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Galaxy Interactive Environments
===============================
A GIE is a Docker container, launched by Galaxy, proxied by Galaxy, with some
extra sugar inside the container to allow users to interact easily with their
Galaxy histories.
How GIEs Work
-------------
A GIE is primarily composed of a Docker container, and the Galaxy visualization
component. Galaxy visualization plugins are rendered using Mako templates and
Mako templates in turn can run Python code. GIEs build upon visualization plugins,
adding features to allow for container management and proxying. This Python code
in the Mako templates is used to launch the Docker container within which a GIE
runs. Once this container is launched, we notify a proxy built into Galaxy which
helps coordinate a 1:1 mapping of users and their docker containers.
Here's a simple diagram recapping the above:
.. image:: interactive_environments.png
Deploying GIEs
--------------
Deploying GIEs is not a trivial operation. They have complex interactions with
numerous services, you'll need to be a fairly competent SysAdmin to debug all
of the possible problems that can occur during deployment. After the initial
hurdle, most find that GIEs require little to no maintenance.
An `Ansible <http://www.ansible.com/>`__ role for installing and managing GIEs
can be found on
`Github <https://github.com/galaxyproject/ansible-interactive-environments>`__
and `Ansible Galaxy <https://galaxy.ansible.com/detail#/role/6056>`__.
Setting up the Proxy
^^^^^^^^^^^^^^^^^^^^
The Galaxy IE Proxy is a NodeJS+Sqlite3 application. The NodeJS that is
installed by default into the Galaxy Virtualenv is suitable for an execution
environment for the Galaxy IE Proxy.
- Note that if you have NodeJS installed under Ubuntu, it often installs to
``/usr/bin/nodejs``, whereas ``npm`` expects it to be ``/usr/bin/node``. You
may need to create that symlink yourself.
Once Node and npm are ready to go, you'll need to install the dependencies
.. code-block:: console
$ cd $GALAXY_ROOT/lib/galaxy/web/proxy/js
$ npm install
Running ``node lib/main.js --help`` should produce some useful help text
.. code-block:: console
Usage: main [options]
Options:
-h, --help output usage information
-V, --version output the version number
--ip <n> Public-facing IP of the proxy
--port <n> Public-facing port of the proxy
--cookie <cookiename> Cookie proving authentication
--sessions <file> Routes file to monitor
--verbose
There are two ways to handle actually running the proxy. The first is to have
Galaxy automatically launches the proxy as needed. This is the default configuration
as of 2014. Alternately, the proxy can be stated manually or via a system such as
Supervisord. Assuming that the ``$GALAXY_ROOT`` environment variable refers to the location of
the Galaxy installation, the command for launching the proxy is:
.. code-block:: console
$ node $GALAXY_ROOT/lib/galaxy/web/proxy/js/lib/main.js --ip 0.0.0.0 \
--port 8800 --sessions $GALAXY_ROOT/database/session_map.sqlite \
--cookie galaxysession --verbose
And this can be configured in your supervisord config by adding:
.. code-block:: console
[program:galaxy_nodejs_proxy]
directory = GALAXY_ROOT
command = GALAXY_ROOT/lib/galaxy/web/proxy/js/lib/main.js --sessions database/session_map.sqlite --ip 0.0.0.0 --port 8800
autostart = true
autorestart = unexpected
user = GALAXY_USER
startsecs = 5
redirect_stderr = true
where ``GALAXY_ROOT`` is the location of your Galaxy installation and ``GALAXY_USER`` is the username of the user that
Galaxy runs as.
