.github/workflows/format.yml - master - githubmirror/yolov5

.github/workflows/format.yml
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Glenn Jocher
Update redirected links (#13794)
12 июн 2026, 23:41
12 июн 2026, 23:4133a3afb
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# Ultralytics Actions https://github.com/ultralytics/actions
# This workflow formats code and documentation in PRs to Ultralytics standards

name: Ultralytics Actions

on:
  issues:
    types: [opened, edited]
  discussion:
    types: [created]
  pull_request:
    types: [opened, closed, synchronize, review_requested]

permissions:
  contents: write # Modify code in PRs
  pull-requests: write # Add comments and labels to PRs
  issues: write # Add comments and labels to issues

jobs:
  actions:
    runs-on: ubuntu-latest
    steps:
      - name: Run Ultralytics Actions
        uses: ultralytics/actions@main
        with:
          token: ${{ secrets._GITHUB_TOKEN || secrets.GITHUB_TOKEN }} # Auto-generated token
          labels: true # Auto-label issues/PRs using AI
          python: true # Format Python with Ruff and docformatter
          prettier: true # Format YAML, JSON, Markdown, CSS
          swift: false # Format Swift (requires macos-latest)
          spelling: true # Check spelling with codespell
          links: false # Check broken links with Lychee
          summary: true # Generate AI-powered PR summaries
          openai_api_key: ${{ secrets.OPENAI_API_KEY }} # Powers PR summaries, labels and comments
          brave_api_key: ${{ secrets.BRAVE_API_KEY }} # Used for broken link resolution
          first_issue_response: |
            👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ [Tutorials](https://docs.ultralytics.com/yolov5) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) all the way to advanced concepts like [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution).
            If this is a 🐛 Bug Report, please provide a **minimum reproducible example** to help us debug it.
            If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our [Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips).
            ## Requirements
            [**Python>=3.8.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). To get started:
            ```bash
            git clone https://github.com/ultralytics/yolov5  # clone
            cd yolov5
            pip install uv  # install uv
            uv pip install -r requirements.txt  # install
            ```
            ## Environments
            YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
            - **Notebooks** with free GPU: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
            - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial)
            - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial)
            - **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
            ## Status
            <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
            If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.