Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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from ultralytics.utils import SETTINGS
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try:
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assert SETTINGS["raytune"] is True # verify integration is enabled
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import ray
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from ray import tune
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from ray.air import session
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except (ImportError, AssertionError):
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tune = None
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def on_fit_epoch_end(trainer):
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"""Report training metrics to Ray Tune at epoch end when a Ray session is active.
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Captures metrics from the trainer object and sends them to Ray Tune with the current epoch number, enabling
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hyperparameter tuning optimization. Only executes when within an active Ray Tune session.
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Args:
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trainer (ultralytics.engine.trainer.BaseTrainer): The Ultralytics trainer object containing metrics and epochs.
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Examples:
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>>> # Called automatically by the Ultralytics training loop
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>>> on_fit_epoch_end(trainer)
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References:
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Ray Tune docs: https://docs.ray.io/en/latest/tune/index.html
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"""
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if ray.train._internal.session.get_session(): # check if Ray Tune session is active
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metrics = trainer.metrics
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session.report({**metrics, **{"epoch": trainer.epoch + 1}})
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callbacks = (
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{
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"on_fit_epoch_end": on_fit_epoch_end,
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}
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if tune
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else {}
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)
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