Configuring the Proxy
^^^^^^^^^^^^^^^^^^^^^
Configuration is all managed in ``galaxy.yml``. The default arguments used
for the proxy are:
.. code-block:: yaml
dynamic_proxy_manage: true
dynamic_proxy_session_map: database/session_map.sqlite
dynamic_proxy_bind_port: 8800
dynamic_proxy_bind_ip: 0.0.0.0
dynamic_proxy_debug: true
As you can see most of these variables map directly to the command line
arguments to the NodeJS script. There are a few extra parameters which will
be needed if you run Galaxy behind an upstream proxy like nginx or
Apache:
.. code-block:: yaml
dynamic_proxy_external_proxy: true
dynamic_proxy_prefix: gie_proxy
The first option says that you have Galaxy and the Galaxy NodeJS proxy wrapped
in an upstream proxy like Apache or NGINX. This will cause Galaxy to connect
users to the same port as Galaxy is being served on (so 80/443), rather than
directing them to port 8800.
The second option is closely entertwined with the first option. When Galaxy is
accessed, it sets a cookie called ``galaxysession``. This cookie generally cannot be sent with requests
to different domains and different ports, so Galaxy and the dynamic proxy must
be accessible on the same port and protocol. In addition, the cookie is only
accessible to URLs that share the same prefix as the Galaxy URL. For example,
if you're running Galaxy under a URL like ``https://f.q.d.n/galaxy/``, the cookie
is only accessible to URLs that look like ``https://f.q.d.n/galaxy/*``. The
second (``dynamic_proxy_prefix``) option sets the URL path that's used to
differentiate requests that should go through the proxy to those that should go
to Galaxy. You will need to add special upstream proxy configuration to handle
this, and you'll need to use the same ``dynamic_proxy_prefix`` in your
``galaxy.yml`` that you use in your URL routes.
In the examples below, we assume that your Galaxy installation is available
at a URL such as ``https://f.q.d.n/galaxy``. If instead it is available at a
URL like ``https://f.q.d.n``, remove the ``/galaxy`` prefix from the examples.
For example ``/galaxy/gie_proxy`` would become ``/gie_proxy``. Remember that
``gie_proxy`` is the value you use for the ``dynamic_proxy_prefix`` option. If
you use a different value in that option you should change the examples
accordingly.
**Apache**
.. code-block:: apache
# Project Jupyter specific. Other IEs may require their own routes.
ProxyPass /galaxy/gie_proxy/jupyter/ipython/api/kernels ws://localhost:8800/galaxy/gie_proxy/jupyter/ipython/api/kernels
# Global GIE configuration
ProxyPass /galaxy/gie_proxy http://localhost:8800/galaxy/gie_proxy
ProxyPassReverse /galaxy/gie_proxy http://localhost:8800/galaxy/gie_proxy
# Normal Galaxy configuration
ProxyPass /galaxy http://localhost:8000/galaxy
ProxyPassReverse /galaxy http://localhost:8000/galaxy
Please note you will need to be using apache2.4 with ``mod_proxy_wstunnel``.
**Nginx**
.. code-block:: nginx
# Global GIE configuration
location /galaxy/gie_proxy {
proxy_pass http://localhost:8800/galaxy/gie_proxy;
proxy_redirect off;
}
# Project Jupyter specific. Other IEs may require their own routes.
location ~ ^/galaxy/gie_proxy/jupyter/(?<nbtype>[^/]+)/api/kernels(?<rest>.*?)$ {
proxy_pass http://localhost:8800/galaxy/gie_proxy/jupyter/$nbtype/api/kernels$rest;
proxy_redirect off;
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
}
If you proxy static content, you may find the following rule useful for
proxying to GIE and other visualization plugin static content.
.. code-block:: nginx
location ~ ^/static/plugins/(?<plug_type>.+?)/(?<vis_name>.+?)/static/(?<static_file>.*?)$ {
alias /path/to/galaxy-dist/config/plugins/$plug_type/$vis_name/static/$static_file;
}
Docker on Another Host
^^^^^^^^^^^^^^^^^^^^^^
You might want to run your IEs on a host different to the one that hosts your
Galaxy webserver, since IEs on the same host as the webserver compete for
resources with that webserver and introduce some security considerations which
could be mitigated by moving containers to a separate host. This feature has
been available since 15.07 and is used in production at the University of
Freiburg and on usegalaxy.org.
