Separate GPU CI job in actions (#4584)

.github/workflows/ci.yaml

@@ -24,6 +24,10 @@ on:
         description: 'Run Benchmarks'
         default: false
         type: boolean
+      gpu:
+        description: 'Run GPU'
+        default: false
+        type: boolean
 
 jobs:
   HUB:
@@ -47,6 +51,7 @@ jobs:
           pip install -e . --extra-index-url https://download.pytorch.org/whl/cpu
       - name: Check environment
         run: |
+          yolo checks
           echo "RUNNER_OS is ${{ runner.os }}"
           echo "GITHUB_EVENT_NAME is ${{ github.event_name }}"
           echo "GITHUB_WORKFLOW is ${{ github.workflow }}"
@@ -110,6 +115,7 @@ jobs:
           yolo export format=tflite imgsz=32 || true
       - name: Check environment
         run: |
+          yolo checks
           echo "RUNNER_OS is ${{ runner.os }}"
           echo "GITHUB_EVENT_NAME is ${{ github.event_name }}"
           echo "GITHUB_WORKFLOW is ${{ github.workflow }}"
@@ -159,7 +165,6 @@ jobs:
           - os: ubuntu-latest
             python-version: '3.8'  # torch 1.8.0 requires python >=3.6, <=3.8
             torch: '1.8.0'  # min torch version CI https://pypi.org/project/torchvision/
-          - os: gpu-latest  # do not pass python-version
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
@@ -189,12 +194,7 @@ jobs:
           pip list
       - name: Pytest tests
         shell: bash  # for Windows compatibility
-        run: |
+        run: pytest --cov=ultralytics/ --cov-report xml tests/
-          if [ "${{ matrix.os }}" == "gpu-latest" ]; then
-              pytest --cov=ultralytics/ --cov-report xml tests/test_cuda.py
-          else
-              pytest --cov=ultralytics/ --cov-report xml tests/
-          fi
       - name: Upload Coverage Reports to CodeCov
         if: github.repository == 'ultralytics/ultralytics' # && matrix.os == 'ubuntu-latest' && matrix.python-version == '3.11'
         uses: codecov/codecov-action@v3
@@ -203,13 +203,42 @@ jobs:
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 
+  GPU:
+    if: github.repository == 'ultralytics/ultralytics' && (github.event_name != 'workflow_dispatch' || github.event.inputs.gpu == 'true')
+    timeout-minutes: 60
+    runs-on: gpu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Install requirements
+        run: pip install -e .
+      - name: Check environment
+        run: |
+          yolo checks
+          echo "RUNNER_OS is ${{ runner.os }}"
+          echo "GITHUB_EVENT_NAME is ${{ github.event_name }}"
+          echo "GITHUB_WORKFLOW is ${{ github.workflow }}"
+          echo "GITHUB_ACTOR is ${{ github.actor }}"
+          echo "GITHUB_REPOSITORY is ${{ github.repository }}"
+          echo "GITHUB_REPOSITORY_OWNER is ${{ github.repository_owner }}"
+          python --version
+          pip --version
+          pip list
+      - name: Pytest tests
+        run: pytest --cov=ultralytics/ --cov-report xml tests/test_cuda.py
+      - name: Upload Coverage Reports to CodeCov
+        uses: codecov/codecov-action@v3
+        with:
+          flags: GPU
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+
   Summary:
     runs-on: ubuntu-latest
-    needs: [HUB, Benchmarks, Tests] # Add job names that you want to check for failure
+    needs: [HUB, Benchmarks, Tests, GPU] # Add job names that you want to check for failure
     if: always() # This ensures the job runs even if previous jobs fail
     steps:
       - name: Check for failure and notify
-        if: (needs.HUB.result == 'failure' || needs.Benchmarks.result == 'failure' || needs.Tests.result == 'failure') && github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event_name == 'push')
+        if: (needs.HUB.result == 'failure' || needs.Benchmarks.result == 'failure' || needs.Tests.result == 'failure' || needs.GPU.result == 'failure') && github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event_name == 'push')
         uses: slackapi/slack-github-action@v1.24.0
         with:
           payload: |

docker/Dockerfile

@@ -29,7 +29,7 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u
 
 # Install pip packages
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -e ".[export]" thop albumentations comet pycocotools
+RUN pip install --no-cache -e ".[export]" thop albumentations comet pycocotools pytest-cov
 
