predictionio-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From don...@apache.org
Subject [06/12] incubator-predictionio git commit: [PIO-24] Update donated template links
Date Fri, 16 Dec 2016 18:03:21 GMT
[PIO-24] Update donated template links


Project: http://git-wip-us.apache.org/repos/asf/incubator-predictionio/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-predictionio/commit/df568b6d
Tree: http://git-wip-us.apache.org/repos/asf/incubator-predictionio/tree/df568b6d
Diff: http://git-wip-us.apache.org/repos/asf/incubator-predictionio/diff/df568b6d

Branch: refs/heads/develop
Commit: df568b6d505812928b59a662408d90119d524173
Parents: 8d80086
Author: Donald Szeto <donald@apache.org>
Authored: Sat Oct 8 16:38:54 2016 -0700
Committer: Donald Szeto <donald@apache.org>
Committed: Sat Oct 8 16:38:54 2016 -0700

----------------------------------------------------------------------
 docs/manual/source/demo/tapster.html.md         |  2 +-
 .../source/demo/textclassification.html.md.erb  |  2 +-
 docs/manual/source/gallery/templates.yaml       | 18 +++++++--------
 .../dimensionalityreduction.html.md             | 24 ++++++++++----------
 .../quickstart/_create_engine.html.md.erb       |  2 +-
 .../classification/quickstart.html.md.erb       |  2 +-
 .../quickstart.html.md.erb                      |  2 +-
 .../quickstart.html.md.erb                      |  2 +-
 .../quickstart.html.md.erb                      |  2 +-
 .../leadscoring/quickstart.html.md.erb          |  2 +-
 .../productranking/quickstart.html.md.erb       |  2 +-
 .../recommendation/quickstart.html.md.erb       |  2 +-
 .../similarproduct/quickstart.html.md.erb       |  2 +-
 .../templates/vanilla/quickstart.html.md.erb    |  2 +-
 14 files changed, 33 insertions(+), 33 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/demo/tapster.html.md
----------------------------------------------------------------------
diff --git a/docs/manual/source/demo/tapster.html.md b/docs/manual/source/demo/tapster.html.md
index bd63f8a..8593fc9 100644
--- a/docs/manual/source/demo/tapster.html.md
+++ b/docs/manual/source/demo/tapster.html.md
@@ -97,7 +97,7 @@ We are going to copy the Similar Product Template into the PIO directory.
 
 ```
 $ cd PredictionIO
-$ pio template get PredictionIO/template-scala-parallel-similarproduct tapster-episode-similar
+$ pio template get apache/incubator-predictionio-template-similar-product tapster-episode-similar
 ```
 
 Next we are going to update the App ID in the ‘engine.json’ file to match the App ID
we just created.

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/demo/textclassification.html.md.erb
----------------------------------------------------------------------
diff --git a/docs/manual/source/demo/textclassification.html.md.erb b/docs/manual/source/demo/textclassification.html.md.erb
index d08964e..8ac5579 100644
--- a/docs/manual/source/demo/textclassification.html.md.erb
+++ b/docs/manual/source/demo/textclassification.html.md.erb
@@ -24,7 +24,7 @@ You should also download the engine template named Text Classification Engine
 that accompanies this tutorial by cloning the template repository:
 
 ```
-pio template get PredictionIO/template-scala-parallel-textclassification < Your new engine
directory >
+pio template get apache/incubator-predictionio-template-text-classifier < Your new engine
directory >
 ```
 
 

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/gallery/templates.yaml
----------------------------------------------------------------------
diff --git a/docs/manual/source/gallery/templates.yaml b/docs/manual/source/gallery/templates.yaml
index 765719e..80a69e3 100644
--- a/docs/manual/source/gallery/templates.yaml
+++ b/docs/manual/source/gallery/templates.yaml
@@ -20,7 +20,7 @@
 
 - template:
     name: E-Commerce Recommendation
-    repo: "https://github.com/PredictionIO/template-scala-parallel-ecommercerecommendation"
+    repo: "https://github.com/apache/incubator-predictionio-template-ecom-recommender"
     description: |-
       This engine template provides personalized recommendation for e-commerce applications
with the following features by default:
 
