Jupyter Setup

To run Spark within Jupyter we recommend using the Toree kernel.We are going to assume you already have the following installed:

1. Python 2.x
2. PIP
3. Docker (required to install Toree)

Install Jupyter

virtualenv venv
source ./venv/bin/activate
pip install jupyter

Build and install Toree

Clone master into your working directory from Toree’s github repo.

For this next step, you’ll need to make sure that docker is running.

cd incubator-toree
make release
cd dist/toree-pip
pip install .
SPARK_HOME=<path to spark> jupyter toree install

Launch Notebook with MLeap for Spark

The most error-proof way to add mleap to your project is to modify the kernel directly (or create a new one for Toree and Spark 2.0).

Kernel config files are typically located in /usr/local/share/jupyter/kernels/apache_toree_scala/kernel.json

Go ahead and add or modify __TOREE_SPARK_OPTS__ like so:

"__TOREE_SPARK_OPTS__": "--packages com.databricks:spark-avro_2.11:3.0.1,ml.combust.mleap:mleap-spark_2.11:0.14.0,"

An alternative way is to use AddDeps Magics, but we’ve run into dependency collisions, so do so at your own risk:

%AddDeps ml.combust.mleap mleap-spark_2.11 0.14.0 --transitive

Launch Notebook with MLeap for PySpark

First go through the steps above for launching a notebook with MLeap for Spark, then add the following to PYTHONPATH

    "PYTHONPATH": "/usr/local/spark-2.0.0-bin-hadoop2.7/python:/usr/local/spark-2.0.0-bin-hadoop2.7/python/lib/py4j-0.10.1-src.zip:/<git directory>/combust/combust-mleap/python",

Launch Notebook with MLeap for Scikit-Learn

No need to modify the kernel.json directly, just instantiate the libraries like described here.