# LabOtter Marking Workflow Marking courseworks which utilize LabOtter (LabTS + Otter Grader) is relatively straight forward, and the recommended workflow is the same as for other courses: 1. Bulk download submissions on Emarking 2. Fill in the marking page for each student, with comments where appropriate. Keep track of the student's grades in a spreadsheet / on paper 3. Bulk upload the resulting PDFs for students to see, and enter the recorded grades on the corresponding CATe assignment (API WIP) ## Submission Layouts The layout of a submission will take two possible forms, depending on whether PDF generation was succesful or not ### Succesful PDF Generation Each PDF will consist of 1. The assignment information page (generated by Emarking) 2. The public and hidden autograder test results 3. (Optionally) PDF renderings of all qualitative questions requiring manual marking 4. (Optionally) a markscheme which needs to be populated by TAs 5. A complete printout of the student's notebook This means that for most submissions you should only need to look at the first few pages (items 1-4 above). ### Unsuccesful PDF Generation For a number of reasons (typically LaTeX syntax errors / lack of support) LabOtter may fail to convert a student's notebook into a PDF. We do not penalize students for this. In this case PDFs consist of: 1. The assignment information page (generated by Emarking) 2. The public and hidden autograder test results 4. (Optionally) a markscheme which needs to be populated by TAs 5. Links to the student's gitlab repository and the hash corresponding to their submitted commit In these cases, for courseworks containing qualitative questions, the TA will need to open the student's notebook inside of gitlab and find the relevant qualitative questions. Additional blank pages are provided after item 5 for longer feedback if needed.
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Alex Spies
PhD Student @ ICL - Neurosymbolic & Object-Centric Deep Learning
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Paperspace Usage Information

Preliminary You should have been provided with Paperspace credits - these will allow you to spawn paid instances with better GPUs than what colab or paperspace offer on their free-tier. Additionally, unless you enable auto-shutdown, these instances will stay awake permanently, and not abort half-way through training. Instance Setup Once you have signed up for Paperspace's gradient platform and applied your coupon: Go to console.paperspace.com and create a under the "ICL Deep Learning 2023" team (selected at top left) Create a notebook within that project. We recommend using the PyTorch 1.10 GPU runtime Select a machine - you can use the free-tier CPU or GPU machines for most of the coursework questions, but when training your models we recommend using a paid P4000 instance. Choose your auto-shutdown period carefully, base on the network's training time, and whether you will need to go AFK You can directly connect the instance to your gitlab repository by

1/25/2023
DoC Environment Setup

SSH You should create an ssh-key pair to allow password-free login from your device to the lab machines (and jump shell). I recommend following this guide. On your local computer you'll want to setup the ~/.ssh/config as follows (with e.g. <USERNAME>=afs219) # This is the Jump box which allows us to access doc machines # from outside of Uni (without the VPN) Host uniJumpShell User <USERNAME> # Sometimes a shell may go down - can choose from 5

10/28/2022
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