Hello everyone,

I'm happy to share I've published my first python package as a contributor to the CS249r project led by the Harvard-Edge Lab. Gonna code more :)

Check the package here: https://pypi.org/project/mlsysim/0.1.1/

Just pip install mlsysim

Read the whole story here:

ML systems today are designed largely on intuition. Senior engineers do back-of-envelope math to figure out whether a workload fits on a GPU, what the memory bandwidth bottleneck will be, whether a 64-node H100 cluster can hit a 50ms latency SLA, what the total cost of ownership looks like over 14 days in Quebec. mlsysim formalises that math.

It's a first-principles analytical framework for ML infrastructure. Five layers, from workload representation down to execution and design space search. You describe your cluster in a declarative YAML file, define your constraints, and the engine returns a three-lens scorecard: Feasibility, Performance, and Macro economics.…