 # Run exports to AutoInstall packages
 RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32

docs/datasets/segment/index.md

@@ -133,13 +133,15 @@ To auto-annotate your dataset using the Ultralytics framework, you can use the `
         auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
         ```
 
-| Argument   | Type                | Description                                                                                             | Default      |
+Certainly, here is the table updated with code snippets:
-|------------|---------------------|---------------------------------------------------------------------------------------------------------|--------------|
+
-| data       | str                 | Path to a folder containing images to be annotated.                                                     |              |
+| Argument     | Type                    | Description                                                                                                 | Default        |
-| det_model  | str, optional       | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'.                                             | 'yolov8x.pt' |
+|--------------|-------------------------|-------------------------------------------------------------------------------------------------------------|----------------|
-| sam_model  | str, optional       | Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'.                                             | 'sam_b.pt'   |
+| `data`       | `str`                   | Path to a folder containing images to be annotated.                                                         | `None`         |
-| device     | str, optional       | Device to run the models on. Defaults to an empty string (CPU or GPU, if available).                    |              |
+| `det_model`  | `str, optional`         | Pre-trained YOLO detection model. Defaults to `'yolov8x.pt'`.                                               | `'yolov8x.pt'` |
-| output_dir | str, None, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | None         |
+| `sam_model`  | `str, optional`         | Pre-trained SAM segmentation model. Defaults to `'sam_b.pt'`.                                               | `'sam_b.pt'`   |
+| `device`     | `str, optional`         | Device to run the models on. Defaults to an empty string (CPU or GPU, if available).                        | `''`           |
+| `output_dir` | `str or None, optional` | Directory to save the annotated results. Defaults to a `'labels'` folder in the same directory as `'data'`. | `None`         |
 
 The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and [SAM segmentation models](https://docs.ultralytics.com/models/sam), the device to run the models on, and the output directory for saving the annotated results.
 

tests/test_python.py

@@ -13,6 +13,7 @@ from PIL import Image
 from torchvision.transforms import ToTensor
 
 from ultralytics import RTDETR, YOLO
+from ultralytics.cfg import TASK2DATA
 from ultralytics.data.build import load_inference_source
 from ultralytics.utils import ASSETS, DEFAULT_CFG, LINUX, ONLINE, ROOT, SETTINGS, WINDOWS
 from ultralytics.utils.downloads import download
@@ -275,11 +276,13 @@ def test_data_utils():
     # from ultralytics.utils.files import WorkingDirectory
     # with WorkingDirectory(ROOT.parent / 'tests'):
 
-    download('https://github.com/ultralytics/hub/raw/main/example_datasets/coco8.zip', unzip=False)
+    for task in 'detect', 'segment', 'pose':
-    shutil.move('coco8.zip', TMP)
+        file = Path(TASK2DATA[task]).with_suffix('.zip')  # i.e. coco8.zip
-    stats = HUBDatasetStats(TMP / 'coco8.zip', task='detect')
+        download(f'https://github.com/ultralytics/hub/raw/main/example_datasets/{file}', unzip=False)
-    stats.get_json(save=True)
+        shutil.move(str(file), TMP)  # Python 3.8 requires string input to shutil.move()
-    stats.process_images()
+        stats = HUBDatasetStats(TMP / file, task=task)
+        stats.get_json(save=True)
+        stats.process_images()
 
     autosplit(TMP / 'coco8')
     zip_directory(TMP / 'coco8/images/val')  # zip