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# Galaxy from an administrator's point of view
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Useful when presenting. --- ![Picture with 125+ platforms for using galaxy written and a number of screenshots of those galaxies.](../images/use-resource-banner.png) --- # Where can Galaxy run? * Cloud (SaaS) - A usegalaxy.* site [Main](https://usegalaxy.org) [Europe](https://usegalaxy.eu) [Australia](https://usegalaxy.org.au/) - [Public Galaxy Servers](https://galaxyproject.org/use) - [Amazon EC2 or MS Azure](https://launch.usegalaxy.org/) - Semi-private cloud (e.g.: [NeCTAR](https://nectar.org.au/), [Jetstream](http://jetstream-cloud.org/)) * 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](https://galaxyproject.org/use/) Supported | Main | Local | Cloud | Appliance --------- | ---- | ----- | ----- | --------- Your data sets are moderately sized | yes | yes | yes | yes Your computation requirements are moderate | yes | yes | yes | yes You want to share your Galaxy objects with others | yes | yes | yes | yes All needed Tools are installed on Main | yes | ? | yes | yes Your data sets are very large | no | ? | yes | yes Your computation requirements are very large | no | ? | yes | yes You have absolute data security requirements | no | yes | yes | yes No network transfer of data | no | yes | no | yes --- ## Reasons to Install Your Own Galaxy - You want to run a local [production Galaxy](https://docs.galaxyproject.org/en/latest/admin/production.html) - You want to develop [Galaxy tools](https://galaxyproject.org/admin/tools/add-tool-tutorial) - You want to [Develop Galaxy](https://galaxyproject.org/develop/) itself - [Install](https://galaxyproject.org/admin/tools/add-tool-tutorial) and use tools unavailable on [public Galaxies](https://galaxyproject.org/public-galaxy-servers) - Use sensitive data (e.g. clinical) - Process large datasets that are too big for public Galaxies - [plug-in](https://galaxyproject.org/admin/internals/data-sources) new datasources --- # Software Requirements Required: - Galaxy is written in Python and depends on **Python 3.5** or newer Minimal [production](https://docs.galaxyproject.org/en/master/admin/production.html) requirements: - [PostgreSQL](../tutorials/database/slides.html) - 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 ??? - 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: Users | Resource estimate ----------|------------------- 1 - 5 | 1 core, 1GB, 10 TB 5 - 20 | 2 cores, 2 GB, 40 TB 20 - 40 | 8 cores, 8 GB, 200 TB 40+ | multiple hosts, 16 GB/host, 500 TB, dedicated DB host 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 `job_conf.yml` - 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](https://github.com/bgruening/docker-galaxy-stable/) - Production server: configuration management (e.g. Ansible) In future: - Alternative to git clone: Galaxy wheel in PyPI --- # Deployment Best Practices - **Use configuration management** - [Ansible](http://docs.ansible.com) for which Galaxy Project maintains roles ([tutorial](https://training.galaxyproject.org/training-material/topics/admin/tutorials/ansible/slides.html)) - 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) --- ## Related tutorials --- ## Thank You! This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors!
This material is licensed under the Creative Commons Attribution 4.0 International License