feat: base data science scenario UI (#525)

* base data science ui

* fix bug

* fix mle grade

* not cache when mle prepare

* fix

* fix grade sample

* fix a small bug

* fix

* cache mle score

* fix

* add gen_mle_score script

* update for mle score

* simple debug show

* small change

* summary folder

* add evo loop tag

* add loop id

* add comment

* fix CI

* add enable_cache for docker conf

* CI

* use setting data path

* fix ui bug

---------

Co-authored-by: yuanteli <1957922024@qq.com>

rdagent/app/data_science/loop.py

@@ -60,7 +60,7 @@ class DataScienceRDLoop(RDLoop):
 
     def direct_exp_gen(self, prev_out: dict[str, Any]):
         exp = self.exp_gen.gen(self.trace)
-        logger.log_object(exp, tag="direct_exp_gen")
+        logger.log_object(exp)
 
         # FIXME: this is for LLM debug webapp, remove this when the debugging is done.
         logger.log_object(exp, tag="debug_exp_gen")
@@ -83,14 +83,14 @@ class DataScienceRDLoop(RDLoop):
             else:
                 raise NotImplementedError(f"Unsupported component in DataScienceRDLoop: {exp.hypothesis.component}")
             exp.sub_tasks = []
-        logger.log_object(exp, tag="coding")
+        logger.log_object(exp)
         return exp
 
     def running(self, prev_out: dict[str, Any]):
         exp: DSExperiment = prev_out["coding"]
         if exp.next_component_required() is None:
             new_exp = self.runner.develop(exp)
-            logger.log_object(new_exp, tag="running")
+            logger.log_object(new_exp)
             return new_exp
         else:
             return exp
@@ -104,7 +104,7 @@ class DataScienceRDLoop(RDLoop):
                 reason=f"{exp.hypothesis.component} is completed.",
                 decision=True,
             )
-        logger.log_object(feedback, tag="feedback")
+        logger.log_object(feedback)
         return feedback
 
     def record(self, prev_out: dict[str, Any]):

rdagent/core/evolving_agent.py

@@ -58,51 +58,51 @@ class RAGEvoAgent(EvoAgent):
         eva: Evaluator | Feedback,
         filter_final_evo: bool = False,
     ) -> EvolvableSubjects:
-        for _ in tqdm(range(self.max_loop), "Implementing"):
-            # with logger.tag(f"evo_loop_{evo_loop_id}"):
-            # 1. knowledge self-evolving
-            if self.knowledge_self_gen and self.rag is not None:
-                self.rag.generate_knowledge(self.evolving_trace)
-            # 2. RAG
-            queried_knowledge = None
-            if self.with_knowledge and self.rag is not None:
-                # TODO: Putting the evolving trace in here doesn't actually work
-                queried_knowledge = self.rag.query(evo, self.evolving_trace)
+        for evo_loop_id in tqdm(range(self.max_loop), "Implementing"):
+            with logger.tag(f"evo_loop_{evo_loop_id}"):
+                # 1. knowledge self-evolving
+                if self.knowledge_self_gen and self.rag is not None:
+                    self.rag.generate_knowledge(self.evolving_trace)
+                # 2. RAG
+                queried_knowledge = None
+                if self.with_knowledge and self.rag is not None:
+                    # TODO: Putting the evolving trace in here doesn't actually work
+                    queried_knowledge = self.rag.query(evo, self.evolving_trace)
 
