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First draft configuration page

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Peter Parente
2018-03-24 23:24:46 -04:00
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docs/configuration.md
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# Options and Configuration
When launched as Docker containers, all of the Jupyter Docker Stacks start a Jupyter Notebook server by default. They do so by executing a `start-notebook.sh` script which configures the internal container environment and then runs `jupyter notebook $*`, passing it any command line arguments received.
This page describes the options supported by the startup script as well as how to bypass it to run alternative commands.
## Notebook Options
The Docker container executes a `start-notebook.sh` script script by default. The `start-notebook.sh` script handles the `NB_UID`, `NB_GID` and `GRANT_SUDO` features documented in the next section, and then executes the `jupyter notebook`.
You can pass [Jupyter command line options](https://jupyter.readthedocs.io/en/latest/projects/jupyter-command.html) through the `start-notebook.sh` script when launching the container. For example, to secure the Notebook server with a custom password hashed using `IPython.lib.passwd()` instead of the default token, run the following:
You can pass [Jupyter command line options](https://jupyter.readthedocs.io/en/latest/projects/jupyter-command.html) to the `start-notebook.sh` script when launching the container. For example, to secure the Notebook server with a custom password hashed using `IPython.lib.passwd()` instead of the default token, you can run the following:
```
docker run -d -p 8888:8888 jupyter/base-notebook start-notebook.sh --NotebookApp.password='sha1:74ba40f8a388:c913541b7ee99d15d5ed31d4226bf7838f83a50e'
```
For example, to set the base URL of the notebook server, run the following:
For example, to set the base URL of the notebook server, you can run the following:
```
docker run -d -p 8888:8888 jupyter/base-notebook start-notebook.sh --NotebookApp.base_url=/some/path
```
For example, to disable all authentication mechanisms (which is not a recommended practice):
For example, to ignore best practice and disable all authentication, you can run the following:
```
docker run -d -p 8888:8888 jupyter/base-notebook start-notebook.sh --NotebookApp.token=''
```
You can sidestep the `start-notebook.sh` script and run your own commands in the container. See the *Alternative Commands* section later in this document for more information.
## Docker Options
You may customize the execution of the Docker container and the command it is running with the following optional arguments.
You may instruct the `start-notebook.sh` script to customize the container environment before launching
the notebook server. You do so by passing arguments to the `docker run` command.
* `-e GEN_CERT=yes` - Generates a self-signed SSL certificate and configures Jupyter Notebook to use it to accept encrypted HTTPS connections.
* `-e NB_UID=1000` - Specify the uid of the `jovyan` user. Useful to mount host volumes with specific file ownership. For this option to take effect, you must run the container with `--user root`. (The `start-notebook.sh` script will `su jovyan` after adjusting the user id.)
* `-e NB_GID=100` - Specify the gid of the `jovyan` user. Useful to mount host volumes with specific file ownership. For this option to take effect, you must run the container with `--user root`. (The `start-notebook.sh` script will `su jovyan` after adjusting the group id.)
* `-e GRANT_SUDO=yes` - Gives the `jovyan` user passwordless `sudo` capability. Useful for installing OS packages. For this option to take effect, you must run the container with `--user root`. (The `start-notebook.sh` script will `su jovyan` after adding `jovyan` to sudoers.) **You should only enable `sudo` if you trust the user or if the container is running on an isolated host.**
* `-e NB_USER=jovyan` - Instructs the startup script to change the default container username from `jovyan` to the provided value. Causes the script to rename the `jovyan` user home folder.
* `-e NB_UID=1000` - Instructs the startup script to switch the numeric user ID of `$NB_USER` to the given value. This feature is useful when mounting host volumes with specific owner permissions. For this option to take effect, you must run the container with `--user root`. (The startup script will `su $NB_USER` after adjusting the user ID.)
* `-e NB_GID=100` - Instructs the startup script to change the numeric group ID of the `$NB_USER` to the given value. This feature is useful when mounting host volumes with specific group permissions. For this option to take effect, you must run the container with `--user root`. (The startup script will `su $NB_USER` after adjusting the group ID.)
* `-e CHOWN_HOME=yes` - Instructs the startup script to recursively change the `$NB_USER` home directory owner and group to the current value of `$NB_UID` and `$NB_GID`. This change will take effect even if the user home directory is mounted from the host using `-v` as described below.