First you need to configure a second host to be Docker enabled. In the
following we call this host ``gx-docker`` You need to start the Docker daemon
and bind it to a TCP port, not to a Unix socket as is the default. For example
you can start the daemon with
.. code-block:: console
$ docker -H 0.0.0.0:4243 -d
On your client, the Galaxy webserver, you can now install a Docker client. This
can also be done on older systems like Scientific-Linux, CentOS 6, which do not
have Docker support by default. The client just talks to the Docker daemon on
host ``gx-docker``, and does not run anything itself, locally. You can test
your configuration for example by starting busybox from your client on the
Docker host with
.. code-block:: console
$ docker -H tcp://gx-docker:4243 run -it busybox sh
So far so good! Note, however, that unless restricted by a firewall, this mode
of operation is insecure, as any client could connect and run containers on
``gx-docker``. If this is a concern at your site, follow the instructions in
the Docker documentation to `Protect the Docker daemon socket
<https://docs.docker.com/engine/security/https/>`__.
Now we need to configure Galaxy to use our new Docker host
to start the Interactive Environments. For that we need to edit the Jupyter GIE
configuration, ``jupyter.ini`` to use our custom docker host
.. code-block:: ini
[main]
[docker]
command = docker -H tcp://gx-docker:4243 {docker_args}
docker_hostname = gx-docker
Please adapt your ``command`` as needed.
The Jupyter GIE supports getting and fetching Galaxy history datasets entirely
through the Galaxy API so it is not necessary to share a filesystem with
``gx-docker``. However, other GIE plugins may still require this.
For those GIE plugins, we need to configure a share mount point between the
Docker host and Galaxy. Unfortunately, this can not be a NFS mount. Docker does
not like NFS yet. You could for example use a sshfs mount with the following
script
.. code-block:: bash
if mount | grep ^gx-docker:/var/tmp/gx-docker; then
echo "/var/tmp/gx-docker already mounted."
else
sshfs gx-docker:/var/tmp/gx-docker /var/tmp/gx-docker
echo 'Mounting ...'
fi
This will let Galaxy and the Docker host share temporary files.
Docker Engine Swarm Mode
^^^^^^^^^^^^^^^^^^^^^^^^
As of Docker Engine version 1.12, Docker Engine can be configured to provide a
cluster of Docker Engines in a configuration known as *Docker Engine swarm
mode*. This replaces the previous and similarly named *Docker Swarm*
clustering solution, which is not compatible with swarm mode.
`The Docker Engine swarm mode documentation
<https://docs.docker.com/engine/swarm/>`__ fully explains the differences, but
the major difference is that whereas under Docker Swarm one could run commands
on the swarm with ``docker run``, Docker Engine swarm mode requires one to
create persistent services with ``docker service create`` and to remove those
services once no longer in use with ``docker service rm``.
Galaxy supports both Docker Engine swarm mode and the legacy Docker Swarm
system. Legacy Docker Swarm is supported without any special configuration,
because the containers are still run with ``docker run`` as before. To support
Docker Engine swarm mode, additional configuration is required. Begin by
editing your GIE plugin's ini configuration file (e.g. ``jupyter.ini``) and set
the ``docker_connect_port`` in addition to any other
relevant options. Unless you are using a non-standard Docker image, the correct
value for ``docker_connect_port`` should be suggested to you in the sample
configuration file:
.. code-block:: ini
[docker]
docker_connect_port = 8888
Note that your Galaxy server does not need to be a member of the swarm itself.
It can use the method outlined above in the `Docker on Another Host`_ section
to connect as a client to a Docker daemon acting as a swarm mode manager.
Once configured, you should see that your GIE containers are started and run as
services, which you can inspect using the ``docker service ls`` command and
other ``docker service`` subcommands.
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Galaxy InteractiveTools
=======================
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Build an Interactive Environment
================================
Interactive Environments
=========================
Unfortunately building a Galaxy Interactive Environment (GIE) isn't completely straightforward, and it's
certainly not as simple as picking out an existing container and plugging it
in. Here we'll go through build a "Hello, World" GIE which just displays a file
from a user's history.
