[
https://issues.apache.org/jira/browse/PIO-183?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16661677#comment-16661677
]
ASF GitHub Bot commented on PIO-183:
------------------------------------
shimamoto commented on a change in pull request #481: [PIO-183] Add Jupyter
Docker image
URL: https://github.com/apache/predictionio/pull/481#discussion_r227632554
##########
File path: docker/JUPYTER.md
##########
@@ -0,0 +1,167 @@
+<!--
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to You under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+Jupyter With PredictionIO
+=========================
+
+## Overview
+
+Using Jupyter based docker, you can use Jupyter Notebook with PredictionIO
environment.
+It help your exploratory data analysis (EDA).
+
+
+## Run Jupyter Notebook
+
+First of all, start Jupyter container with PredictionIO environment:
+
+```
+docker-compose -f docker-compose.jupyter.yml \
+ -f pgsql/docker-compose.base.yml \
+ -f pgsql/docker-compose.meta.yml \
+ -f pgsql/docker-compose.event.yml \
+ -f pgsql/docker-compose.model.yml \
+ up
+```
+
+The above command prints a token to the console as below.
+
+```
+pio_1 | http://(3aaf67361022 or
127.0.0.1):8888/?token=e87a634b4ab7e2c8bcd86aea9def3eb48183c043eac86f3e
+```
+
+Open `http://127.0.0.1:8888/`, type the token, and then open a new terminal in
Jupyter from `New` pulldown button.
+
+## Getting Started With Scala Based Template
+
+### Download Template
+
+Clone a template using Git:
+
+```
+cd templates/
+git clone https://github.com/apache/predictionio-template-recommender.git
+cd predictionio-template-recommender/
+```
+
+Replace a name with `MyApp1`.
+
+```
+sed -i "s/INVALID_APP_NAME/MyApp1/" engine.json
+```
+
+### Register New Application
+
+Using pio command, register a new application as `MyApp1`.
+
+```
+pio app new MyApp1
+```
+
+This command prints an access key as below.
+
+```
+[INFO] [Pio$] Access Key:
bbe8xRHN1j3Sa8WeAT8TSxt5op3lUqhvXmKY1gLRjg70K-DUhHIJJ0-UzgKumxGm
+```
+
+Set it to an environment variable `ACCESS_KEY`.
+
+```
+ACCESS_KEY=bbe8xRHN1j3Sa8WeAT8TSxt5op3lUqhvXmKY1gLRjg70K-DUhHIJJ0-UzgKumxGm
+```
+
+### Import Training Data
+
+Download trainging data and import them to PredictionIO Event server.
+
+```
+curl
https://raw.githubusercontent.com/apache/spark/master/data/mllib/sample_movielens_data.txt
--create-dirs -o data/sample_movielens_data.txt
+python data/import_eventserver.py --access_key $ACCESS_KEY
+```
+
+### Build Template
+
+Build your template by the following command:
+
+```
+pio build --verbose
+```
+
+### Create Model
+
+To create a model, run:
+
+```
+pio train
+```
+
+## Getting Started With Python Based Template
+
+### Download Template
+
+Clone a template using Git:
+
+```
+cd templates/
+git clone https://github.com/jpioug/predictionio-template-iris.git
+predictionio-template-iris/
+```
+
+### Register New Application
+
+Using pio command, register a new application as `IrisApp`.
+
+```
+pio app new --access-key IRIS_TOKEN IrisApp
+```
+
+### Import Training Data
+
+Download trainging data and import them to PredictionIO Event server.
+
+```
+python data/import_eventserver.py
+```
+
+### Build Template
+
+Build your template by the following command:
+
+```
+pio build --verbose
+```
+
+### EDA
+
+To do data analysis, open `templates/predictionio-template-iris/eda.ipynb` on
Jupyter.
+
+### Create Model
+
+You need to clear the following environment variables in the terminal before
executing `pio train`.
+
+```
+unset PYSPARK_PYTHON
+unset PYSPARK_DRIVER_PYTHON
+unset PYSPARK_DRIVER_PYTHON_OPTS
+```
+
+To create a model, run:
+
+```
+pio train --main-py-file train.py
+```
Review comment:
Exactly, so I have come up with a following plan: if the `main-py-file`
option is specified, PYSPARK_* environment variables are removed automatically
before running `spark-submit` internally. Each has its pros and cons. It
doesn't have to be implemented :)
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> Add Jupyter Docker image
> ------------------------
>
> Key: PIO-183
> URL: https://issues.apache.org/jira/browse/PIO-183
> Project: PredictionIO
> Issue Type: New Feature
> Reporter: Shinsuke Sugaya
> Assignee: Shinsuke Sugaya
> Priority: Major
>
> For usage, see
> [JUPYTER.md|https://github.com/marevol/predictionio/blob/jupyter/docker/JUPYTER.md].
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