* `-e GRANT_SUDO=yes` - Instructs the startup script to grant the `NB_USER` user passwordless `sudo` capability. You do **not** need too this option to allow the user to `conda` or `pip` install additional packages. This option is useful, however, when you wish to give `$NB_USER` the ability to install OS packages with `apt` or modify other root-owned files in the container. For this option to take effect, you must run the container with `--user root`. (The `start-notebook.sh` script will `su $NB_USER` after adding `$NB_USER` to sudoers.) **You should only enable `sudo` if you trust the user or if the container is running on an isolated host.**
* `-e GEN_CERT=yes` - Instructs the startup script to generates a self-signed SSL certificate and configure Jupyter Notebook to use it to accept encrypted HTTPS connections.
* `-v /some/host/folder/for/work:/home/jovyan/work` - Mounts a host machine directory as folder in the container. Useful when you want to preserve notebooks and other work even after the container is destroyed. **You must grant the within-container notebook user or group (`NB_UID` or `NB_GID`) write access to the host directory (e.g., `sudo chown 1000 /some/host/folder/for/work`).**
* `--group-add users` - use this argument if you are also specifying
a specific user id to launch the container (`-u 5000`), rather than launching the container as root and relying on *NB_UID* and *NB_GID* to set the user and group.
* `-user 5000 --group-add users` - Launches the container with a specific user ID and adds that user to the `users` group so that it can modify files in the default home directory and `/opt/conda`. You can use these arguments as alternatives to setting `$NB_UID` and `$NB_GID`.
## SSL Certificates
You may mount SSL key and certificate files into a container and configure Jupyter Notebook to use them to accept HTTPS connections. For example, to mount a host folder containing a `notebook.key` and `notebook.crt`:
You may mount SSL key and certificate files into a container and configure Jupyter Notebook to use them to accept HTTPS connections. For example, to mount a host folder containing a `notebook.key` and `notebook.crt` and use them, you might run the following:
```
docker run -d -p 8888:8888 \
@@ -65,23 +67,11 @@ For additional information about using SSL, see the following:
* The [jupyter_notebook_config.py](jupyter_notebook_config.py) file for how this Docker image generates a self-signed certificate.
* The [Jupyter Notebook documentation](https://jupyter-notebook.readthedocs.io/en/latest/public_server.html#securing-a-notebook-server) for best practices about securing a public notebook server in general.
## Conda Environments
The default Python 3.x [Conda environment](http://conda.pydata.org/docs/using/envs.html) resides in `/opt/conda`. The `/opt/conda/bin` directory is part of the default `jovyan` user's `$PATH`. That directory is also whitelisted for use in `sudo` commands by the `start.sh` script.
The `jovyan` user has full read/write access to the `/opt/conda` directory. You can use either `conda` or `pip` to install new packages without any additional permissions.
```
# install a package into the default (python 3.x) environment
pip install some-package
conda install some-package
```
## Alternative Commands
### start.sh
The `start.sh` script supports the same features as the default `start-notebook.sh` script (e.g., `GRANT_SUDO`), but allows you to specify an arbitrary command to execute. For example, to run the text-based `ipython` console in a container, do the following:
The `start-notebook.sh` script actually inherits most of its option handling capability from a more generic `start.sh` script. The `start.sh` script supports all of the features described above, but allows you to specify an arbitrary command to execute. For example, to run the text-based `ipython` console in a container, do the following:
```
docker run -it --rm jupyter/base-notebook start.sh ipython
@@ -97,21 +87,29 @@ This script is particularly useful when you derive a new Dockerfile from this im
### Others
You can bypass the provided scripts and specify your an arbitrary start command. If you do, keep in mind that certain features documented above will not function (e.g., `GRANT_SUDO`).
You can bypass the provided scripts and specify your an arbitrary start command. If you do, keep in mind that features supported by the `start.sh` script and its kin will not function (e.g., `GRANT_SUDO`).
## Image Specifics
## Conda Environments
### Spark and PySpark
The default Python 3.x [Conda environment](http://conda.pydata.org/docs/using/envs.html) resides in `/opt/conda`. The `/opt/conda/bin` directory is part of the default `jovyan` user's `$PATH`. That directory is also whitelisted for use in `sudo` commands by the `start.sh` script.
#### Using Spark Local Mode
The `jovyan` user has full read/write access to the `/opt/conda` directory. You can use either `conda` or `pip` to install new packages without any additional permissions.
This configuration is nice for using Spark on small, local data.