Directory Layout
----------------
The GIE directory layout looks identical to that of normal visualization
plugins, for those familiar with developing those
.. code-block:: console
$ tree config/plugins/interactive_environments/jupyter/
config/plugins/interactive_environments/jupyter/
├── config
│ ├── allowed_images.yml.sample
│ ├── jupyter.ini.sample
│ └── jupyter.xml
├── static
│ └── js
│ └── jupyter.js
└── templates
└── jupyter.mako
We'll use the variable ``{gie}`` to stand in for the name of your GIE. It
should match ``[a-z]+``, like ``jupyter`` or ``rstudio``. Here you can see the
``config/`` directory with a ``{gie}.ini.sample`` providing docker and image
configuration, ``allowed_images.yml.sample`` specifies acceptable images,
and then ``{gie}.xml`` which outlines that it is a GIE.
The static directory can hold resources such as JavaScript and CSS files. If
you are actively developing a GIE, you'll need to restart Galaxy after adding
any resources to that file, before they can be accessed in the browser.
Lastly, and most importantly, there's the templates folder. This normally just
contains ``{gie}.mako``, however the Jupyter file needs an extra template file.
First Steps, Configuration
--------------------------
We will name our GIE "helloworld", but you are free to name your's differently.
We'll first need to create the directory structure and set up our
configuration
.. code-block:: console
$ mkdir -p $GALAXY_ROOT/config/plugins/interactive_environments/helloworld/{config,static,templates}
$ cd $GALAXY_ROOT/config/plugins/interactive_environments/helloworld/
Next, you'll need to create the GIE plugin XML file ``config/helloworld.xml``
.. code-block:: xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE interactive_environment SYSTEM "../../interactive_environments.dtd">
<!-- This is the name which will show up in the User's Browser -->
<interactive_environment name="HelloWorld">
<data_sources>
<data_source>
<model_class>HistoryDatasetAssociation</model_class>
<!-- here you filter out which types of datasets are
appropriate for this GIE -->
<test type="isinstance" test_attr="datatype" result_type="datatype">tabular.Tabular</test>
<test type="isinstance" test_attr="datatype" result_type="datatype">data.Text</test>
<to_param param_attr="id">dataset_id</to_param>
</data_source>
</data_sources>
<params>
<param type="dataset" var_name_in_template="hda" required="true">dataset_id</param>
</params>
<!-- Be sure that your entrypoint name is correct! -->
<entry_point entry_point_type="mako">helloworld.mako</entry_point>
</interactive_environment>
Once this is done, we can set up our INI file, ``config/helloworld.ini.sample`` which controls docker interaction
.. code-block:: ini
[main]
# Unused
[docker]
