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* Always remain as root during install * Put kernel specs in system path, not user home * Create user work directory at startup * Note this is in 4.0 and up images, not 3.2 Contribution (c) Copyright IBM Corp. 2015
102 lines
5.6 KiB
Markdown
102 lines
5.6 KiB
Markdown
# Jupyter Notebook Python, Spark, Mesos Stack
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## What it Gives You
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* Jupyter Notebook server (v4.0.x or v3.2.x, see tag)
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* Conda Python 3.4.x and Python 2.7.x environments
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* pyspark, pandas, matplotlib, scipy, seaborn, scikit-learn pre-installed
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* Spark 1.4.1 for use in local mode or to connect to a cluster of Spark workers
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* Mesos client 0.22 binary that can communicate with a Mesos master
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* Options for HTTPS, password auth, and passwordless `sudo`
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## Basic Use
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The following command starts a container with the Notebook server listening for HTTP connections on port 8888 without authentication configured.
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```
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docker run -d -p 8888:8888 jupyter/pyspark-notebook
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```
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## Using Spark Local Mode
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This configuration is nice for using Spark on small, local data.
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0. Run the container as shown above.
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2. Open a Python 2 or 3 notebook.
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3. Create a `SparkContext` configured for local mode.
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For example, the first few cells in a Python 3 notebook might read:
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```python
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import pyspark
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sc = pyspark.SparkContext('local[*]')
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# do something to prove it works
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rdd = sc.parallelize(range(1000))
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rdd.takeSample(False, 5)
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```
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In a Python 2 notebook, prefix the above with the following code to ensure the local workers use Python 2 as well.
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```python
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import os
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os.environ['PYSPARK_PYTHON'] = 'python2'
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# include pyspark cells from above here ...
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```
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## Connecting to a Spark Cluster on Mesos
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This configuration allows your compute cluster to scale with your data.
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0. [Deploy Spark on Mesos](http://spark.apache.org/docs/latest/running-on-mesos.html).
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1. Ensure Python 2.x and/or 3.x and any Python libraries you wish to use in your Spark lambda functions are installed on your Spark workers.
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2. Run the Docker container with `--net=host` in a location that is network addressable by all of your Spark workers. (This is a [Spark networking requirement](http://spark.apache.org/docs/latest/cluster-overview.html#components).)
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3. Open a Python 2 or 3 notebook.
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4. Create a `SparkConf` instance in a new notebook pointing to your Mesos master node (or Zookeeper instance) and Spark binary package location.
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5. Create a `SparkContext` using this configuration.
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For example, the first few cells in a Python 3 notebook might read:
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```python
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import os
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# make sure pyspark tells workers to use python3 not 2 if both are installed
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os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'
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import pyspark
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conf = pyspark.SparkConf()
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# point to mesos master or zookeeper entry (e.g., zk://10.10.10.10:2181/mesos)
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conf.setMaster("mesos://10.10.10.10:5050")
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# point to spark binary package in HDFS or on local filesystem on all slave
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# nodes (e.g., file:///opt/spark/spark-1.4.1-bin-hadoop2.6.tgz)
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conf.set("spark.executor.uri", "hdfs://10.122.193.209/spark/spark-1.4.1-bin-hadoop2.6.tgz")
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# set other options as desired
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conf.set("spark.executor.memory", "8g")
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conf.set("spark.core.connection.ack.wait.timeout", "1200")
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# create the context
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sc = pyspark.SparkContext(conf=conf)
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# do something to prove it works
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rdd = sc.parallelize(range(100000000))
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rdd.sumApprox(3)
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```
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To use Python 2 in the notebook and on the workers, change the `PYSPARK_PYTHON` environment variable to point to the location of the Python 2.x interpreter binary. If you leave this environment variable unset, it defaults to `python`.
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Of course, all of this can be hidden in an [IPython kernel startup script](http://ipython.org/ipython-doc/stable/development/config.html?highlight=startup#startup-files), but "explicit is better than implicit." :)
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## Options
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You may customize the execution of the Docker container and the Notebook server it contains with the following optional arguments.
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* `-e PASSWORD="YOURPASS"` - Configures Jupyter Notebook to require the given password. Should be conbined with `USE_HTTPS` on untrusted networks.
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* `-e USE_HTTPS=yes` - Configures Jupyter Notebook to accept encrypted HTTPS connections. If a `pem` file containing a SSL certificate and key is not found in `/home/jovyan/.ipython/profile_default/security/notebook.pem`, the container will generate a self-signed certificate for you.
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* **(v4.0.x)** `-e NB_UID=1000` - Specify the uid of the `jovyan` user. Useful to mount host volumes with specific file ownership.
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* `-e GRANT_SUDO=yes` - Gives the `jovyan` user passwordless `sudo` capability. Useful for installing OS packages. **You should only enable `sudo` if you trust the user or if the container is running on an isolated host.**
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* `-v /some/host/folder/for/work:/home/jovyan/work` - Host mounts the default working directory on the host to preserve work even when the container is destroyed and recreated (e.g., during an upgrade).
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* **(v3.2.x)** `-v /some/host/folder/for/server.pem:/home/jovyan/.ipython/profile_default/security/notebook.pem` - Mounts a SSL certificate plus key for `USE_HTTPS`. Useful if you have a real certificate for the domain under which you are running the Notebook server.
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* **(v4.0.x)** `-v /some/host/folder/for/server.pem:/home/jovyan/.local/share/jupyter/notebook.pem` - Mounts a SSL certificate plus key for `USE_HTTPS`. Useful if you have a real certificate for the domain under which you are running the Notebook server.
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* `-e INTERFACE=10.10.10.10` - Configures Jupyter Notebook to listen on the given interface. Defaults to '*', all interfaces, which is appropriate when running using default bridged Docker networking. When using Docker's `--net=host`, you may wish to use this option to specify a particular network interface.
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* `-e PORT=8888` - Configures Jupyter Notebook to listen on the given port. Defaults to 8888, which is the port exposed within the Dockerfile for the image. When using Docker's `--net=host`, you may wish to use this option to specify a particular port. |