ultralytics 8.0.163 add new gpu-latest runner to CI actions (#4565)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Maia Numerosky <17316848+maianumerosky@users.noreply.github.com>

tests/test_cuda.py

@@ -0,0 +1,73 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+import subprocess
+from pathlib import Path
+
+import pytest
+import torch
+
+from ultralytics import YOLO
+from ultralytics.utils import ASSETS, SETTINGS
+
+CUDA_IS_AVAILABLE = torch.cuda.is_available()
+CUDA_DEVICE_COUNT = torch.cuda.device_count()
+
+WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
+MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt'  # test spaces in path
+DATA = 'coco8.yaml'
+
+
+def test_checks():
+    from ultralytics.utils.checks import cuda_device_count, cuda_is_available
+
+    assert cuda_device_count() == CUDA_DEVICE_COUNT
+    assert cuda_is_available() == CUDA_IS_AVAILABLE
+
+
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
+def test_train():
+    YOLO(MODEL).train(data=DATA, imgsz=64, epochs=1, batch=-1, device=0)  # also test AutoBatch, requires imgsz>=64
+
+
+@pytest.mark.skipif(CUDA_DEVICE_COUNT < 2, reason=f'DDP is not available, {CUDA_DEVICE_COUNT} device(s) found')
+def test_train_ddp():
+    YOLO(MODEL).train(data=DATA, imgsz=64, epochs=1, device=[0, 1])  # requires imgsz>=64
+
+
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
+def test_utils_benchmarks():
+    from ultralytics.utils.benchmarks import ProfileModels
+
+    YOLO(MODEL).export(format='engine', imgsz=32, dynamic=True, batch=1)  # pre-export engine model, auto-device
+    ProfileModels([MODEL], imgsz=32, half=False, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
+
+
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
+def test_predict_sam():
+    from ultralytics import SAM
+
+    # Load a model
+    model = SAM(WEIGHTS_DIR / 'sam_b.pt')
+
+    # Display model information (optional)
+    model.info()
+
+    # Run inference
+    model(ASSETS / 'bus.jpg', device=0)
+
+    # Run inference with bboxes prompt
+    model(ASSETS / 'zidane.jpg', bboxes=[439, 437, 524, 709], device=0)
+
+    # Run inference with points prompt
+    model(ASSETS / 'zidane.jpg', points=[900, 370], labels=[1], device=0)
+
+
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
+def test_model_tune():
+    subprocess.run('pip install ray[tune]'.split(), check=True)
+    YOLO('yolov8n-cls.yaml').tune(data='imagenet10',
+                                  grace_period=1,
+                                  max_samples=1,
+                                  imgsz=32,
+                                  epochs=1,
+                                  plots=False,
+                                  device='cpu')