Skip to content

Latest commit

 

History

History
134 lines (97 loc) · 4.92 KB

ex1-jupyter.md

File metadata and controls

134 lines (97 loc) · 4.92 KB
Raw

galaxy logo

Galaxy Administrators Course

Exercise - Running the Jupyter Galaxy Interactive Environment (GIE)

Authors: Nate Coraor. 2017

Introduction

Galaxy Interactive Environments provide an interface between containerized interactive analysis tools and the Galaxy analysis environment (history). There are a number of GIEs available, but the most frequently used (and easiest to configure) is the Jupyter GIE. A proxy server written in Node.js connects the client (browser) to the Dockerized Jupyter notebook.

Section 1 - Install Components

Begin by installing Docker and Node.js

$ sudo apt install docker.io npm nodejs-legacy

Next, allow the galaxy user to use Docker without requiring sudo by adding it to the docker group:

$ sudo usermod -a -G docker galaxy

While not explicitly necessary, it's best to pull the Docker image we'll use first. Otherwise, this occurs the first time the Jupyter GIE is called from the Galaxy UI. Because this takes a long time, attempts to use Jupyter in the UI will time out until the image has been downloaded.

$ sudo -u galaxy docker pull quay.io/bgruening/docker-jupyter-notebook:17.09

Section 2 - Configure Node.js proxy

Part 1 - Configure proxy

In a separate terminal window, open a shell as the galaxy user and run:

$ sudo -Hsu galaxy
$ cd /srv/galaxy/server/lib/galaxy/web/proxy/js
$ npm install
$ cd /srv/galaxy/server/config/plugins/interactive_environments/jupyter/config
$ cp jupyter.ini.sample jupyter.ini
$ cp allowed_images.yml.sample allowed_images.yml

Have a look in the config files you have just created. Edit allowed_images.yml and change the image:

    image: quay.io/bgruening/docker-jupyter-notebook:17.09

Next, edit /srv/galaxy/config/galaxy.ini and add the following:

interactive_environment_plugins_directory = /srv/galaxy/server/config/plugins/interactive_environments
dynamic_proxy_external_proxy = True
dynamic_proxy_manage = False
dynamic_proxy_bind_port = 8800
dynamic_proxy_debug = True
dynamic_proxy_session_map = /srv/galaxy/config/gie_proxy_session_map.sqlite
dynamic_proxy_prefix = gie_proxy
dynamic_proxy_bind_ip = 127.0.0.1

Once saved, exit your galaxy user shell and return to the ubuntu user. Then, restart Galaxy:

$ sudo supervisorctl restart galaxy:

Part 2 - Configure nginx

We'll now proxy the Node.js proxy with nginx. This is done by adding to /etc/nginx/sites-available/galaxy. Inside the server { ... } block, add:

    # Global GIE configuration
    location /gie_proxy {
        proxy_pass http://localhost:8800/gie_proxy;
        proxy_redirect off;
    }

    # Project Jupyter / IPython specific. Other IEs may require their own routes.
    location ~ ^/gie_proxy/jupyter/(?<nbtype>[^/]+)/api/kernels(?<rest>.*?)$ {
        proxy_pass http://localhost:8800/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";
    }

Once saved, restart nginx to reread the config:

$ sudo supervisorctl restart nginx

Part 3 - Configure proxy to start with supervisor

All that remains is to start the proxy, which we'll do with supervisor. Add to /etc/supervisor/conf.d/galaxy.conf:

[program:gie_proxy]
command         = node /srv/galaxy/server/lib/galaxy/web/proxy/js/lib/main.js --ip 127.0.0.1 --port 8800 --sessions /srv/galaxy/config/gie_proxy_session_map.sqlite --cookie galaxysession --verbose
directory       = /srv/galaxy/server/lib/galaxy/web/proxy
umask           = 022
autostart       = true
autorestart     = true
startsecs       = 5
user            = galaxy
numprocs        = 1
stdout_logfile  = /srv/galaxy/log/gie_proxy.log
redirect_stderr = true

Once saved, start the proxy by updating supervisor:

$ sudo supervisorctl update

Section 3 - Test

Reload the Galaxy UI and choose Interactive Environments from the Visualization menu in the masthead. Alternatively, you can click the bar graph icon on any dataset in your history and choose the Jupyter option from the popup menu. You should see a spinner icon and eventually the Jupyter interface will load. You can see that the container is running using docker ps:

$ sudo docker ps
CONTAINER ID        IMAGE                                               COMMAND                  CREATED             STATUS              PORTS                     NAMES
2e65f3ef0c53        quay.io/bgruening/docker-jupyter-notebook:16.01.1   "tini -- /bin/sh -c /"   11 seconds ago      Up 9 seconds        0.0.0.0:32768->8888/tcp   agitated_liskov

In addition to arbitrary Python, you can use the get() and put() commands (get() takes a single argument - an integer id of a dataset in your current history) to interact with your history.