@@ -37,7 +37,7 @@
 
 - template:
     name: E-Commerce Recommendation (Java)
-    repo: "https://github.com/PredictionIO/template-java-parallel-ecommercerecommendation"
+    repo: "https://github.com/apache/incubator-predictionio-template-java-ecom-recommender"
     description: |-
       This engine template provides personalized recommendation for e-commerce applications
with the following features by default:
 
@@ -66,7 +66,7 @@
 
 - template:
     name: Similar Product
-    repo: "https://github.com/PredictionIO/template-scala-parallel-similarproduct"
+    repo: "https://github.com/apache/incubator-predictionio-template-similar-product"
     description: |-
        This engine template recommends products that are "similar" to the input product(s).
Similarity is not defined by user or item attributes but by users' previous actions. By default,
it uses 'view' action such that product A and B are considered similar if most users who view
A also view B. The template can be customized to support other action types such as buy, rate,
like..etc
     tags: [recommender]
@@ -102,7 +102,7 @@
 
 - template:
     name: Recommendation
-    repo: "https://github.com/PredictionIO/template-scala-parallel-recommendation"
+    repo: "https://github.com/apache/incubator-predictionio-template-recommender"
     description: |-
       An engine template is an almost-complete implementation of an engine. PredictionIO's
Recommendation Engine Template has integrated Apache Spark MLlib's Collaborative Filtering
algorithm by default. You can customize it easily to fit your specific needs.
     tags: [unsupervised]
@@ -114,7 +114,7 @@
 
 - template:
     name: Classification
-    repo: "https://github.com/PredictionIO/template-scala-parallel-classification"
+    repo: "https://github.com/apache/incubator-predictionio-template-attribute-based-classifier"
     description: |-
       An engine template is an almost-complete implementation of an engine. PredictionIO's
Classification Engine Template has integrated Apache Spark MLlib's Naive Bayes algorithm by
default.
     tags: [classification]
@@ -397,7 +397,7 @@
 
 - template:
     name: Text Classification
-    repo: "https://github.com/PredictionIO/template-scala-parallel-textclassification"
+    repo: "https://github.com/apache/incubator-predictionio-template-text-classifier"
     description: |-
       Use this engine for general text classification purposes. Uses OpenNLP library for
text vectorization, includes t.f.-i.d.f.-based feature transformation and reduction, and uses
Spark MLLib's Multinomial Naive Bayes implementation for classification.
     tags: [nlp]
@@ -504,10 +504,10 @@
     pio_min_version: 0.9.5
 
 - template:
-    name: Vanilla
-    repo: "https://github.com/PredictionIO/template-scala-parallel-vanilla"
+    name: Skeleton
+    repo: "https://github.com/apache/incubator-predictionio-template-skeleton"
     description: |-
-      Vanilla template is for developing new engine when you find other engine templates
do not fit your needs. This template provides a skeleton to kick start new engine development.
+      Skeleton template is for developing new engine when you find other engine templates
do not fit your needs. This template provides a skeleton to kick start new engine development.
     tags: [other]
     type: Parallel
     language: Scala

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/machinelearning/dimensionalityreduction.html.md
----------------------------------------------------------------------
diff --git a/docs/manual/source/machinelearning/dimensionalityreduction.html.md b/docs/manual/source/machinelearning/dimensionalityreduction.html.md
index 042eb19..fd26577 100644
--- a/docs/manual/source/machinelearning/dimensionalityreduction.html.md
+++ b/docs/manual/source/machinelearning/dimensionalityreduction.html.md
@@ -19,33 +19,33 @@ In machine learning, specifically in [supervised learning](http://en.wikipedia.o
 | ![Square Samples](/images/machinelearning/featureselection/square100.png) | ![Cube Samples](/images/machinelearning/featureselection/cube100.png)
|
 |                                                          |                            
                         |
 
-Dimensionality reduction is the process of applying a transformation to your feature vectors
in order to produce a vector with the same or less number of features. Principal component
Analysis (PCA) is a technique for dimensionality reduction. This can be treated as a data
processing technique, and so with respect to the [DASE](/customize/) framework, it will fall
into the Data Preparator engine component. 
+Dimensionality reduction is the process of applying a transformation to your feature vectors
in order to produce a vector with the same or less number of features. Principal component
Analysis (PCA) is a technique for dimensionality reduction. This can be treated as a data
processing technique, and so with respect to the [DASE](/customize/) framework, it will fall
into the Data Preparator engine component.
 
