To run Spark within Jupyter we recommend using the Toree kernel. We are going to assume you already have the following installed:
- Python 2.x
- Docker (required to install Toree)
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
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_2.11:0.17.0,"
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 0.17.0 --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": "/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.