feat: ui, support disable cache (#1217)

you-n-g · web-flow · commit 92efe33fa9c8 · 2025-09-01T15:58:05.000+08:00
* fix: (drop) add cache_enable for functions in ui.utils

* fix: (drop) handle no logging, rename sota_exp to last_sota_exp for clarity in running_win
diff --git a/rdagent/log/ui/ds_trace.py b/rdagent/log/ui/ds_trace.py
@@ -475,7 +475,7 @@ def coding_win(data, base_exp, llm_data: dict | None = None):
         workspace_win(data["no_tag"].experiment_workspace)
 
 
-def running_win(data, base_exp, llm_data=None, sota_exp=None):
+def running_win(data, base_exp, llm_data=None, last_sota_exp=None):
     st.header("Running", divider="blue", anchor="running")
     if llm_data is not None:
         common_llm_data = llm_data.pop("no_tag", [])
@@ -491,7 +491,7 @@ def running_win(data, base_exp, llm_data=None, sota_exp=None):
         st.subheader("Exp Workspace (running final)")
         workspace_win(
             data["no_tag"].experiment_workspace,
-            cmp_workspace=sota_exp.experiment_workspace if sota_exp else None,
+            cmp_workspace=last_sota_exp.experiment_workspace if last_sota_exp else None,
             cmp_name="last SOTA(to_submit)",
         )
         st.subheader("Result")
@@ -555,7 +555,7 @@ def main_win(loop_id, llm_data=None):
         )
     if "running" in loop_data:
         # get last SOTA_exp_to_submit
-        sota_exp = None
+        last_sota_exp = None
         if "record" in loop_data:
             current_trace = loop_data["record"]["trace"]
             current_selection = current_trace.get_current_selection()
@@ -565,13 +565,15 @@ def main_win(loop_id, llm_data=None):
                 if len(parent_idxs) >= 2 and hasattr(current_trace, "idx2loop_id"):
                     parent_idx = parent_idxs[-2]
                     parent_loop_id = current_trace.idx2loop_id[parent_idx]
-                    sota_exp = state.data[parent_loop_id]["record"].get("sota_exp_to_submit", None)
+                    if parent_loop_id in state.data:
+                        # in some cases, the state.data is synthesized, logs does not necessarily exist
+                        last_sota_exp = state.data[parent_loop_id]["record"].get("sota_exp_to_submit", None)
 
         running_win(
             loop_data["running"],
             base_exp=loop_data["coding"].get("no_tag", None),
             llm_data=llm_data["running"] if llm_data else None,
-            sota_exp=sota_exp,
+            last_sota_exp=last_sota_exp,
         )
     if "feedback" in loop_data:
         feedback_win(loop_data["feedback"], llm_data.get("feedback", None) if llm_data else None)
diff --git a/rdagent/log/ui/utils.py b/rdagent/log/ui/utils.py
@@ -161,7 +161,6 @@ def map_stat(sota_mle_score: dict | None) -> str:
     return sota_exp_stat
 
 
-@cache_with_pickle(_log_path_hash_func, force=True)
 def get_best_report(log_path: Path) -> dict | None:
     log_storage = FileStorage(log_path)
     mle_reports = [extract_json(i.content) for i in log_storage.iter_msg(pattern="**/running/mle_score/*/*.pkl")]
@@ -176,11 +175,14 @@ def get_best_report(log_path: Path) -> dict | None:
     return None
 
 
+if UI_SETTING.enable_cache:
+    get_best_report = cache_with_pickle(_log_path_hash_func, force=True)(get_best_report)
+
+
 def _get_sota_exp_stat_hash_func(log_path: Path, selector: Literal["auto", "best_valid"] = "auto") -> str:
     return _log_path_hash_func(log_path) + selector
 
 
-@cache_with_pickle(_get_sota_exp_stat_hash_func, force=True)
 def get_sota_exp_stat(
     log_path: Path, selector: Literal["auto", "best_valid"] = "auto"
 ) -> tuple[DSExperiment | None, int | None, dict | None, str | None]:
@@ -253,11 +255,14 @@ def get_sota_exp_stat(
     return sota_exp, sota_loop_id, sota_mle_score, map_stat(sota_mle_score)
 
 
+if UI_SETTING.enable_cache:
+    get_sota_exp_stat = cache_with_pickle(_get_sota_exp_stat_hash_func, force=True)(get_sota_exp_stat)
+
+
 def _get_score_stat_hash_func(log_path: Path, sota_loop_id: int) -> str:
     return _log_path_hash_func(log_path) + str(sota_loop_id)
 
 
-@cache_with_pickle(_get_score_stat_hash_func, force=True)
 def get_score_stat(log_path: Path, sota_loop_id: int) -> tuple[float | None, float | None, bool, float | None]:
     """
     Get the scores before and after merge period.
@@ -351,7 +356,10 @@ def get_score_stat(log_path: Path, sota_loop_id: int) -> tuple[float | None, flo
     return valid_improve, test_improve, submit_is_merge, merge_sota_rate
 
 
-@cache_with_pickle(_log_path_hash_func, force=True)
+if UI_SETTING.enable_cache:
+    get_score_stat = cache_with_pickle(_get_score_stat_hash_func, force=True)(get_score_stat)
+
+
 def load_times_deprecated(log_path: Path):
     try:
         session_path = log_path / "__session__"
@@ -365,7 +373,10 @@ def load_times_deprecated(log_path: Path):
     return rd_times
 
 
-@cache_with_pickle(_log_path_hash_func, force=True)
+if UI_SETTING.enable_cache:
+    load_times_deprecated = cache_with_pickle(_log_path_hash_func, force=True)(load_times_deprecated)
+
+
 def load_times_info(log_path: Path) -> dict[int, dict[str, dict[Literal["start_time", "end_time"], datetime]]]:
     """
     Load timing information for each loop and step.
@@ -403,6 +414,10 @@ def load_times_info(log_path: Path) -> dict[int, dict[str, dict[Literal["start_t
     return times_info
 
 
+if UI_SETTING.enable_cache:
+    load_times_info = cache_with_pickle(_log_path_hash_func, force=True)(load_times_info)
+
+
 def _log_folders_summary_hash_func(log_folder: str | Path, hours: int | None = None):
     summary_p = Path(log_folder) / (f"summary.pkl" if hours is None else f"summary_{hours}h.pkl")
     if summary_p.exists():
@@ -412,7 +427,6 @@ def _log_folders_summary_hash_func(log_folder: str | Path, hours: int | None = N
     return md5_hash(hash_str)
 
 
-@cache_with_pickle(_log_folders_summary_hash_func, force=True)
 def get_summary_df(log_folder: str | Path, hours: int | None = None) -> tuple[dict, pd.DataFrame]:
     """Process experiment logs and generate summary DataFrame.
 
@@ -645,6 +659,10 @@ def compare_score(s1, s2):
     return summary, base_df
 
 
+if UI_SETTING.enable_cache:
+    get_summary_df = cache_with_pickle(_log_folders_summary_hash_func, force=True)(get_summary_df)
+
+
 def percent_df(summary_df: pd.DataFrame, show_origin=True) -> pd.DataFrame:
     """
     Convert the summary DataFrame to a percentage format.