# Command to execute docker. For example `sudo docker` or `docker-lxc`.
#command = docker {docker_args}
# The image argument was moved to "allowed_images.yml.sample"
# Additional arguments that are passed to the `docker run` command.
#command_inject = --sig-proxy=true -e DEBUG=false
# URL to access the Galaxy API with from the spawn Docker container, if empty
# this falls back to galaxy.ini's galaxy_infrastructure_url and finally to the
# Docker host of the spawned container if that is also not set.
#galaxy_url =
# The Docker hostname. It can be useful to run the Docker daemon on a different
# host than Galaxy.
#docker_hostname = localhost
# Try to set the temp directory to world execute - this can fix the issue
# where 'sudo docker' is not able to mount the folder otherwise.
# "finalize namespace chdir to /import permission denied"
#wx_tempdir = False
# Overwrite the IE temp directory. This can be useful if you regular tempdir is
# located on an NFS share, which does not work well as Docker volume. In this case
# you can have a shared sshfs share which you can use as temporary directory to
# share data between the IE and Galaxy.
#docker_galaxy_temp_dir = None
# If your Docker container exposes more then one port, Galaxy needs to know to
# which ports it needs to connect. With this option you can specify the port number
# inside your container to which Galaxy should connect the UI.
#docker_connect_port = None
# Set the following value to false if Docker volumes between Galaxy server and Docker
# container cannot or should not be used.
#use_volumes = True
You'll then need to create the GIE plugin YML file ``allowed_images.yml.sample``
to specify allowed images
.. code-block::
# This file lists acceptable images to allow running.
#
# This allows you, the admin, to create multiple flavours
# for your users to run. E.g. maybe you need a geosciences flavour,
# you can create the image based on our default image and add the
# appropriate `apt-get/pip install` statements.
---
-
image: hello-ie
description: |
hello-ie is for getting you up to speed with
IE development.
We've named our image ``hello-ie``, we'll get to creating that in a minute.
Mako Templates
--------------
Mako templates are very easy to use, and they allow significantly more
flexibility than many other templating languages. It's because of this
flexibility (and ability to write plain python code in them) that GIEs were
possible to develop easily.
In our ``templates/helloworld.mako``, we'll add the following
.. code-block:: html+mako
<%namespace name="ie" file="ie.mako" />
This line says to inherit from the ``ie.mako`` file that's available in
``$GALAXY_ROOT/config/plugins/interactive_environments/common/templates/ie.mako``.
Next we'll add the following
.. code-block:: html+mako
<%
# Sets ID and sets up a lot of other variables
ie_request.load_deploy_config()
# Define a volume that will be mounted into the container.
# This is a useful way to provide access to large files in the container,
# if the user knows ahead of time that they will need it.
user_file = ie_request.volume(
hda.file_name, '/import/file.dat', mode='ro')
# Launch the IE. This builds and runs the docker command in the background.
ie_request.launch(
volumes=[user_file],
env_override={
'custom': '42'
}
)
# Only once the container is launched can we template our URLs. The ie_request
# doesn't have all of the information needed until the container is running.
url = ie_request.url_template('${PROXY_URL}/helloworld/')
%>
That mako snippet loaded the configuration from the INI files, launched the
docker container, and then built a URL to the correct endpoint, through the
Galaxy NodeJS proxy. Additionally we've set an environment variable named ``CUSTOM``
with the value ``42`` to be passed to the container, and we've attached the dataset the
user selected (available in ``hda``) to the container as a read-only volume.
We'll continue appending to our ``helloworld.mako`` the HTML code that's actually displayed to the user, when this template is rendered
.. code-block:: html+mako
<html>
<head>
<!-- Loads core Galaxy javascript libraries for a 'generic' entrypoint. -->
${ ie.load_default_js() }
<!-- This will load code from static/main.js, often used to handle things like login -->
${ ie.load_default_app() }
</head>
<body>
<script type="text/javascript">
// see $GALAXY_ROOT/config/plugins/interactive_environments/common/templates/ie.mako to learn what this does
${ ie.default_javascript_variables() }
var notebook_login_url = 'unused';
var notebook_access_url = '${ notebook_access_url }';
// Load notebook
// The load_notebook function will eventually append an IFrame to the <div id="main" /> below.
IES.load_when_ready(ie_readiness_url, function(){
load_notebook(notebook_access_url);
});
</script>
<div id="main" width="100%" height="100%">
</div>
</body>
</html>
We've glossed over some of the features of this file, but most IEs do a significant amount
of "magic" in the top half of the mako template. For instance, the Jupyter notebook:
- If the user is trying to run the Jupyter GIE Visualization on an existing notebook in their history, then that gets loaded into the docker container via the temp directory and set as the default notebook
- Otherwise a default notebook is built for the user.
The RStudio notebook:
- generates a random password and configures the image to use this password
- Copies in an RData file if the user has loaded one
- sets some custom environment variables.
Connecting the User to the Container via Javascript
---------------------------------------------------
With the mako template above finished, if you were to load this in your
browser, not a lot would happen because we haven't built the hello-ie image,
and we haven't used Javascript to connect the user with the container. In the
tail end of the template, we set a variable ``notebook_access_url``. These are
partially a legacy of how things used to be done and you're welcome to clean up
your code according to your desires. Galaxy's NodeJS proxy handles the
authentication of users, so you don't have to worry about it, and can just
assume that only the correct user will have access to a given notebook.
