Overview
What this challenge is about.
Pick a representative fine-tune job (an open 7B model on a public instruction dataset is fine). Define the search space: NCCL_ALGO, NCCL_PROTO, num_workers, prefetch_factor, gradient_accumulation, microbatch size. Use Optuna or a clean grid+bandit hybrid to explore 30-60 configurations under a fixed GPU-hour budget. Report tokens-per-second-per-GPU with confidence intervals and identify the top-3 most impactful knobs. Package the result as a one-page recipe team leads can apply, plus a Python helper that auto-suggests a starting config given (model size, GPU count, dataset size).
The Brief
What you'll do, and what you'll demonstrate.
Find the highest-impact knobs for distributed-training throughput on the cluster and ship a recipe + helper script the team applies on day one.
Earning criteria — what you'll demonstrate
- Define a meaningful search space for distributed-training knobs
- Run a budget-constrained hyperparameter search at cluster scale
- Quantify the marginal impact of each knob honestly
- Package systems knowledge as a reusable team tool
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
MLOps Engineer
Tuning distributed-training systems for throughput and shipping a reusable recipe is the work that platform MLOps engineers do on training infrastructure teams.
This challenge sharpens
- distributed-training
- nccl
- throughput-modeling
Machine Learning Engineer
Hands-on knowledge of NCCL, dataloader, and gradient-accumulation tuning is the systems-MLE skill set that startups training their own models hire for.
This challenge sharpens
- distributed-training
- pytorch
- hyperparameter-tuning
AI Solutions Architect
Translating cluster-tuning wins into runway extension and a deployable recipe is core AI solutions architecture for cloud providers and consulting firms.
This challenge sharpens
- throughput-modeling
- distributed-training
- experiment-design