Add spylon-kernel to all-spark

all-spark-notebook/Dockerfile

@@ -31,3 +31,7 @@ RUN conda config --add channels r && \
 # Apache Toree kernel
 RUN pip --no-cache-dir install https://dist.apache.org/repos/dist/dev/incubator/toree/0.2.0/snapshots/dev1/toree-pip/toree-0.2.0.dev1.tar.gz
 RUN jupyter toree install --sys-prefix
+
+# Spylon-kernel
+RUN pip --no-cache-dir install metakernel spylon findspark spylon-kernel
+RUN python -m spylon_kernel install --sys-prefix

all-spark-notebook/README.md

@@ -8,11 +8,12 @@
 * Jupyter Notebook 4.3.x
 * Conda Python 3.x and Python 2.7.x environments
 * Conda R 3.3.x environment
-* Scala 2.10.x
+* Scala 2.11.x
 * pyspark, pandas, matplotlib, scipy, seaborn, scikit-learn pre-installed for Python
 * ggplot2, rcurl preinstalled for R
 * Spark 2.0.2 with Hadoop 2.7 for use in local mode or to connect to a cluster of Spark workers
 * Mesos client 0.25 binary that can communicate with a Mesos master
+* spylon-kernel
 * Unprivileged user `jovyan` (uid=1000, configurable, see options) in group `users` (gid=100) with ownership over `/home/jovyan` and `/opt/conda`
 * [tini](https://github.com/krallin/tini) as the container entrypoint and [start-notebook.sh](../base-notebook/start-notebook.sh) as the default command
 * A [start-singleuser.sh](../base-notebook/start-singleuser.sh) script useful for running a single-user instance of the Notebook server, as required by JupyterHub
@@ -81,6 +82,19 @@ val rdd = sc.parallelize(0 to 999)
 rdd.takeSample(false, 5)
 ```
 
+### In spylon-kernel - Scala Notebook
+
+0. Run the container as shown above.
+1. Open a spylon-kernel notebook
+2. Lazily instantiate the sparkcontext by just running any cell without magics
+
+For example
+
+```
+val rdd = sc.parallelize(0 to 999)
+rdd.takeSample(false, 5)
+```
+
 ## Connecting to a Spark Cluster on Mesos
 
 This configuration allows your compute cluster to scale with your data.