Upload folder using huggingface_hub

.gitattributes

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20251029-detection.pt

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README.md

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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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chute_config.yml

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+Image:
+  from_base: parachutes/python:3.12
+  run_command:
+    - pip install --upgrade setuptools wheel
+    - pip install "ultralytics==8.3.222" "opencv-python-headless" "numpy" "pydantic"
+    - pip install "torch==2.7.1" "torchvision==0.22.1" --index-url https://download.pytorch.org/whl/cu128
+    - pip install scikit-learn
+    - pip install onnxruntime-gpu
+    - pip install psutil
+    - pip install "team-classifier-osnet==0.1.3"
+  set_workdir: /app
+
+NodeSelector:
+  gpu_count: 1
+  min_vram_gb_per_gpu: 24
+
+Chute:
+  timeout_seconds: 900
+  concurrency: 48
+  max_instances: 5
+  scaling_threshold: 0.5
+  shutdown_after_seconds: 96000

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hrnetv2_w48.yaml

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+MODEL:
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+  NUM_JOINTS: 58
+  PRETRAIN: ''
+  EXTRA:
+    FINAL_CONV_KERNEL: 1
+    STAGE1:
+      NUM_MODULES: 1
+      NUM_BRANCHES: 1
+      BLOCK: BOTTLENECK
+      NUM_BLOCKS: [4]
+      NUM_CHANNELS: [64]
+      FUSE_METHOD: SUM
+    STAGE2:
+      NUM_MODULES: 1
+      NUM_BRANCHES: 2
+      BLOCK: BASIC
+      NUM_BLOCKS: [4, 4]
+      NUM_CHANNELS: [48, 96]
+      FUSE_METHOD: SUM
+    STAGE3:
+      NUM_MODULES: 4
+      NUM_BRANCHES: 3
+      BLOCK: BASIC
+      NUM_BLOCKS: [4, 4, 4]
+      NUM_CHANNELS: [48, 96, 192]
+      FUSE_METHOD: SUM
+    STAGE4:
+      NUM_MODULES: 3
+      NUM_BRANCHES: 4
+      BLOCK: BASIC
+      NUM_BLOCKS: [4, 4, 4, 4]
+      NUM_CHANNELS: [48, 96, 192, 384]
+      FUSE_METHOD: SUM
+

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