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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
parent e795277391
commit fe27db2f6e
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139 changed files with 6870 additions and 5125 deletions
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51
ultralytics/utils/callbacks/mlflow.py
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@ -26,15 +26,15 @@ from ultralytics.utils import LOGGER, RUNS_DIR, SETTINGS, TESTS_RUNNING, colorst
try:
import os
assert not TESTS_RUNNING or 'test_mlflow' in os.environ.get('PYTEST_CURRENT_TEST', '') # do not log pytest
assert SETTINGS['mlflow'] is True # verify integration is enabled
assert not TESTS_RUNNING or "test_mlflow" in os.environ.get("PYTEST_CURRENT_TEST", "") # do not log pytest
assert SETTINGS["mlflow"] is True # verify integration is enabled
import mlflow
assert hasattr(mlflow, '__version__') # verify package is not directory
assert hasattr(mlflow, "__version__") # verify package is not directory
from pathlib import Path
PREFIX = colorstr('MLflow: ')
SANITIZE = lambda x: {k.replace('(', '').replace(')', ''): float(v) for k, v in x.items()}
PREFIX = colorstr("MLflow: ")
SANITIZE = lambda x: {k.replace("(", "").replace(")", ""): float(v) for k, v in x.items()}
except (ImportError, AssertionError):
mlflow = None
@ -61,33 +61,33 @@ def on_pretrain_routine_end(trainer):
"""
global mlflow
uri = os.environ.get('MLFLOW_TRACKING_URI') or str(RUNS_DIR / 'mlflow')
LOGGER.debug(f'{PREFIX} tracking uri: {uri}')
uri = os.environ.get("MLFLOW_TRACKING_URI") or str(RUNS_DIR / "mlflow")
LOGGER.debug(f"{PREFIX} tracking uri: {uri}")
mlflow.set_tracking_uri(uri)
# Set experiment and run names
experiment_name = os.environ.get('MLFLOW_EXPERIMENT_NAME') or trainer.args.project or '/Shared/YOLOv8'
run_name = os.environ.get('MLFLOW_RUN') or trainer.args.name
experiment_name = os.environ.get("MLFLOW_EXPERIMENT_NAME") or trainer.args.project or "/Shared/YOLOv8"
run_name = os.environ.get("MLFLOW_RUN") or trainer.args.name
mlflow.set_experiment(experiment_name)
mlflow.autolog()
try:
active_run = mlflow.active_run() or mlflow.start_run(run_name=run_name)
LOGGER.info(f'{PREFIX}logging run_id({active_run.info.run_id}) to {uri}')
LOGGER.info(f"{PREFIX}logging run_id({active_run.info.run_id}) to {uri}")
if Path(uri).is_dir():
LOGGER.info(f"{PREFIX}view at http://127.0.0.1:5000 with 'mlflow server --backend-store-uri {uri}'")
LOGGER.info(f"{PREFIX}disable with 'yolo settings mlflow=False'")
mlflow.log_params(dict(trainer.args))
except Exception as e:
LOGGER.warning(f'{PREFIX}WARNING ⚠️ Failed to initialize: {e}\n'
f'{PREFIX}WARNING ⚠️ Not tracking this run')
LOGGER.warning(f"{PREFIX}WARNING ⚠️ Failed to initialize: {e}\n" f"{PREFIX}WARNING ⚠️ Not tracking this run")
def on_train_epoch_end(trainer):
"""Log training metrics at the end of each train epoch to MLflow."""
if mlflow:
mlflow.log_metrics(metrics=SANITIZE(trainer.label_loss_items(trainer.tloss, prefix='train')),
step=trainer.epoch)
mlflow.log_metrics(
metrics=SANITIZE(trainer.label_loss_items(trainer.tloss, prefix="train")), step=trainer.epoch
)
mlflow.log_metrics(metrics=SANITIZE(trainer.lr), step=trainer.epoch)
@ -101,16 +101,23 @@ def on_train_end(trainer):
"""Log model artifacts at the end of the training."""
if mlflow:
mlflow.log_artifact(str(trainer.best.parent)) # log save_dir/weights directory with best.pt and last.pt
for f in trainer.save_dir.glob('*'): # log all other files in save_dir
if f.suffix in {'.png', '.jpg', '.csv', '.pt', '.yaml'}:
for f in trainer.save_dir.glob("*"): # log all other files in save_dir
if f.suffix in {".png", ".jpg", ".csv", ".pt", ".yaml"}:
mlflow.log_artifact(str(f))
mlflow.end_run()
LOGGER.info(f'{PREFIX}results logged to {mlflow.get_tracking_uri()}\n'
f"{PREFIX}disable with 'yolo settings mlflow=False'")
LOGGER.info(
f"{PREFIX}results logged to {mlflow.get_tracking_uri()}\n"
f"{PREFIX}disable with 'yolo settings mlflow=False'"
)
callbacks = {
'on_pretrain_routine_end': on_pretrain_routine_end,
'on_fit_epoch_end': on_fit_epoch_end,
'on_train_end': on_train_end} if mlflow else {}
callbacks = (
{
"on_pretrain_routine_end": on_pretrain_routine_end,
"on_fit_epoch_end": on_fit_epoch_end,
"on_train_end": on_train_end,
}
if mlflow
else {}
)