 This guide will also help to solidify the concept of taking an engine template and customizing
it for a particular use case: hand-written numeric digit recognition.
 
 ## Data Example
 
-As a guiding example, a base data set, the [MNIST digit recognition dataset](https://www.kaggle.com/c/digit-recognizer/data),
is used. This is a perfect data set for dimensionality reduction, for, in this data set, the
features that will be used for learning are pixel entries in a \\(28 \times 28\\) pixel image.
There is really no direct interpretation of any one feature, so that you do not lose anything
in applying a transformation that will treat the features as [linear combinations](https://en.wikipedia.org/wiki/Linear_combination)
of some set "convenient" vectors. 
+As a guiding example, a base data set, the [MNIST digit recognition dataset](https://www.kaggle.com/c/digit-recognizer/data),
is used. This is a perfect data set for dimensionality reduction, for, in this data set, the
features that will be used for learning are pixel entries in a \\(28 \times 28\\) pixel image.
There is really no direct interpretation of any one feature, so that you do not lose anything
in applying a transformation that will treat the features as [linear combinations](https://en.wikipedia.org/wiki/Linear_combination)
of some set "convenient" vectors.
 
 Now, we first pull the [classification engine template](/gallery/template-gallery/#classification)
via the following bash line
 
 ```
-pio template get PredictionIO/template-scala-parallel-classification <Your new engine
directory>
+pio template get apache/incubator-predictionio-template-attribute-based-classifier <Your
new engine directory>
 ```
 
 You should immediately be prompted with the following message:
 
 ```
-Please enter the template's Scala package name (e.g. com.mycompany): 
+Please enter the template's Scala package name (e.g. com.mycompany):
 ```
 
-Go ahead and input `FeatureReduction`, and feel free to just press enter for the remaining
message prompts. For the remainder of this guide, you will be working in your new engine directory,
so go ahead and `cd` into your new engine directory. At this point, go ahead and run the command

+Go ahead and input `FeatureReduction`, and feel free to just press enter for the remaining
message prompts. For the remainder of this guide, you will be working in your new engine directory,
so go ahead and `cd` into your new engine directory. At this point, go ahead and run the command
 
 ```
 pio build
-``` 
+```
 
-This will make sure that the PredictionIO dependency version for your project matches the
version installed on your computer. Now, download the MNIST `train.csv` data set from the
link above, and put this file in the `data` directory contained in the new engine directory.

+This will make sure that the PredictionIO dependency version for your project matches the
version installed on your computer. Now, download the MNIST `train.csv` data set from the
link above, and put this file in the `data` directory contained in the new engine directory.
 
 ### **Optional**: Visualizing Observations
 
@@ -165,7 +165,7 @@ PCA begins with the data matrix \\(\bf X\\) whose rows are feature vectors
corre
 
 **Input:** \\(N \times p\\) data matrix \\(\bf X\\); \\(k \leq p\\), the number of desired
features.
 
-**1.** For each column in the data matrix: compute the average of all the entries contained
in the column, and then subtract this average from each of the column entries. 
+**1.** For each column in the data matrix: compute the average of all the entries contained
in the column, and then subtract this average from each of the column entries.
 
 **2.** Compute the \\(k\\) eigenvectors corresponding to the \\(k\\) largest eigenvalues
of the matrix obtained in the first step.
 
@@ -271,7 +271,7 @@ The motivation for defining the `Observation` class is to make it easy
to mainta
 
 ### Preparator Modifications
 
-Remember that the Data Preparator is the engine component that takes care of the necessary
data processing prior to the fitting of a predictive model in the Algorithm component. Hence
this stage is where you will implement PCA. 
+Remember that the Data Preparator is the engine component that takes care of the necessary
data processing prior to the fitting of a predictive model in the Algorithm component. Hence
this stage is where you will implement PCA.
 
 To make sure there is no confusion, replace the import statements in the `Preparator.scala`
script with the following:
 
@@ -293,7 +293,7 @@ numFeatures : Int
 ) extends Params
 ```
 
-The next step is to implement the algorithm discussed in the above digression. This will
all be done in the `PreparedData` class. 
+The next step is to implement the algorithm discussed in the above digression. This will
all be done in the `PreparedData` class.
 
