1. Project Report (Wiki/Read the docs | Due Dec. 17th @ Noon)
Overview of project
What is the gantry phenotyping platform
What sensors does it have
How much data does it produce
Why do we need to process data coming from this machine ASAP?
Overview of workflow and the main features of it
Scalable, flexible, modular, extensible, high performant, high reliability (checks and retries errors)
Overview of the team and team responsibilities
Role of each person (a couple of words)
Description of data: which sensors, what do they do
How much data
Where/how were they obtained.
What was done to clean them for processing.
Link, description, and instructions of using code to get and clean data.
Analysis Workflow/Code - Workflow team
Description of analytical workflow.
Description of computational framework (master/worker with Workqueue and Makeflow)
Instructions for use
Description of datasets
Describe scaling methods
Description of how workflow can be modified
How are results managed
How are results evaluated for faults
Where can researchers find the results
How long it took to run the full dataset:
How much would this cost to do on AWS (e.g., for each workflow, approximate cost to process one day’s worth of data)
Post-Mortem Analysis (SubPage) Documentation
One for each team!
Focus on team processes; not technical problems
What worked well
What didn't work well
What you would do differently
2. Project Code (GitHub) and Documentation (Read the Docs)
How to use
How to scale (what is needed)
Description of output
Warnings and caveats
Where do get additional help
3. Client Presentation (In-Class | Due Dec. 17 @ 8AM)
Introduction (Sateesh Peri)
Overview of project
Overview of workflow
Overview of solution
Overview of results
Data: obtaining, cleaning, about the final dataset.
Analysis workflow: overview, obtaining, how to use
Benchmarks: Scaling, full analysis time
Results and result management
Where is it?
What does it take to rerun it?
Show an example of making a modification to workflow (adding a new extractor) an rerunning
Documentation and training materials
How would a new person take the code and get to work?
Live demo (John Xu & Class)
Show us the system in action!
You have done some amazing work, here is your chance to put a bow on it
Team members, roles and responsibilities
Help from external teams (cctools, David LB group, class clients, CyVerse people, etc.) Note -- always make sure to give credit (sometimes extra credit) for anyone who has helped make the project possible
Computational infrastructure: UA HPC, CyVerse, XSEDE, JetStream
4. Teammate Evaluations
You will lose 50% of your final score if this is not completed.
This is for each sub team
Note: Feel free to review team members in other teams (especially if someone has gone above and beyond to help)