In the ``static/`` directory, we generally create a ``js/`` directory below that,
and create a ``main.js`` file in there.
That file will have a function, ``load_notebook`` which will check if the GIE is available,
and when it is, display it to the user.
We start by writing the load notebook function, which is pretty generic
.. code-block:: javascript
// Globals
var IES = window.IES;
// Load an interactive environment (IE) from a remote URL
// @param {String} notebook_access_url: the URL embeded in the page and loaded
function load_notebook(notebook_access_url){
// When the page has completely loaded...
// Test if we can access the GIE, and if so, execute the function
// to load the GIE for the user.
IES.test_ie_availability(notebook_access_url, function(){
IES.append_notebook(notebook_access_url);
});
}
This function will display a spinner to the user to indicate process, make multiple requests
to ``notebook_access_url`` and display the GIE to the user in an iframe. That MUST return a 200 OK for the ``append_notebook`` function to ever be called. 302s do not count!
Historically, the GIE process involved a complex dance of:
- generating a random password in the mako template
- setting it as a javascript variable
- passing it to the docker container
- once the container was available, have the javascript automatically log a
user in (something browsers try to prevent since that's otherwise an XSS
vulnerability.)
- hope everything worked
You may wish to look at the Jupyter and RStudio GIEs for examples of the complex things that can be done at every step.
The GIE Container
-----------------
We'll build a simple container that just displays the dataset a user has
selected to them. Remember when we attached a volume to the container? We'll
make use of that now.
GIE Containers (often) consist of:
- Dockerfile
- NGINX Proxy configuration
- A custom startup script/entrypoint
- A script to monitor traffic and kill unused containers
We have to monitor the container's traffic and kill off unused containers,
because no one is watching them. The user launches the container in Galaxy, and
Galaxy immediately forgets the container exists. Thus, we say that if a
container has no connections to TCP connections to itself, then it should
commit suicide by killing the root process.
Here's an example ``Dockerfile`` for our helloworld container
.. code-block:: dockerfile
FROM alpine
# These environment variables are passed from Galaxy to the container
# and help you enable connectivity to Galaxy from within the container.
# This means your user can import/export data from/to Galaxy.
ENV DEBIAN_FRONTEND=noninteractive \
API_KEY=none \
DEBUG=false \
PROXY_PREFIX=none \
GALAXY_URL=none \
GALAXY_WEB_PORT=10000 \
HISTORY_ID=none \
REMOTE_HOST=none \
DOCKER_PORT=none \
CORS_ORIGIN-none
RUN apk update && \
apk add \
wget procps nginx python py2-pip net-tools nginx git patch
# Our very important scripts. Make sure you've run `chmod +x startup.sh
# monitor_traffic.sh` outside of the container!
ADD ./startup.sh /startup.sh
ADD ./monitor_traffic.sh /monitor_traffic.sh
# /import will be the universal mount-point
# The Galaxy instance can copy in data that needs to be present to the
# container
RUN mkdir -p /import /web/helloworld /run/nginx
# Nginx configuration
COPY ./proxy.conf /proxy.conf
COPY ./index.html /web/helloworld/
RUN chmod ugo+r /web/helloworld/index.html
VOLUME ["/import"]
WORKDIR /import/
# EXTREMELY IMPORTANT! You must expose a SINGLE port on your container.
EXPOSE 80
CMD /startup.sh
If you have questions on this, please feel free to contact us on IRC
(`irc.freenode.net#galaxyproject <https://webchat.freenode.net/?channels=galaxyproject>`__).