```
# install a package into the default (python 3.x) environment
pip install some-package
conda install some-package
```
0. Run the container as shown above.
2. Open a Python 2 or 3 notebook.
3. Create a `SparkContext` configured for local mode.
## Apache Spark
For example, the first few cells in the notebook might read:
The `jupyter/pyspark-notebook` and `jupyter/all-spark-notebook` images support the use of Apache Spark in Python, R, and Scala notebooks. The following sections provide some examples of how to get started using them.
### Using Spark Local Mode
Spark local mode is useful for experimentation on small data when you do not have a Spark cluster available.
#### In a Python Notebook
```python
import pyspark
@@ -122,20 +120,54 @@ rdd = sc.parallelize(range(1000))
rdd.takeSample(False, 5)
```
#### In a R Notebook
```r
library(SparkR)
as <- sparkR.session("local[*]")
# do something to prove it works
df <- as.DataFrame(iris)
head(filter(df, df$Petal_Width > 0.2))
```
#### In a Spylon Kernel Scala Notebook
Spylon kernel instantiates a `SparkContext` for you in variable `sc` after you configure Spark options in a `%%init_spark` magic cell.
```python
%%init_spark
# Configure Spark to use a local master
launcher.master = "local[*]"
```
```scala
// Now run Scala code that uses the initialized SparkContext in sc
val rdd = sc.parallelize(0 to 999)
rdd.takeSample(false, 5)
```
#### In an Apache Toree Scala Notebook
Apache Toree instantiates a local `SparkContext` for you in variable `sc` when the kernel starts.
```scala
val rdd = sc.parallelize(0 to 999)
rdd.takeSample(false, 5)
```
### Connecting to a Spark Cluster on Mesos
This configuration allows your compute cluster to scale with your data.
0. [Deploy Spark on Mesos](http://spark.apache.org/docs/latest/running-on-mesos.html).
1. Configure each slave with [the `--no-switch_user` flag](https://open.mesosphere.com/reference/mesos-slave/) or create the `jovyan` user on every slave node.
2. Ensure Python 2.x and/or 3.x and any Python libraries you wish to use in your Spark lambda functions are installed on your Spark workers.
3. Run the Docker container with `--net=host` in a location that is network addressable by all of your Spark workers. (This is a [Spark networking requirement](http://spark.apache.org/docs/latest/cluster-overview.html#components).)
1. Configure each slave with [the `--no-switch_user` flag](https://open.mesosphere.com/reference/mesos-slave/) or create the `$NB_USER` account on every slave node.
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).)
* NOTE: When using `--net=host`, you must also use the flags `--pid=host -e TINI_SUBREAPER=true`. See https://github.com/jupyter/docker-stacks/issues/64 for details.
4. Open a Python 2 or 3 notebook.
5. Create a `SparkConf` instance in a new notebook pointing to your Mesos master node (or Zookeeper instance) and Spark binary package location.
6. Create a `SparkContext` using this configuration.
3. Follow the language specific instructions below.
For example, the first few cells in a Python 3 notebook might read:
#### In a Python Notebook
```python
import os
@@ -149,7 +181,7 @@ conf = pyspark.SparkConf()
conf.setMaster("mesos://10.10.10.10:5050")
# point to spark binary package in HDFS or on local filesystem on all slave
# nodes (e.g., file:///opt/spark/spark-2.2.0-bin-hadoop2.7.tgz)
conf.set("spark.executor.uri", "hdfs://10.122.193.209/spark/spark-2.2.0-bin-hadoop2.7.tgz")
conf.set("spark.executor.uri", "hdfs://10.10.10.10/spark/spark-2.2.0-bin-hadoop2.7.tgz")
# set other options as desired
conf.set("spark.executor.memory", "8g")
conf.set("spark.core.connection.ack.wait.timeout", "1200")
@@ -162,13 +194,68 @@ rdd = sc.parallelize(range(100000000))
rdd.sumApprox(3)
```
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`.
