Add a second end-to-end example model beyond MNIST
Maintainers usually reply within 1 day
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 55/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Active
- Tech stack
- machine-learning, python
- Domain
- machine-learning
Research direction
Read the existing cache_model_0/ structure and the guide in Documentation/public/what-can-be-trained.md; related issue #240 covers training beyond MNIST. Choose a small public dataset and model, then add a manifest.json and services/ example following that structure. Done means it completes a full training round on a local devnet and includes a short run guide.
Written by the indexing model from the issue text.
Description
Today the only model tested end-to-end on the network is the MNIST reference (cache_model_0/). We'd like a second working example, so model trainers can see the protocol isn't limited to one demo.
Suggested scope: a small model on a public dataset, for example a CNN on Fashion-MNIST or CIFAR-10, following the same structure as cache_model_0/ (manifest.json plus services/).
Done when:
- it completes a full training round on a local devnet
- a short doc explains how to run it
Good fit for: machine-learning contributors. No Solidity needed.
Related: #240 (guide to training models beyond MNIST) and Documentation/public/what-can-be-trained.md.
- Dominant language
- Python
- Stars
- 4
- Forks
- 7
- Avg merge
- 3d 5h
- Merged PRs (30d)
- 34
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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