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 (
- 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 (
- 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.
Updated: November 12, 2019Edit this Page via GitHub Comment by Filing an Issue Have Questions? Ask them here.