest version of [Apache Spark](http://spark.apache.org/) and [sbt](http://www.scala-sbt.org/download.html).
 
 2. Clone this repository and build the Spark script with `sbt package` (in the root directory of this repo).
 
-3. Use the resulting jar file when running spark jobs included in the tutorials.
-\ No newline at end of file
+3. Use the resulting jar file when running spark jobs included in the tutorials.
+
+## Vespa Tutorial pt.3
+
+Pre-computed data used through out the tutorial can be found [here](http://trdstorage.trondheim.corp.yahoo.com/~tmartins/vespa_tutorial_data/).
+
+You can download ```vespa_tutorial_data.tar.gz``` (144MB) and decompress it with
+
+    $ wget http://trdstorage.trondheim.corp.yahoo.com/~tmartins/vespa_tutorial_data.tar.gz
+    $ tar -xvzf vespa_tutorial_data.tar.gz 
+
+### Create Training Dataset
+
+    $ ./generateDataset.R -d vespa_tutorial_data/user_item_cf_cv/product.json \
+                          -u vespa_tutorial_data/user_item_cf_cv/user.json \
+                          -t vespa_tutorial_data/training_and_test_indices/train.txt \
+                          -o vespa_tutorial_data/nn_model/training_set.txt
+
+### Train model with TensorFlow
+
+Train the model with 
+
+    $ python vespaModel.py --product_features_file_path vespa_tutorial_data/user_item_cf_cv/product.json \
+                           --user_features_file_path vespa_tutorial_data/user_item_cf_cv/user.json \
+                           --dataset_file_path vespa_tutorial_data/nn_model/training_set.txt
+
+Model parameters and summary statistics will be saved at folder ```runs/${start_time}``` with ```${start_time}``` representing the time you started to train the model.
+
+Visualize the accuracy and loss metrics with
+    
+    $ tensorboard --logdir runs/1473845959/summaries/
+
+**Note**: The folder ```1473845959``` depends on the time you start to train the model and will be different in your case.
+
+### Export model parameters to Tensor Vespa format 
+
+```checkpoint_dir``` holds the folder that TensorFlow writes the learned model parameters (stored using protobuf) and ```output_dir``` is the folder that we will output the model parameters in 
+Vespa Tensor format.
+
+    import vespaModel
+
+    checkpoint_dir = "./runs/1473845959/checkpoints"
+    output_dir = "application_package/constants"
+       
+    serializer = serializeVespaModel(checkpoint_dir, output_dir)
+    serializer.serialize_to_disk(variable_name = "W_hidden", dimension_names = ['input', 'hidden'])
+    serializer.serialize_to_disk(variable_name = "b_hidden", dimension_names = ['hidden'])
+    serializer.serialize_to_disk(variable_name = "W_final", dimension_names = ['hidden', 'final'])
+    serializer.serialize_to_disk(variable_name = "b_final", dimension_names = ['final'])
+
+The python code containing the class ```serializeVespaModel``` can be found at: ```src/python/vespaModel.py```
+
+### Offline evaluation
+
+Query Vespa using the rank-profile ```tensor``` for users in the test set and return 100 blog post recommendations. Use those recommendations in the information contained in the test set to compute 
+metrics defined in the Tutorial pt. 2. 
+
+    pig -x local -f tutorial_compute_metric.pig \
+      -param VESPA_HADOOP_JAR=vespa-hadoop.jar \
+      -param TEST_INDICES=blog-job/training_and_test_indices/testing_set_ids \ 
+      -param ENDPOINT=$(hostname):8080
+      -param NUMBER_RECOMMENDATIONS=100
+      -param RANKING_NAME=tensor    
+      -param OUTPUT=blog-job/cf-metric
+
+Repeat the process, but now using the rank-profile ```nn_tensor```.
+
+    pig -x local -f tutorial_compute_metric.pig \
+      -param VESPA_HADOOP_JAR=vespa-hadoop.jar \
+      -param TEST_INDICES=blog-job/training_and_test_indices/testing_set_ids \ 
+      -param ENDPOINT=$(hostname):8080
+      -param NUMBER_RECOMMENDATIONS=100
+      -param RANKING_NAME=nn_tensor
+      -param OUTPUT=blog-job/cf-metric
+\ No newline at end of file