Where to run my code?

For R and Python, you can run the code you have written locally on your computer, or remotely on the Linux clusters. For running remotely, you can either run on a cluster node shared with other users, or reserve a node for your exclusive use for a limited time.

Running on your computer

• Pro: immediate access, familiar environment
• Con: limited CPU, memory and disk resources for large tasks (eg. aligning RNASeq reads, variant calling, etc.)

Running remotely on shared cluster node (Rhino)

• Pro: higher CPU, memory and disk resources
• Con: need to transfer files to Hutch servers, requires Internet connection, can be temporarily slow if Rhino has many concurrent users

Running remotely on reserved cluster node (Gizmo)

• Pro: higher CPU, memory and disk resources, and you’re the exclusive user
• Con: need to transfer files to Hutch servers, requires Internet connection, if you request a very powerful computer, you may have to wait a while for one to become available

When using the Fred Hutch computing clusters, users should access these programming languages via the environmental modules (eg. ml R rather than simply R in Rhino). Doing this will ensure reproducibility of your code and that you’re using the latest software available. More information about environment modules are available here.