-            # 3. evolve
-            evo = self.evolving_strategy.evolve(
-                evo=evo,
-                evolving_trace=self.evolving_trace,
-                queried_knowledge=queried_knowledge,
-            )
-            # TODO: Due to design issues, we have chosen to ignore this mypy error.
-            logger.log_object(evo.sub_workspace_list, tag="evolving code")  # type: ignore[attr-defined]
-            for sw in evo.sub_workspace_list:  # type: ignore[attr-defined]
-                logger.info(f"evolving code workspace: {sw}")
-
-            # 4. Pack evolve results
-            es = EvoStep(evo, queried_knowledge)
-
-            # 5. Evaluation
-            if self.with_feedback:
-                es.feedback = (
-                    # TODO: Due to the irregular design of rdagent.core.evaluation.Evaluator,
-                    # it fails mypy's test here, so we'll ignore this error for now.
-                    eva
-                    if isinstance(eva, Feedback)
-                    else eva.evaluate(evo, queried_knowledge=queried_knowledge)  # type: ignore[arg-type, call-arg]
+                # 3. evolve
+                evo = self.evolving_strategy.evolve(
+                    evo=evo,
+                    evolving_trace=self.evolving_trace,
+                    queried_knowledge=queried_knowledge,
                 )
-                logger.log_object(es.feedback, tag="evolving feedback")
+                # TODO: Due to design issues, we have chosen to ignore this mypy error.
+                logger.log_object(evo.sub_workspace_list, tag="evolving code")  # type: ignore[attr-defined]
+                for sw in evo.sub_workspace_list:  # type: ignore[attr-defined]
+                    logger.info(f"evolving code workspace: {sw}")
 
-            # 6. update trace
-            self.evolving_trace.append(es)
+                # 4. Pack evolve results
+                es = EvoStep(evo, queried_knowledge)
 
-            # 7. check if all tasks are completed
-            if self.with_feedback:
-                all_completed = all(es.feedback) if isinstance(es.feedback, list) else es.feedback
-                if all_completed:
-                    logger.info("All tasks in evolving subject have been completed.")
-                    break
+                # 5. Evaluation
+                if self.with_feedback:
+                    es.feedback = (
+                        # TODO: Due to the irregular design of rdagent.core.evaluation.Evaluator,
+                        # it fails mypy's test here, so we'll ignore this error for now.
+                        eva
+                        if isinstance(eva, Feedback)
+                        else eva.evaluate(evo, queried_knowledge=queried_knowledge)  # type: ignore[arg-type, call-arg]
+                    )
+                    logger.log_object(es.feedback, tag="evolving feedback")
+
+                # 6. update trace
+                self.evolving_trace.append(es)
+
+                # 7. check if all tasks are completed
+                if self.with_feedback:
+                    all_completed = all(es.feedback) if isinstance(es.feedback, list) else es.feedback
+                    if all_completed:
+                        logger.info("All tasks in evolving subject have been completed.")
+                        break
 
         if self.with_feedback and filter_final_evo:
             evo = self.filter_evolvable_subjects_by_feedback(evo, self.evolving_trace[-1].feedback)