 Remember that the classes `Observation` and `Query` store the pixel features as a string
separated by `", "`. Hence, for data processing, you first need a function, `string2Vector`,
that will transform the feature strings to vectors. Now, you will need a function, `scaler`,
that centers your observations (step 1 in PCA algorithm). Luckily, the `StandardScaler` and
`StandardScalerModel` classes implemented in Spark MLLib can easily take care of this for
you. The last part will be to actually compute the SVD of the data matrix which can also be
easily done in MLLib. All this will be implemented in the `PreparedData` class which you will
redefine as follows:
 
@@ -353,11 +353,11 @@ The Data Preparator engine component is now complete, and we can move
on to the
 
 ### Algorithm Modifications
 
-The default algorithm used in the classification template is Naive Bayes. Now, this is a
[probabilistic classifier](https://en.wikipedia.org/wiki/Probabilistic_classification) that
makes certain assumptions about the data that do not really match the format of the PCA-transformed
data. In particular, it assumes that the vectors consist of counts. In particular, this means
it assumes non-negative feature values. However, upon applying PCA on the data, you have no
guarantees that you will have purely non-negative features. Given this, you will delete the
script `NaiveBayesAlgorithm.scala`, and create one called `LRAlgorithm.scala` (in the `src/main/scala/`
directory) which implements [Multinomial Logistic Regression](https://en.wikipedia.org/wiki/Multinomial_logistic_regression).

+The default algorithm used in the classification template is Naive Bayes. Now, this is a
[probabilistic classifier](https://en.wikipedia.org/wiki/Probabilistic_classification) that
makes certain assumptions about the data that do not really match the format of the PCA-transformed
data. In particular, it assumes that the vectors consist of counts. In particular, this means
it assumes non-negative feature values. However, upon applying PCA on the data, you have no
guarantees that you will have purely non-negative features. Given this, you will delete the
script `NaiveBayesAlgorithm.scala`, and create one called `LRAlgorithm.scala` (in the `src/main/scala/`
directory) which implements [Multinomial Logistic Regression](https://en.wikipedia.org/wiki/Multinomial_logistic_regression).
 
 The implementation details are not discussed in this guide, as the point of this guide is
to show how to incorporate **dimensionality reduction** techniques by incorporating PCA. The
latter paragraph is mentioned in order to emphasize the fact that applying the PCA transformation
(or possibly other dimensionality reduction techniques) will largely remove the interpretability
of features, so that model assumptions relying on such interpretations may no longer be satisfied.
This is just something to keep in mind.
 
-The following code is taken from the [text classification engine template](/gallery/template-gallery/#classification)
and adapted to match the project definitions.  Copy and paste into the new scala script, `LRAlgorithm.scala`:

+The following code is taken from the [text classification engine template](/gallery/template-gallery/#classification)
and adapted to match the project definitions.  Copy and paste into the new scala script, `LRAlgorithm.scala`:
 
 ```scala
 package FeatureReduction

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/partials/shared/quickstart/_create_engine.html.md.erb
----------------------------------------------------------------------
diff --git a/docs/manual/source/partials/shared/quickstart/_create_engine.html.md.erb b/docs/manual/source/partials/shared/quickstart/_create_engine.html.md.erb
index fd7c7dc..d17be2e 100644
--- a/docs/manual/source/partials/shared/quickstart/_create_engine.html.md.erb
+++ b/docs/manual/source/partials/shared/quickstart/_create_engine.html.md.erb
@@ -1,7 +1,7 @@
 Now let's create a new engine called *<%= engine_name %>* by downloading the <%=
template_name %>. Go to a directory where you want to put your engine and run the following:
 
