gloriforge/ScoreVision

🚀 Example Chute for Turbovision 🪂

This repository demonstrates how to deploy a Chute via the Turbovision CLI, hosted on Hugging Face Hub. It serves as a minimal example showcasing the required structure and workflow for integrating machine learning models, preprocessing, and orchestration into a reproducible Chute environment.

Repository Structure

The following two files must be present (in their current locations) for a successful deployment — their content can be modified as needed:

File	Purpose
miner.py	Defines the ML model type(s), orchestration, and all pre/postprocessing logic.
config.yml	Specifies machine configuration (e.g., GPU type, memory, environment variables).

Other files — e.g., model weights, utility scripts, or dependencies — are optional and can be included as needed for your model. Note: Any required assets must be defined or contained within this repo, which is fully open-source, since all network-related operations (downloading challenge data, weights, etc.) are disabled inside the Chute

Overview

Below is a high-level diagram showing the interaction between Huggingface, Chutes and Turbovision:

Local Testing

After editing the config.yml and miner.py and saving it into your Huggingface Repo, you will want to test it works locally.

1. Copy the file scorevision/chute_tmeplate/turbovision_chute.py.j2 as a python file called my_chute.py and fill in the missing variables:

HF_REPO_NAME = "{{ huggingface_repository_name }}"
HF_REPO_REVISION = "{{ huggingface_repository_revision }}"
CHUTES_USERNAME = "{{ chute_username }}"
CHUTE_NAME = "{{ chute_name }}"

2. Run the following command to build the chute locally (Caution: there are known issues with the docker location when running this on a mac)

chutes build my_chute:chute --local --public

3. Run the name of the docker image just built (i.e. CHUTE_NAME) and enter it

docker run -p 8000:8000 -e CHUTES_EXECUTION_CONTEXT=REMOTE -it <image-name> /bin/bash

4. Run the file from within the container

chutes run my_chute:chute --dev --debug

5. In another terminal, test the local endpoints to ensure there are no bugs

curl -X POST http://localhost:8000/health -d '{}'
curl -X POST http://localhost:8000/predict -d '{"url": "https://scoredata.me/2025_03_14/35ae7a/h1_0f2ca0.mp4","meta": {}}'

Live Testing

1. If you have any chute with the same name (ie from a previous deployment), ensure you delete that first (or you will get an error when trying to build).

chutes chutes list

Take note of the chute id that you wish to delete (if any)

chutes chutes delete <chute-id>

You should also delete its associated image

chutes images list

Take note of the chute image id

chutes images delete <chute-image-id>

2. Use Turbovision's CLI to build, deploy and commit on-chain (Note: you can skip the on-chain commit using --no-commit. You can also specify a past huggingface revision to point to using --revision and/or the local files you want to upload to your huggingface repo using --model-path)

sv -vv push

3. When completed, warm up the chute (if its cold 🧊). (You can confirm its status using chutes chutes list or chutes chutes get <chute-id> if you already know its id). Note: Warming up can sometimes take a while but if the chute runs without errors (should be if you've tested locally first) and there are sufficient nodes (i.e. machines) available matching the config.yml you specified, the chute should become hot 🔥!

chutes warmup <chute-id>

4. Test the chute's endpoints

curl -X POST https://<YOUR-CHUTE-SLUG>.chutes.ai/health -d '{}' -H "Authorization: Bearer $CHUTES_API_KEY"
curl -X POST https://<YOUR-CHUTE-SLUG>.chutes.ai/predict -d '{"url": "https://scoredata.me/2025_03_14/35ae7a/h1_0f2ca0.mp4","meta": {}}' -H "Authorization: Bearer $CHUTES_API_KEY"

5. Test what your chute would get on a validator (this also applies any validation/integrity checks which may fail if you did not use the Turbovision CLI above to deploy the chute)

sv -vv run-once