Galaxy from an administrator's point of view

Contributors

Authors: Valentin Marcon Nate Coraor Björn Grüning Helena Rasche

Questions

• What options to deploy Galaxy do I have?

• Which platforms are supported by Galaxy?

• What requirements does Galaxy have?

Objectives

• Learn about different options about Galaxy deployment.

• Make an educated decision about your preferred deployment model.

Where can Galaxy run?

• Cloud (SaaS)
  • A usegalaxy.* site: Galaxy France, Galaxy Main, Galaxy Europe, Galaxy Australia
  • Public Galaxy Servers
  • Amazon EC2 or MS Azure
  • Semi-private cloud (e.g.: NeCTAR, Jetstream)
• Private cloud (build your own Galaxy SaaS)
• Cloud (IaaS)
  • Any cloud
• Scalable Local Server
  • Dedicated or shared compute cluster(s)
  • Cloud compute resources
• Standalone Local Server

Choosing where to run

Resource Directory

Reasons to Install Your Own Galaxy

• You want to run a local production Galaxy
• You want to develop Galaxy tools
• You want to Develop Galaxy itself
• Install and use tools unavailable on public Galaxies
• Use sensitive data (e.g. clinical)
• Process large datasets that are too big for public Galaxies
• plug-in new datasources

Software Requirements

Required:

• Galaxy is written in Python and depends on Python 3.8 or newer

Minimal production requirements:

• PostgreSQL
• Reverse proxy server (NGINX, Apache)

Hardware Requirements

This depends:

• What do you intend to run?
• Where do you intend to run it?

If possible, run the Galaxy server separate from Galaxy jobs

Storage will usually be the biggest concern

Speaker Notes

• Depends on your available infrastructure
• If you are storage limited, can be addressed by policy of deleting old/unused histories
• If you are compute limited, can be addressed with queue limits

Server Hardware Requirements

Based on concurrent user count and assuming separate compute for jobs:

Storage is more difficult to estimate since it is, like compute, analysis and policy dependent

template: left-aligned

Galaxy Storage Philosophy

• Foster transparency and reproducibility
• Data is always created, never overwritten
• Copying history or library datasets associate them with the original file on disk without an actual copy
• By default, data is never really deleted unless explicitly instructed
• Even deleted data can be undeleted unless forcibly purged

template: left-aligned

Storage Requirements

An “average” 2018 NGS analysis (by Anton Nekrutenko): 66 GB

10 users, 10 histories: > 6 TB

Solutions:

• Quotas
• Set job limits in the job conf
• Clean up deleted data (with a cron job)
• Forced removal based on age
• Users can configure their workflows to delete intermediate tool outputs
• Data libraries for common data
• Public servers: require email verification (and watch for duplicates)
• Plug in more/heterogeneous storage using Object Store configuration

Compute Requirements

This depends:

• What tools will your users be using?
  • What are their requirements?
• In general, the most commonly used tools use a single core
  • But can use lots of memory!
• Some compute-intensive tools use multiple cores

usegalaxy.org allocates from 8 GB/core to 16 GB/core

Connecting Galaxy to clusters/HPC is covered in the advanced section.

Making plans

Before deploying your first Galaxy server:

• Figure out where Galaxy will be stored
  • Make sure it will be accessible to any eventual compute
• Figure out where data will be stored
  • Make sure it will be accessible to any eventual compute

Galaxy deployment options

• As developer: git clone https://github.com/galaxyproject/galaxy.git
• Ready-to-use locally: Docker
• Production server: configuration management (e.g. Ansible)

In future:

• Alternative to git clone: Galaxy wheel in PyPI

Deployment Best Practices

• Use configuration management
  • Ansible for which Galaxy Project maintains roles (tutorial)
  • Other systems are possible (Chef, Puppet, SaltStack, CFEngine) but do not have project-maintained roles.
• Whether you use configuration management or not, record every change you make on a version control system (e.g. git):
  • Large, complex deployments grow organically
  • If you don’t know what you did, you can’t do it again

template: left-aligned

System Administration Best Practices

• Take security seriously
• Update Galaxy when security updates are released
• Follow OS security best practices
• Privilege separate code/job/data ownership
• Write protect Galaxy and data if you can
• Read-only cluster mounts

Back up everything (except that which is managed by configuration management)

Key Points

• Galaxy is scalable from personal computers to huge HPC and cloud-instances.
• Amount of expected users, types of common tasks, and storage capabilities have a big impact on the deployment.

Thank you!