#### In a R Notebook
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." :)
```r
library(SparkR)
#### Connecting to a Spark Cluster in Standalone Mode
# Point to mesos master or zookeeper entry (e.g., zk://10.10.10.10:2181/mesos)
# Point to spark binary package in HDFS or on local filesystem on all slave
# nodes (e.g., file:///opt/spark/spark-2.2.0-bin-hadoop2.7.tgz) in sparkEnvir
# Set other options in sparkEnvir
sc <- sparkR.session("mesos://10.10.10.10:5050", sparkEnvir=list(
spark.executor.uri="hdfs://10.10.10.10/spark/spark-2.2.0-bin-hadoop2.7.tgz",
spark.executor.memory="8g"
)
)
Connection to Spark Cluster in Standalone Mode requires the following set of steps:
# do something to prove it works
data(iris)
df <- as.DataFrame(iris)
head(filter(df, df$Petal_Width > 0.2))
```
#### In a Spylon Kernel Scala Notebook
```python
%%init_spark
# Configure the location of the mesos master and spark distribution on HDFS
launcher.master = "mesos://10.10.10.10:5050"
launcher.conf.spark.executor.uri=hdfs://10.10.10.10/spark/spark-2.2.0-bin-hadoop2.7.tgz
```
```scala
// Now run Scala code that uses the initialized SparkContext in sc
val rdd = sc.parallelize(0 to 999)
rdd.takeSample(false, 5)
```
#### In an Apache Toree Scala Notebook
The Apache Toree kernel automatically creates a `SparkContext` when it starts based on configuration information from its command line arguments and environment variables. You can pass information about your Mesos cluster via the `SPARK_OPTS` environment variable when you spawn a container.
For instance, to pass information about a Mesos master, Spark binary location in HDFS, and an executor options, you could start the container like so:
```
docker run -d -p 8888:8888 -e SPARK_OPTS='--master=mesos://10.10.10.10:5050 \
--spark.executor.uri=hdfs://10.10.10.10/spark/spark-2.2.0-bin-hadoop2.7.tgz \
--spark.executor.memory=8g' jupyter/all-spark-notebook
```
Note that this is the same information expressed in a notebook in the Python case above. Once the kernel spec has your cluster information, you can test your cluster in an Apache Toree notebook like so:
```scala
// should print the value of --master in the kernel spec
println(sc.master)
// do something to prove it works
val rdd = sc.parallelize(0 to 99999999)
rdd.sum()
```
### Connecting to a Spark Cluster on Standalone Mode
Connection to Spark Cluster on Standalone Mode requires the following set of steps:
0. Verify that the docker image (check the Dockerfile) and the Spark Cluster which is being deployed, run the same version of Spark.
1. [Deploy Spark on Standalone Mode](http://spark.apache.org/docs/latest/spark-standalone.html).

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@@ -22,7 +22,7 @@ The Jupyter team maintains a set of Docker image definitions in the [https://git
`jupyter/base-notebook` is a small image supporting the [options common across all core stacks](configuration.html). It is the basis for all other stacks.
* Minimally-functional Jupyter Notebook server (e.g., no [pandoc](https://pandoc.org/) for saving notebooks as PDFs)
* [Miniconda](https://conda.io/miniconda.html) Python 3.x
* [Miniconda](https://conda.io/miniconda.html) Python 3.x in `/opt/conda`
* No preinstalled scientific computing packages
* Unprivileged user `jovyan` (`uid=1000`, configurable, see options) in group `users` (`gid=100`) with ownership over the `/home/jovyan` and `/opt/conda` paths
* `tini` as the container entrypoint and a `start-notebook.sh` script as the default command
@@ -251,7 +251,7 @@ notebook
### Using Binder
[Binder](https://mybinder.org/) is a service that allows you to create and share custom computing environments for projects in version control. You can use any of the Jupyter Docker Stack images as a basis for a Binder-compatible Dockerfile. See the [docker-stacks example](https://mybinder.readthedocs.io/en/latest/sample_repos.html#using-a-docker-image-from-the-jupyter-docker-stacks-repository) and [Using a Dockerfile](https://mybinder.readthedocs.io/en/latest/dockerfile.html) sections in the [Binder documentation](https://mybinder.readthedocs.io/en/latest/index.html) for instructions.
[Binder](https://mybinder.org/) is a service that allows you to create and share custom computing environments for projects in version control. You can use any of the Jupyter Docker Stacks images as a basis for a Binder-compatible Dockerfile. See the [docker-stacks example](https://mybinder.readthedocs.io/en/latest/sample_repos.html#using-a-docker-image-from-the-jupyter-docker-stacks-repository) and [Using a Dockerfile](https://mybinder.readthedocs.io/en/latest/dockerfile.html) sections in the [Binder documentation](https://mybinder.readthedocs.io/en/latest/index.html) for instructions.
### Using JupyterHub