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trinity/common/config.py

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@@ -746,6 +746,8 @@ class MonitorConfig:
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monitor_type: str = "tensorboard"
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# the default args for monitor
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monitor_args: Optional[Dict] = None
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# whether to return detailed stats (mean, std, max, min) for evaluation metrics
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detailed_stats: bool = False
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# whether to enable ray timeline profile
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# the output file will be saved to `cache_dir/timeline.json`
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enable_ray_timeline: bool = False

trinity/explorer/scheduler.py

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@@ -114,14 +114,8 @@ def calculate_task_level_metrics(metrics: List[Dict], is_eval: bool) -> Dict[str
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[(bon_mean, bon_std), (won_mean, won_std)] = bootstrap_metric(
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data=values, subset_size=n, reduce_fns=[np.max, np.min], seed=42
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)
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result[f"{key}/best@{n}/mean"], result[f"{key}/best@{n}/std"] = (
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bon_mean,
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bon_std,
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)
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result[f"{key}/worst@{n}/mean"], result[f"{key}/worst@{n}/std"] = (
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won_mean,
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won_std,
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)
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result[f"{key}/best@{n}"] = bon_mean
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result[f"{key}/worst@{n}"] = won_mean
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return result
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else:
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return {

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