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Install Fsl Windows8/29/2023 ![]() Output and temporary files = ~/fsl_groups/fslg_piccololab/compute.Also commit these to a Git repository and push them to BitBucket or GitHub as you develop. Code, scripts, and test data files = Your home directory.Please do not make copies of these files. Piccolo will place them in a subdirectory. Input data files = ~/fsl_groups/fslg_piccololab. ![]() If you have a UNIX-based system and want to connect to FSL without using a password, follow the instructions here. It will allow you to connect using either a terminal window or an Explorer-like window. If you have a Windows machine, you can install the Bitvise SSH tool. (In some cases, you may need to specify something other than this.) Mention that you anticipate executing jobs that typically require between 1 and 64 GB memory per job and 1-16 processing cores per job, depending on the specific needs of your project. Tell them that you will be executing custom Python and/or R scripts, that you expect to execute only single-node jobs, and that you expect to install bioinformatics software and execute it. When it asks what you plan to do with your account, tell them briefly about the research you will be doing. If you need this resource, request an account from the Office of Research Computing. Some research projects require use of the BYU Supercomputer.
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