rdagent/log/mle_summary.py

@@ -0,0 +1,137 @@
+import json
+import re
+from collections import defaultdict
+from pathlib import Path
+
+import fire
+import pandas as pd
+
+from rdagent.app.data_science.conf import DS_RD_SETTING
+from rdagent.log.storage import FileStorage
+from rdagent.utils.env import DockerEnv, MLEBDockerConf
+
+mle_de_conf = MLEBDockerConf()
+mle_de_conf.extra_volumes = {
+    f"{DS_RD_SETTING.local_data_path}/zip_files": "/mle/data",
+}
+de = DockerEnv(conf=mle_de_conf)
+de.prepare()
+
+
+def extract_mle_json(log_content):
+    match = re.search(r"\{.*\}", log_content, re.DOTALL)
+    if match:
+        return json.loads(match.group(0))
+    return None
+
+
+def save_grade_info(log_trace_path: Path):
+    for msg in FileStorage(log_trace_path).iter_msg():
+        if "competition" in msg.tag:
+            competition = msg.content
+
+        if "running" in msg.tag:
+            msg.content.experiment_workspace.execute(
+                env=de,
+                entry=f"bash -c 'mlebench grade-sample submission.csv {competition} --data-dir /mle/data > mle_score.txt 2>&1'",
+            )
+            msg.content.experiment_workspace.execute(env=de, entry="chmod 777 mle_score.txt")
+
+
+def save_all_grade_info(log_folder):
+    for log_trace_path in log_folder.iterdir():
+        save_grade_info(log_trace_path)
+
+
+def summarize_folder(log_folder: Path):
+    stat = defaultdict(dict)
+    for log_trace_path in log_folder.iterdir():  # One log trace
+        if not log_trace_path.is_dir():
+            continue
+        loop_num = 0
+        made_submission_num = 0
+        test_scores = {}
+        valid_scores = {}
+        medal = "None"
+        success_loop_num = 0
+
+        for msg in FileStorage(log_trace_path).iter_msg():  # messages in log trace
+            if "competition" in msg.tag:
+                stat[log_trace_path.name]["competition"] = msg.content
+
+            if "direct_exp_gen" in msg.tag:
+                loop_num += 1
+            if "running" in msg.tag:
+
+                submission_path = msg.content.experiment_workspace.workspace_path / "submission.csv"
+                if submission_path.exists():
+                    made_submission_num += 1
+                    scores_path = msg.content.experiment_workspace.workspace_path / "scores.csv"
+                    valid_scores[loop_num - 1] = pd.read_csv(scores_path, index_col=0)
+                    grade_output_path = msg.content.experiment_workspace.workspace_path / "mle_score.txt"
+                    if not grade_output_path.exists():
+                        raise FileNotFoundError(f"mle_score.txt in {grade_output_path} not found, genarate it first!")
+                    grade_output = extract_mle_json(grade_output_path.read_text())
+                    if grade_output["score"] is not None:
+                        test_scores[loop_num - 1] = grade_output["score"]
+                        if grade_output["any_medal"]:
+                            medal = (
+                                "gold"
+                                if grade_output["gold_medal"]
+                                else "silver" if grade_output["silver_medal"] else "bronze"
+                            )
+
+            if "feedback" in msg.tag and "evolving" not in msg.tag:
+                if bool(msg.content):
+                    success_loop_num += 1
+
+        stat[log_trace_path.name].update(
+            {
+                "loop_num": loop_num,
+                "made_submission_num": made_submission_num,
+                "test_scores": test_scores,
+                "valid_scores": valid_scores,
+                "medal": medal,
+                "success_loop_num": success_loop_num,
+            }
+        )
+    if (log_folder / "summary.pkl").exists():
+        (log_folder / "summary.pkl").unlink()
+        print("Old summary file removed.")
+    pd.to_pickle(stat, log_folder / "summary.pkl")
+
+
+# {
+#     "competition_id": "stanford-covid-vaccine",
+#     "score": null,
+#     "gold_threshold": 0.34728,
+#     "silver_threshold": 0.35175,
+#     "bronze_threshold": 0.3534,
+#     "median_threshold": 0.363095,
+#     "any_medal": false,
+#     "gold_medal": false,
+#     "silver_medal": false,
+#     "bronze_medal": false,
+#     "above_median": false,
+#     "submission_exists": true,
+#     "valid_submission": false,
+#     "is_lower_better": true,
+#     "created_at": "2025-01-21T11:59:33.788201",
+#     "submission_path": "submission.csv"
+# }
+
+
+def grade_summary(log_folder):
+    log_folder = Path(log_folder)
+    save_all_grade_info(log_folder)
+    summarize_folder(log_folder)
+
+
+if __name__ == "__main__":
+    fire.Fire(
+        {
+            "grade": save_all_grade_info,
+            "summary": summarize_folder,
+            "grade_summary": grade_summary,
+        }
+    )

rdagent/log/ui/app.py

@@ -1,4 +1,5 @@
 import argparse
+import re
 import textwrap
 from collections import defaultdict
 from datetime import datetime, timezone
@@ -143,6 +144,13 @@ def get_msgs_until(end_func: Callable[[Message], bool] = lambda _: True):
         while True:
             try:
                 msg = next(state.fs)
+
+                # new scenario gen this tags, old version UI not have these tags.
+                msg.tag = re.sub(r"\.evo_loop_\d+", "", msg.tag)
+                msg.tag = re.sub(r"Loop_\d+\.[^.]+", "", msg.tag)
+                msg.tag = re.sub(r"\.\.", ".", msg.tag)
+                msg.tag = msg.tag.strip(".")
+
                 if should_display(msg):
                     tags = msg.tag.split(".")
                     if "r" not in state.current_tags and "r" in tags:

rdagent/log/ui/dsapp.py

@@ -0,0 +1,285 @@
+import json
+import re
+from collections import defaultdict
+from pathlib import Path
+
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import streamlit as st
+from plotly.subplots import make_subplots
+from streamlit import session_state as state
+
+from rdagent.log.mle_summary import extract_mle_json
+from rdagent.log.storage import FileStorage
+
+st.set_page_config(layout="wide", page_title="RD-Agent", page_icon="🎓", initial_sidebar_state="expanded")
+
+# 设置主日志路径
+if "log_folder" not in state:
+    state.log_folder = Path("./log")
+if "log_path" not in state:
+    state.log_path = None
+
+
+# @st.cache_data
+def load_data(log_path):
+    data = defaultdict(lambda: defaultdict(dict))
+    li = -1  # loop id
+    ei = -1  # evo id
+    for msg in FileStorage(state.log_folder / log_path).iter_msg():
+        if msg.tag and "llm" not in msg.tag and "session" not in msg.tag:
+            if msg.tag == "competition":
+                data["competition"] = msg.content
+                continue
+            if msg.tag == "direct_exp_gen":
+                li += 1
+                ei = -1
+
+            if "evolving " in msg.tag:
+                if "evolving code" in msg.tag:
+                    ei += 1
+                data[li][ei][msg.tag] = msg.content
+            else:
+                data[li][msg.tag] = msg.content
+
+    return data
+
+
+@st.cache_data
+def get_folders_sorted(log_path):
+    """缓存并返回排序后的文件夹列表，并加入进度打印"""
+    with st.spinner("正在加载文件夹列表..."):
+        folders = sorted(
+            (folder for folder in log_path.iterdir() if folder.is_dir() and list(folder.iterdir())),
+            key=lambda folder: folder.stat().st_mtime,
+            reverse=True,
+        )
+        st.write(f"找到 {len(folders)} 个文件夹")
+    return [folder.name for folder in folders]
+
+
+# UI - Sidebar
+with st.sidebar:
+    state.log_folder = Path(st.text_input("**Log Folder**", placeholder=state.log_folder, value=state.log_folder))
+    if not state.log_folder.exists():
+        st.warning(f"Path {state.log_folder} does not exist!")
+    folders = get_folders_sorted(state.log_folder)
+    st.selectbox(f"Select from :blue[**{state.log_folder.absolute()}**]", folders, key="log_path")
+
+    if st.button("Refresh Data"):
+        if state.log_path is None:
+            st.toast("Please select a log path first!", type="error")
+            st.stop()
+
+        state.data = load_data(state.log_path)
+        st.rerun()
+
+    show_all_summary = st.toggle("One Trace / Log Folder Summary", value=True)
+
+
+# UI windows
+def task_win(data):
+    with st.container(border=True):
+        st.markdown(f"**:violet[{data.name}]**")
+        st.markdown(data.description)
+        if hasattr(data, "architecture"):  # model task
+            st.markdown(
+                f"""
+    | Model_type | Architecture | hyperparameters |
+    |------------|--------------|-----------------|
+    | {data.model_type} | {data.architecture} | {data.hyperparameters} |
+    """
+            )
+
+
+def workspace_win(data):
+    show_files = {k: v for k, v in data.file_dict.items() if not "test" in k}
+    if len(show_files) > 0:
+        with st.expander(f"Files in :blue[{data.workspace_path}]"):
+            code_tabs = st.tabs(show_files.keys())
+            for ct, codename in zip(code_tabs, show_files.keys()):
+                with ct:
+                    st.code(
+                        show_files[codename],
+                        language=("python" if codename.endswith(".py") else "markdown"),
+                        wrap_lines=True,
+                    )
+    else:
+        st.markdown("No files in the workspace")
+
+
+def exp_gen_win(data):