The proxy configuration is interesting, here we'll point NGINX to reverse proxy
a service running on ``:8000`` inside the container. That port will be hosting
a python process which serves up the directory contents of ``/import``, i.e.
the file the user selected which was mounted as a volume into
``/import/file.dat``
.. code-block:: nginx
events {
worker_connections 1024;
}
http {
include mime.types;
default_type application/octet-stream;
sendfile on;
keepalive_timeout 65;
server {
listen 80;
server_name localhost;
access_log /var/log/nginx/localhost.access.log;
root /web/;
location PROXY_PREFIX/ {
alias /web/;
}
rewrite ^(.*)/helloworld/(.*\.dat)$ PROXY_PREFIX/helloworld/dir/$2;
location PROXY_PREFIX/helloworld/dir/ {
proxy_buffering off;
proxy_pass http://127.0.0.1:8000/;
proxy_redirect http://127.0.0.1:8000/ PROXY_PREFIX/helloworld/dir/;
}
}
}
Below is our ``index.html`` file
.. code-block:: html
<html>
<head>
</head>
<body>
<h1>Welcome to Hello-World IE</h1>
<p>
There is one service running, a <a href="dir/">directory
listing</a>. (We originally had more but the github repository
for the flask app we were using disappeared.)
</p>
</body>
</html>
And here we'll run that service in our ``startup.sh`` file
.. code-block:: bash
#!/bin/bash
# First, replace the PROXY_PREFIX value in /proxy.conf with the value from
# the environment variable.
sed -i "s|PROXY_PREFIX|${PROXY_PREFIX}|" /proxy.conf;
# Then copy into the default location for ubuntu+nginx
cp /proxy.conf /etc/nginx/sites-enabled/default;
# Here you would normally start whatever service you want to start. In our
# example we start a simple directory listing service on port 8000
cd /import/ && python -mSimpleHTTPServer &
# Launch traffic monitor which will automatically kill the container if
# traffic stops
/monitor_traffic.sh &
# And finally launch nginx in foreground mode. This will make debugging
# easier as logs will be available from `docker logs ...`
nginx -g 'daemon off;'
Lastly, our ``monitor_traffic.sh`` file is often re-used between containers, the only adjustment being the port that is looked at
.. code-block:: bash
#!/bin/bash
while true; do
sleep 60
if [ `netstat -t | grep -v CLOSE_WAIT | grep ':80' | wc -l` -lt 3 ]
then
pkill nginx
fi
done
With those files, ``monitor_traffic.sh``, ``Dockerfile``, ``startup.sh``, and ``proxy.conf``, you should be able to build your ``hello-ie`` container
.. code-block:: bash
$ cd hello-ie
$ docker build -t hello-ie .
Now, if everything went smoothly, you should be able to restart Galaxy and try out your new GIE on a tabular or text file!
Debugging
---------
When you launch your new GIE in Galaxy, your Galaxy logs should show something like the following:
.. code-block:: console
Starting docker container for IE helloworld with command [docker run --sig-proxy=true -e DEBUG=false -e "GALAXY_URL=http://localhost/galaxy/" -e "CORS_ORIGIN=http://localhost" -e "GALAXY_WEB_PORT=8000" -e "HISTORY_ID=f2db41e1fa331b3e" -e "CUSTOM=42" -e "GALAXY_PASTER_PORT=8000" -e "PROXY_PREFIX=/galaxy/gie_proxy" -e "API_KEY=1712364174a0ff79b34e9a78fee3ca1c" -e "REMOTE_HOST=127.0.0.1" -e "USER_EMAIL=hxr@local.host" -d -P -v "/home/hxr/work/galaxy/database/tmp/tmp5HaqZy:/import/" -v "/home/hxr/work/galaxy/database/files/000/dataset_68.dat:/import/file.dat:ro" hello-ie]
Here's the docker command written out in a more readable manner:
.. code-block:: console
$ docker run --sig-proxy=true \
-d -P \
-e "API_KEY=1712364174a0ff79b34e9a78fee3ca1c" \
-e "CORS_ORIGIN=http://localhost" \
-e "CUSTOM=42" \
-e "DEBUG=false" \
-e "GALAXY_PASTER_PORT=8000" \
-e "GALAXY_URL=http://localhost/galaxy/" \
-e "GALAXY_WEB_PORT=8000" \
-e "HISTORY_ID=f2db41e1fa331b3e" \
-e "PROXY_PREFIX=/galaxy/gie_proxy" \
-e "REMOTE_HOST=127.0.0.1" \
-e "USER_EMAIL=hxr@local.host" \
-v "/home/hxr/work/galaxy/database/tmp/tmp5HaqZy:/import/" \
-v "/home/hxr/work/galaxy/database/files/000/dataset_68.dat:/import/file.dat:ro" \
hello-ie
As you can see, a LOT is going on! We'll break it down further:
- ``-d`` runs the container in daemon mode, it launches and the client
submitting the container finished
- ``-P`` randomly assigns an unused port to the container for each ``EXPOSE``d
port from our ``Dockerfile``. This is why you must expose a port, and only
one port.
