mnist-ddpm-a100

DIA-MVP/mnist-ddpm-a100

mnist-ddpm-a100 is a machine learning model from DIA-MVP. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.

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.

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Updated Jun 29, 2026

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1.8 MB

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At a glance

License: apache-2.0
Access: Public
Created: Jun 29, 2026
Updated: Jun 29, 2026
SHA: 3c5ec82d

Model card

From the Hugging Face model README

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