Add new @Retry() decorator (#7854)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher
GitHub
parent
5f00fbd227
commit
1435f0e9de
9 changed files with 372 additions and 269 deletions
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@ -9,67 +9,67 @@ from ultralytics import YOLO, download
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from ultralytics.utils import ASSETS, DATASETS_DIR, ROOT, SETTINGS, WEIGHTS_DIR
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from ultralytics.utils.checks import check_requirements
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MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt' # test spaces in path
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CFG = 'yolov8n.yaml'
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SOURCE = ASSETS / 'bus.jpg'
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TMP = (ROOT / '../tests/tmp').resolve() # temp directory for test files
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MODEL = WEIGHTS_DIR / "path with spaces" / "yolov8n.pt" # test spaces in path
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CFG = "yolov8n.yaml"
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SOURCE = ASSETS / "bus.jpg"
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TMP = (ROOT / "../tests/tmp").resolve() # temp directory for test files
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@pytest.mark.skipif(not check_requirements('ray', install=False), reason='ray[tune] not installed')
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@pytest.mark.skipif(not check_requirements("ray", install=False), reason="ray[tune] not installed")
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def test_model_ray_tune():
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"""Tune YOLO model with Ray optimization library."""
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YOLO('yolov8n-cls.yaml').tune(use_ray=True,
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data='imagenet10',
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grace_period=1,
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iterations=1,
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imgsz=32,
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epochs=1,
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plots=False,
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device='cpu')
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YOLO("yolov8n-cls.yaml").tune(
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use_ray=True, data="imagenet10", grace_period=1, iterations=1, imgsz=32, epochs=1, plots=False, device="cpu"
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)
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@pytest.mark.skipif(not check_requirements('mlflow', install=False), reason='mlflow not installed')
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@pytest.mark.skipif(not check_requirements("mlflow", install=False), reason="mlflow not installed")
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def test_mlflow():
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"""Test training with MLflow tracking enabled."""
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SETTINGS['mlflow'] = True
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YOLO('yolov8n-cls.yaml').train(data='imagenet10', imgsz=32, epochs=3, plots=False, device='cpu')
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SETTINGS["mlflow"] = True
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YOLO("yolov8n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=3, plots=False, device="cpu")
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@pytest.mark.skipif(not check_requirements('tritonclient', install=False), reason='tritonclient[all] not installed')
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@pytest.mark.skipif(not check_requirements("tritonclient", install=False), reason="tritonclient[all] not installed")
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def test_triton():
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"""Test NVIDIA Triton Server functionalities."""
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check_requirements('tritonclient[all]')
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check_requirements("tritonclient[all]")
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import subprocess
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import time
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from tritonclient.http import InferenceServerClient # noqa
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# Create variables
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model_name = 'yolo'
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triton_repo_path = TMP / 'triton_repo'
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model_name = "yolo"
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triton_repo_path = TMP / "triton_repo"
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triton_model_path = triton_repo_path / model_name
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# Export model to ONNX
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f = YOLO(MODEL).export(format='onnx', dynamic=True)
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f = YOLO(MODEL).export(format="onnx", dynamic=True)
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# Prepare Triton repo
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(triton_model_path / '1').mkdir(parents=True, exist_ok=True)
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Path(f).rename(triton_model_path / '1' / 'model.onnx')
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(triton_model_path / 'config.pbtxt').touch()
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(triton_model_path / "1").mkdir(parents=True, exist_ok=True)
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Path(f).rename(triton_model_path / "1" / "model.onnx")
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(triton_model_path / "config.pbtxt").touch()
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# Define image https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver
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tag = 'nvcr.io/nvidia/tritonserver:23.09-py3' # 6.4 GB
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tag = "nvcr.io/nvidia/tritonserver:23.09-py3" # 6.4 GB
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# Pull the image
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subprocess.call(f'docker pull {tag}', shell=True)
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subprocess.call(f"docker pull {tag}", shell=True)
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# Run the Triton server and capture the container ID
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container_id = subprocess.check_output(
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f'docker run -d --rm -v {triton_repo_path}:/models -p 8000:8000 {tag} tritonserver --model-repository=/models',
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shell=True).decode('utf-8').strip()
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container_id = (
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subprocess.check_output(
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f"docker run -d --rm -v {triton_repo_path}:/models -p 8000:8000 {tag} tritonserver --model-repository=/models",
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shell=True,
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)
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.decode("utf-8")
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.strip()
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)
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# Wait for the Triton server to start
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triton_client = InferenceServerClient(url='localhost:8000', verbose=False, ssl=False)
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triton_client = InferenceServerClient(url="localhost:8000", verbose=False, ssl=False)
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# Wait until model is ready
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for _ in range(10):
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@ -79,13 +79,13 @@ def test_triton():
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time.sleep(1)
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# Check Triton inference
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YOLO(f'http://localhost:8000/{model_name}', 'detect')(SOURCE) # exported model inference
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YOLO(f"http://localhost:8000/{model_name}", "detect")(SOURCE) # exported model inference
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# Kill and remove the container at the end of the test
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subprocess.call(f'docker kill {container_id}', shell=True)
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subprocess.call(f"docker kill {container_id}", shell=True)
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@pytest.mark.skipif(not check_requirements('pycocotools', install=False), reason='pycocotools not installed')
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@pytest.mark.skipif(not check_requirements("pycocotools", install=False), reason="pycocotools not installed")
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def test_pycocotools():
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"""Validate model predictions using pycocotools."""
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from ultralytics.models.yolo.detect import DetectionValidator
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@ -93,25 +93,25 @@ def test_pycocotools():
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from ultralytics.models.yolo.segment import SegmentationValidator
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# Download annotations after each dataset downloads first
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url = 'https://github.com/ultralytics/assets/releases/download/v8.1.0/'
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url = "https://github.com/ultralytics/assets/releases/download/v8.1.0/"
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args = {'model': 'yolov8n.pt', 'data': 'coco8.yaml', 'save_json': True, 'imgsz': 64}
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args = {"model": "yolov8n.pt", "data": "coco8.yaml", "save_json": True, "imgsz": 64}
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validator = DetectionValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8/annotations')
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download(f"{url}instances_val2017.json", dir=DATASETS_DIR / "coco8/annotations")
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_ = validator.eval_json(validator.stats)
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args = {'model': 'yolov8n-seg.pt', 'data': 'coco8-seg.yaml', 'save_json': True, 'imgsz': 64}
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args = {"model": "yolov8n-seg.pt", "data": "coco8-seg.yaml", "save_json": True, "imgsz": 64}
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validator = SegmentationValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8-seg/annotations')
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download(f"{url}instances_val2017.json", dir=DATASETS_DIR / "coco8-seg/annotations")
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_ = validator.eval_json(validator.stats)
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args = {'model': 'yolov8n-pose.pt', 'data': 'coco8-pose.yaml', 'save_json': True, 'imgsz': 64}
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args = {"model": "yolov8n-pose.pt", "data": "coco8-pose.yaml", "save_json": True, "imgsz": 64}
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validator = PoseValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}person_keypoints_val2017.json', dir=DATASETS_DIR / 'coco8-pose/annotations')
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download(f"{url}person_keypoints_val2017.json", dir=DATASETS_DIR / "coco8-pose/annotations")
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_ = validator.eval_json(validator.stats)
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