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ultralytics 8.0.198 MLflow fix, tests and Docs page (#5357)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-10-13 20:41:05 +02:00 • committed by GitHub
parent 12e3eef844
commit 5b3c4cfc0e
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11 changed files with 228 additions and 65 deletions
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23
tests/test_cuda.py
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@ -1,16 +1,13 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
import pytest
import torch
from ultralytics import YOLO, download
from ultralytics.utils import ASSETS, DATASETS_DIR, WEIGHTS_DIR
from ultralytics.utils.checks import cuda_device_count, cuda_is_available
from ultralytics.utils import ASSETS, DATASETS_DIR, WEIGHTS_DIR, checks
CUDA_IS_AVAILABLE = cuda_is_available()
CUDA_DEVICE_COUNT = cuda_device_count()
CUDA_IS_AVAILABLE = checks.cuda_is_available()
CUDA_DEVICE_COUNT = checks.cuda_device_count()
MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt' # test spaces in path
DATA = 'coco8.yaml'
@ -107,20 +104,6 @@ def test_predict_sam():
predictor.reset_image()
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
def test_model_ray_tune():
"""Tune YOLO model with Ray optimization library."""
with contextlib.suppress(RuntimeError): # RuntimeError may be caused by out-of-memory
YOLO('yolov8n-cls.yaml').tune(use_ray=True,
data='imagenet10',
grace_period=1,
iterations=1,
imgsz=32,
epochs=1,
plots=False,
device='cpu')
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
def test_model_tune():
"""Tune YOLO model for performance."""

26
tests/test_integrations.py Normal file
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@ -0,0 +1,26 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import pytest
from ultralytics import YOLO
from ultralytics.utils import SETTINGS, checks
@pytest.mark.skipif(not checks.check_requirements('ray', install=False), reason='RayTune not installed')
def test_model_ray_tune():
"""Tune YOLO model with Ray optimization library."""
YOLO('yolov8n-cls.yaml').tune(use_ray=True,
data='imagenet10',
grace_period=1,
iterations=1,
imgsz=32,
epochs=1,
plots=False,
device='cpu')
@pytest.mark.skipif(not checks.check_requirements('mlflow', install=False), reason='MLflow not installed')
def test_mlflow():
"""Test training with MLflow tracking enabled."""
SETTINGS['mlflow'] = True
YOLO('yolov8n-cls.yaml').train(data='imagenet10', imgsz=32, epochs=3, plots=False, device='cpu')