 ```
-$ pio template get PredictionIO/<%= template_repo %> <%= engine_name %>
+$ pio template get <%= template_repo %> <%= engine_name %>
 $ cd <%= engine_name %>
 ```
 

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/templates/classification/quickstart.html.md.erb
----------------------------------------------------------------------
diff --git a/docs/manual/source/templates/classification/quickstart.html.md.erb b/docs/manual/source/templates/classification/quickstart.html.md.erb
index fe75aca..f7554ee 100644
--- a/docs/manual/source/templates/classification/quickstart.html.md.erb
+++ b/docs/manual/source/templates/classification/quickstart.html.md.erb
@@ -43,7 +43,7 @@ WARNING: for version < v0.3.1, it is array of features values
 
 ## 2. Create a new Engine from an Engine Template
 
-<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyClassification',
template_name: 'Classification Engine Template', template_repo: 'template-scala-parallel-classification'
} %>
+<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyClassification',
template_name: 'Classification Engine Template', template_repo: 'apache/incubator-predictionio-template-attribute-based-classifier'
} %>
 
 ## 3. Generate an App ID and Access Key
 

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/templates/complementarypurchase/quickstart.html.md.erb
----------------------------------------------------------------------
diff --git a/docs/manual/source/templates/complementarypurchase/quickstart.html.md.erb b/docs/manual/source/templates/complementarypurchase/quickstart.html.md.erb
index b9eb964..d1c38b7 100644
--- a/docs/manual/source/templates/complementarypurchase/quickstart.html.md.erb
+++ b/docs/manual/source/templates/complementarypurchase/quickstart.html.md.erb
@@ -33,7 +33,7 @@ NOTE: You can customize to use other event.
 
 ## 2. Create a new Engine from an Engine Template
 
-<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyComplementaryPurchase',
template_name: 'Complementary Purchase Engine Template', template_repo: 'template-scala-parallel-complementarypurchase'
} %>
+<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyComplementaryPurchase',
template_name: 'Complementary Purchase Engine Template', template_repo: 'PredictionIO/template-scala-parallel-complementarypurchase'
} %>
 
 ## 3. Generate an App ID and Access Key
 

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/templates/ecommercerecommendation/quickstart.html.md.erb
----------------------------------------------------------------------
diff --git a/docs/manual/source/templates/ecommercerecommendation/quickstart.html.md.erb b/docs/manual/source/templates/ecommercerecommendation/quickstart.html.md.erb
index 57f8c46..4ce81ec 100644
--- a/docs/manual/source/templates/ecommercerecommendation/quickstart.html.md.erb
+++ b/docs/manual/source/templates/ecommercerecommendation/quickstart.html.md.erb
@@ -53,7 +53,7 @@ Likewise, if a blacklist is provided, the engine will exclude those products
in
 
 ## 2. Create a new Engine from an Engine Template
 
-<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyECommerceRecommendation',
template_name: 'E-Commerce Recommendation Engine Template', template_repo: 'template-scala-parallel-ecommercerecommendation'
} %>
+<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyECommerceRecommendation',
template_name: 'E-Commerce Recommendation Engine Template', template_repo: 'apache/incubator-predictionio-template-ecom-recommender'
} %>
 
 ## 3. Generate an App ID and Access Key
 

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/templates/javaecommercerecommendation/quickstart.html.md.erb
----------------------------------------------------------------------
diff --git a/docs/manual/source/templates/javaecommercerecommendation/quickstart.html.md.erb
b/docs/manual/source/templates/javaecommercerecommendation/quickstart.html.md.erb
index 9b2de9b..58d3893 100644
--- a/docs/manual/source/templates/javaecommercerecommendation/quickstart.html.md.erb
+++ b/docs/manual/source/templates/javaecommercerecommendation/quickstart.html.md.erb
@@ -53,7 +53,7 @@ Likewise, if a blacklist is provided, the engine will exclude those products
in
 
 ## 2. Create a new Engine from an Engine Template
 
-<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyECommerceRecommendation',
template_name: 'E-Commerce Recommendation Engine Template', template_repo: 'template-java-parallel-ecommercerecommendation'
} %>
+<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyECommerceRecommendation',
template_name: 'E-Commerce Recommendation Engine Template', template_repo: 'apache/incubator-predictionio-template-java-ecom-recommender'
} %>
 
 ## 3. Generate an App ID and Access Key
 

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/templates/leadscoring/quickstart.html.md.erb
----------------------------------------------------------------------
diff --git a/docs/manual/source/templates/leadscoring/quickstart.html.md.erb b/docs/manual/source/templates/leadscoring/quickstart.html.md.erb
