# ZeroGPU migration plan

This is a deployment plan, not a claim that the current Docker Space runs on
ZeroGPU.

## Current constraint

`Simam3D` is a Docker Space. Hugging Face ZeroGPU currently targets Gradio SDK
Spaces, so changing the hardware selector on this repository is not enough.
The current Docker Space remains the reproducible target for DA3 experiments
when metered GPU capacity is available.

## Separate variant steps

1. Create a Gradio-SDK Space variant from the tested `app.py` and
   `simam3d_core.py` modules.
2. Change the Space metadata from `sdk: docker` to `sdk: gradio` and remove
   Docker-only installation assumptions.
3. Use the platform-managed `gradio`, `spaces`, and `huggingface_hub` packages;
   do not pin those packages in the ZeroGPU requirements.
4. Keep model inference behind the optional `spaces.GPU` boundary and set a
   bounded duration for depth requests.
5. Validate the ZeroGPU-supported Python/Torch matrix before selecting the
   runtime; the current Docker requirements are intentionally independent.
6. Run the same dependency-free geometry and manifest tests (currently 34) and compare manifests
   between Docker and ZeroGPU runs.

For the current Docker image, DA3 is intentionally opt-in: build with
`SIMAM3D_INSTALL_DA3=1` only after selecting a compatible upstream revision.
The default build remains V2-first and does not require the DA3 Git package.

## Acceptance checks

- A V2 single-image request returns depth, GLB, PLY, fused PLY, Gaussian PLY,
  metadata, uncertainty map, turntable GIF, evidence card, reveal card, and
  manifest.
- The manifest records the ZeroGPU runtime, exact software versions, input
  hashes, pose source, confidence source, and fusion settings.
- DA3 remains an explicit opt-in experiment and its failure cannot prevent the
  V2 baseline from returning an artifact.

Reference: https://huggingface.co/docs/hub/spaces-zerogpu