DDPM — MNIST (NVIDIA A100)
A demo model from the Data & Impact Accounting (DIA) lab. It performs denoising diffusion generative model (MNIST) via from-scratch DDPM, with the base model trained from scratch, trained on NVIDIA A100.
The point of this repo is not the model itself but its dia_report — a
standardized record of the energy, carbon, and water used to train it, embedded
in this card's metadata.
This footprint feeds the DIA dashboard, which rolls up a base model and all its derivatives to show the cumulative carbon, water, and energy cost of a model family.
Training footprint
| Metric | Value |
|---|---|
| Hardware | 1× NVIDIA A100-SXM4-80GB |
| Compute | 0.5017 GPU-hours |
| Energy | 0.104 (measured) kWh |
| Carbon | 0.0067 (measured) kgCO₂eq |
| Water | 0.187–0.416 (estimated-from-default-wue) L |
| Grid region | ca-on |
Energy and carbon are measured with CodeCarbon; water is estimated from a default water-usage-effectiveness range. Carbon uses the local grid's intensity (Ontario, ~0.03 kgCO₂eq/kWh).
Reproduce
REPO=DIA-MVP/mnist-ddpm-a100 python scripts/train_ddpm_mnist.py
Links
- Footprint table (dataset): DIA-MVP/dia-state-lab-2026
- Project / paper: ai-impact-accounting
- Lab workflow: see
LAB.mdin the repo
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