+    st.header("Exp Gen", divider="blue")
+    st.subheader("Hypothesis")
+    st.markdown(data.hypothesis)
+
+    st.subheader("pending_tasks")
+    for tasks in data.pending_tasks_list:
+        task_win(tasks[0])
+    st.subheader("Exp Workspace", anchor="exp-workspace")
+    workspace_win(data.experiment_workspace)
+
+
+def evolving_win(data):
+    st.header("Code Evolving", divider="green")
+    if len(data) > 1:
+        evo_id = st.slider("Evolving", 0, len(data) - 1, 0)
+    else:
+        evo_id = 0
+
+    if evo_id in data:
+        st.subheader("codes")
+        workspace_win(data[evo_id]["evolving code"][0])
+        fb = data[evo_id]["evolving feedback"][0]
+        st.subheader("evolving feedback" + ("✅" if bool(fb) else "❌"), anchor="c_feedback")
+        f1, f2, f3 = st.tabs(["execution", "return_checking", "code"])
+        f1.code(fb.execution, wrap_lines=True)
+        f2.code(fb.return_checking, wrap_lines=True)
+        f3.code(fb.code, wrap_lines=True)
+    else:
+        st.markdown("No evolving.")
+
+
+def exp_after_coding_win(data):
+    st.header("Exp After Coding", divider="blue")
+    st.subheader("Exp Workspace", anchor="eac-exp-workspace")
+    workspace_win(data.experiment_workspace)
+
+
+def exp_after_running_win(data, mle_score):
+    st.header("Exp After Running", divider="blue")
+    st.subheader("Exp Workspace", anchor="ear-exp-workspace")
+    workspace_win(data.experiment_workspace)
+    st.subheader("Result")
+    st.write(data.result)
+    st.subheader("MLE Submission Score")
+    st.json(mle_score)
+
+
+def feedback_win(data):
+    st.header("Feedback" + ("✅" if bool(data) else "❌"), divider="orange")
+    st.code(data, wrap_lines=True)
+    if data.exception is not None:
+        st.markdown(f"**:red[Exception]**: {data.exception}")
+
+
+def sota_win(data):
+    st.header("SOTA Experiment", divider="rainbow")
+    if data:
+        st.subheader("Exp Workspace", anchor="sota-exp-workspace")
+        workspace_win(data.experiment_workspace)
+    else:
+        st.markdown("No SOTA experiment.")
+
+
+def main_win(data):
+    exp_gen_win(data["direct_exp_gen"])
+    evo_data = {k: v for k, v in data.items() if isinstance(k, int)}
+    evolving_win(evo_data)
+    if "coding" in data:
+        exp_after_coding_win(data["coding"])
+    if "running" in data:
+        exp_after_running_win(data["running"], data["mle_score"])
+    if "feedback" in data:
+        feedback_win(data["feedback"])
+    sota_win(data["SOTA experiment"])
+
+    with st.sidebar:
+        st.markdown(
+            f"""
+- [Exp Gen](#exp-gen)
+    - [Hypothesis](#hypothesis)
+    - [pending_tasks](#pending-tasks)
+    - [Exp Workspace](#exp-workspace)
+- [Code Evolving ({len(evo_data)})](#code-evolving)
+    - [codes](#codes)
+    - [evolving feedback](#c_feedback)
+{"- [Exp After Coding](#exp-after-coding)" if "coding" in data else ""}
+{"- [Exp After Running](#exp-after-running)" if "running" in data else ""}
+{"- [Feedback](#feedback)" if "feedback" in data else ""}
+- [SOTA Experiment](#sota-experiment)
+"""
+        )
+
+
+def summarize_data():
+    st.header("Summary", divider="rainbow")
+    df = pd.DataFrame(columns=["Component", "Running Score", "Feedback"], index=range(len(state.data) - 1))
+
+    for loop in range(len(state.data) - 1):
+        loop_data = state.data[loop]
+        df.loc[loop, "Component"] = loop_data["direct_exp_gen"].hypothesis.component
+
+        if "running" in loop_data:
+            if "mle_score" not in state.data[loop]:
+                mle_score_path = loop_data["running"].experiment_workspace.workspace_path / "mle_score.txt"
+                try:
+                    state.data[loop]["mle_score"] = extract_mle_json(mle_score_path.read_text())