index 8a66bbb..3b8eb7d 100644
--- a/docs/manual/source/templates/leadscoring/quickstart.html.md.erb
+++ b/docs/manual/source/templates/leadscoring/quickstart.html.md.erb
@@ -38,7 +38,7 @@ NOTE: You can customize what the "conversion" event is. It's "buy" item
event by
 
 ## 2. Create a new Engine from an Engine Template
 
-<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyLeadScoring',
template_name: 'Lead Scoring Engine Template', template_repo: 'template-scala-parallel-leadscoring'
} %>
+<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyLeadScoring',
template_name: 'Lead Scoring Engine Template', template_repo: 'PredictionIO/template-scala-parallel-leadscoring'
} %>
 
 ## 3. Generate an App ID and Access Key
 

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/templates/productranking/quickstart.html.md.erb
----------------------------------------------------------------------
diff --git a/docs/manual/source/templates/productranking/quickstart.html.md.erb b/docs/manual/source/templates/productranking/quickstart.html.md.erb
index 1761780..33862e2 100644
--- a/docs/manual/source/templates/productranking/quickstart.html.md.erb
+++ b/docs/manual/source/templates/productranking/quickstart.html.md.erb
@@ -34,7 +34,7 @@ INFO: This template can easily be customized to consider more user events
such a
 
 ## 2. Create a new Engine from an Engine Template
 
-<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyProductRanking',
template_name: 'Product Ranking Engine Template', template_repo: 'template-scala-parallel-productranking'
} %>
+<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyProductRanking',
template_name: 'Product Ranking Engine Template', template_repo: 'PredictionIO/template-scala-parallel-productranking'
} %>
 
 ## 3. Generate an App ID and Access Key
 

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/templates/recommendation/quickstart.html.md.erb
----------------------------------------------------------------------
diff --git a/docs/manual/source/templates/recommendation/quickstart.html.md.erb b/docs/manual/source/templates/recommendation/quickstart.html.md.erb
index f84e9d1..11e6292 100644
--- a/docs/manual/source/templates/recommendation/quickstart.html.md.erb
+++ b/docs/manual/source/templates/recommendation/quickstart.html.md.erb
@@ -38,7 +38,7 @@ NOTE: You can customize to use other event.
 
 ## 2. Create a new Engine from an Engine Template
 
-<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyRecommendation',
template_name: 'Recommendation Engine Template', template_repo: 'template-scala-parallel-recommendation'
} %>
+<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyRecommendation',
template_name: 'Recommendation Engine Template', template_repo: 'apache/incubator-predictionio-template-recommender'
} %>
 
 ## 3. Generate an App ID and Access Key
 

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/templates/similarproduct/quickstart.html.md.erb
----------------------------------------------------------------------
diff --git a/docs/manual/source/templates/similarproduct/quickstart.html.md.erb b/docs/manual/source/templates/similarproduct/quickstart.html.md.erb
index 82af17f..ed3e851 100644
--- a/docs/manual/source/templates/similarproduct/quickstart.html.md.erb
+++ b/docs/manual/source/templates/similarproduct/quickstart.html.md.erb
@@ -52,7 +52,7 @@ Likewise, if a black-list is provided, the engine will exclude those products
in
 
 ## 2. Create a new Engine from an Engine Template
 
-<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MySimilarProduct',
template_name: 'Similar Product Engine Template', template_repo: 'template-scala-parallel-similarproduct'
} %>
+<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MySimilarProduct',
template_name: 'Similar Product Engine Template', template_repo: 'apache/incubator-predictionio-template-similar-product'
} %>
 
 ## 3. Generate an App ID and Access Key
 

http://git-wip-us.apache.org/repos/asf/incubator-predictionio/blob/df568b6d/docs/manual/source/templates/vanilla/quickstart.html.md.erb
----------------------------------------------------------------------
diff --git a/docs/manual/source/templates/vanilla/quickstart.html.md.erb b/docs/manual/source/templates/vanilla/quickstart.html.md.erb
index 6e24038..62827c5 100644
--- a/docs/manual/source/templates/vanilla/quickstart.html.md.erb
+++ b/docs/manual/source/templates/vanilla/quickstart.html.md.erb
@@ -29,7 +29,7 @@ No special event requirement
 
 ## 2. Create a new Engine from an Engine Template
 
-<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyNewEngine', template_name:
'Vanilla Engine Template', template_repo: 'template-scala-parallel-vanilla' } %>
+<%= partial 'shared/quickstart/create_engine', locals: { engine_name: 'MyNewEngine', template_name:
'Vanilla Engine Template', template_repo: 'apache/incubator-predictionio-template-skeleton'
} %>
 
 ## 3. Generate an App ID and Access Key
 


Mime
View raw message