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🚀 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:
```
┌─────────────┐ ┌──────────┐ ┌──────────────┐
│ HuggingFace │ ───> │ Chutes │ ───> │ Turbovision │
│ Hub │ │ .ai │ │ Validator │
└─────────────┘ └──────────┘ └──────────────┘
```
## 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 template file** `scorevision/chute_template/turbovision_chute.py.j2` as a python file called `my_chute.py` and fill in the missing variables:
```python
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):
```bash
chutes build my_chute:chute --local --public
```
3. **Run the name of the docker image just built** (i.e. `CHUTE_NAME`) and enter it:
```bash
docker run -p 8000:8000 -e CHUTES_EXECUTION_CONTEXT=REMOTE -it <image-name> /bin/bash
```
4. **Run the file from within the container**:
```bash
chutes run my_chute:chute --dev --debug
```
5. **In another terminal, test the local endpoints** to ensure there are no bugs:
```bash
# Health check
curl -X POST http://localhost:8000/health -d '{}'
# Prediction test
curl -X POST http://localhost:8000/predict -d '{"url": "https://scoredata.me/2025_03_14/35ae7a/h1_0f2ca0.mp4","meta": {}}'
```
## Live Testing
If you have any chute with the same name (i.e. from a previous deployment), ensure you delete that first (or you will get an error when trying to build).
1. **List existing chutes**:
```bash
chutes chutes list
```
Take note of the chute id that you wish to delete (if any):
```bash
chutes chutes delete <chute-id>
```
2. **You should also delete its associated image**:
```bash
chutes images list
```
Take note of the chute image id:
```bash
chutes images delete <chute-image-id>
```
3. **Use Turbovision's CLI to build, deploy and commit on-chain**:
```bash
sv -vv push
```
> **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`.
4. **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 🔥!
```bash
chutes warmup <chute-id>
```
5. **Test the chute's endpoints**:
```bash
# Health check
curl -X POST https://<YOUR-CHUTE-SLUG>.chutes.ai/health -d '{}' -H "Authorization: Bearer $CHUTES_API_KEY"
# Prediction
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"
```
6. **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:
```bash
sv -vv run-once
```
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