- A large number of environment variables are set:
- The user's API key is provided, allowing you to access datasets and
submit jobs on their behalf. If you have an environment like
Jupyter/RStudio, it is **highly recommended** that you provide some magic
by which the user can use their API key without embedding it in the
notebook. If you do embed it somehow in a document that gets saved to
their history, anyone can impersonate that user if they get a hold of it.
In the Jupyter GIE we have a variable that just runs
``os.environ.get('API_KEY')`` to avoid embedding it in their notebook.
- A CORS Origin is provided for very strict servers, but it may be easier
to simply void CORS requirements within the nginx proxy in your
container.
- Custom variables specified in your ``launch()`` command are available
- A ``DEBUG`` environment variable should be used to help admins debug
existing containers. You should use it to increase logging, not cleanup
temporary files, and so on.
- ``GALAXY_PASTER_PORT`` (deprecated) and ``GALAXY_WEB_PORT`` are the raw
port that Galaxy is listening on. You can use this to help decide how to
talk to Galaxy.
- ``GALAXY_URL`` is the URL that Galaxy should be accessible at. For
various reasons this may not be true. We recommend looking at our
implementation of `galaxy.py
<https://github.com/bgruening/docker-ipython-notebook/blob/15.07/galaxy.py>`__
which is a small utility script to provide API access to Galaxy to get
and fetch data, based on those environment variables.
- The ``HISTORY_ID`` of the current history the user is on is provided. In
the Jupyter/RStudio containers, we provide a dead simple method for users
to download datasets from their current history which will be visible to
them on the right hand side of their screen.
- A ``PROXY_PREFIX`` is provided which should be used in the nginx conf.
- ``REMOTE_HOST`` is another component used to test for a possible Galaxy
access path
- The user's email is made available, very convenient for webservices like
Entrez which require the user's email address. You can pre-fill it out
for them, making their life easier.
- Two volumes are mounted, one a temporary directory from Galaxy (rw), and one
the dataset the user selected (ro).
- and finally the image is specified.
Most of this information is usually required to build friendly, easy-to-use
GIEs. One of the strong points of GIEs is their magic interaction with Galaxy.
Here we've mounted a volume read-only, but in real life you may wish to provide
connectivity like Jupyter and RStudio provide, allowing the user to load
datasets on demand for interactive analysis, and then to store analysis
artifacts (and a log of what was done inside the container, à la Jupyter's
"notebooks") back to their current history.
If everything went well, at this point you should see a directory listing show up:
.. image:: interactive_environments_success.png
If you find yourself encountering difficulties, the "Hello, World" IE is
available in a `GitHub repo <https://github.com/hexylena/hello-world-interactive-environment/releases/tag/v15.10>`__, and there are people on the IRC channel who can help debug.
Galaxy Interactive Environments are deprecated and were the precursors to the current :doc:`Galaxy Interactive Tools <../admin/special_topics/interactivetools>`.
You can find information about Galaxy Interactive Environments in `older versions <https://docs.galaxyproject.org/en/release_21.09/admin/special_topics/interactive_environments.html>`__ of this document.
doc/source/dev/interactive_environments_success.png
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