When you are working on the lab machines, there are three places where you can directly access and store your files via a file system:
\\acsnfs4.ucsd.edu\unix_home\linux\ieng6\cs125s\userThere is also a Windows link to it under the following path:
C:\ClassResources\cs125s storage directory ...
This directory should have a large amount of available space (3GB). If you keep your working directory in a remote share, use this location instead of your student account (H: drive) since it has a much larger capacity.
A more convenient way to use this directory is to mount it under a drive letter (some programs need this, e.g., when using a git shell). You can map the share path above to a drive letter using the following steps:
Start -> Computer -> Map network drive (in menu bar)
There are also a few places where you access and store files over the network:
Given all of the places where your data can live, here is one suggested approach for organizing your data and getting your work done:
Source repo: Store your source code, config files, models, etc., in your source repository. Perhaps store the header files and binaries of libraries (as long as they are not too large). You want to keep your source repo reasonably sized because you will be checking it out many times over the quarter.
cs125s share: Place large files (large images, textures, compiled libraries, etc.) in the cs125s network drive. These are files that will not change often (or at all) and are too large to store in the source repo.
Lab machine: When you come to the lab to work and login to a machine, you have a couple of options. One is to do a checkout from your repo to a local directory on the machine (e.g., under C:\Workarea) and do all of your compiling and debugging there. This will have the best performance since you'll be working with files on the local drive. Note, though, that you need to commit any modifications back to the repo before you leave.
Another option is to do a checkout to the cs125s network share. Files there will persist across logins, reboots, and new images on the lab machines. But, performance is worse because all file data has to be read and written over the network while you are working.