+                    df.loc[loop, "Running Score"] = str(state.data[loop]["mle_score"]["score"])
+                except Exception as e:
+                    state.data[loop]["mle_score"] = str(e)
+                    df.loc[loop, "Running Score"] = "❌"
+        else:
+            df.loc[loop, "Running Score"] = "N/A"
+
+        if "feedback" in loop_data:
+            df.loc[loop, "Feedback"] = "✅" if bool(loop_data["feedback"]) else "❌"
+        else:
+            df.loc[loop, "Feedback"] = "N/A"
+    st.dataframe(df)
+
+
+def all_summarize_win():
+    if not (state.log_folder / "summary.pkl").exists():
+        st.warning(
+            f"No summary file found in {state.log_folder}\nRun:`dotenv run -- python rdagent/log/mle_summary.py grade_summary --log_folder=<your trace folder>`"
+        )
+        return
+    summary = pd.read_pickle(state.log_folder / "summary.pkl")
+    base_df = pd.DataFrame(
+        columns=["Competition", "Total Loops", "Made Submission", "Successful Final Decision", "Medal"],
+        index=summary.keys(),
+    )
+    for k, v in summary.items():
+        loop_num = v["loop_num"]
+        base_df.loc[k, "Competition"] = v["competition"]
+        base_df.loc[k, "Total Loops"] = loop_num
+        base_df.loc[k, "Made Submission"] = (
+            f"{v['made_submission_num']} ({round(v['made_submission_num'] / loop_num * 100, 2)}%)"
+        )
+        base_df.loc[k, "Successful Final Decision"] = (
+            f"{v['success_loop_num']} ({round(v['success_loop_num'] / loop_num * 100, 2)}%)"
+        )
+        base_df.loc[k, "Medal"] = v["medal"]
+    st.dataframe(base_df)
+    # write curve
+    for k, v in summary.items():
+        with st.container(border=True):
+            st.markdown(f"**:blue[{k}] - :violet[{v['competition']}]**")
+            vscores = {k: v.iloc[:, 0] for k, v in v["valid_scores"].items()}
+            if len(vscores) > 0:
+                metric_name = list(vscores.values())[0].name
+            else:
+                metric_name = "None"
+
+            fc1, fc2 = st.columns(2)
+            vdf = pd.DataFrame(vscores)
+            vdf.columns = [f"loop {i}" for i in vdf.columns]
+            f1 = px.line(vdf.T, markers=True, title=f"Valid scores (metric: {metric_name})")
+            fc1.plotly_chart(f1, key=f"{k}_v")
+
+            tscores = {f"loop {k}": v for k, v in v["test_scores"].items()}
+            tdf = pd.Series(tscores, name="score")
+            f2 = px.line(tdf, markers=True, title="Test scores")
+            fc2.plotly_chart(f2, key=k)
+
+
+# UI - Main
+if show_all_summary:
+    all_summarize_win()
+elif "data" in state:
+    st.title(state.data["competition"])
+    summarize_data()
+    loop_id = st.slider("Loop", 0, len(state.data) - 2, 0)
+    main_win(state.data[loop_id])

rdagent/log/ui/llm_st.py

@@ -72,8 +72,6 @@ with st.sidebar:
         load_data()
         st.rerun()
 
-    expand_all = st.toggle("Expand All", key="expand_all")
-
 
 # Helper functions
 def show_text(text, lang=None):
@@ -126,6 +124,67 @@ sorted_loop_ids = sorted(loop_groups.keys(), key=int)  # 假设 Loop ID 是数
 total_loops = len(sorted_loop_ids)
 total_pages = total_loops  # 每页展示一个 Loop
 
+
+# simple display
+# FIXME: Delete this simple UI if trace have tag(evo_id & loop_id)
+# with st.sidebar:
+#     start = int(st.text_input("start", 0))
+#     end = int(st.text_input("end", 100))
+# for m in session_state.data[start:end]:
+#     if "tpl" in m["tag"]:
+#         obj = m["obj"]
+#         uri = obj["uri"]
+#         tpl = obj["template"]
+#         cxt = obj["context"]
+#         rd = obj["rendered"]
+#         with st.expander(highlight_prompts_uri(uri), expanded=False, icon="⚙️"):
+#             t1, t2, t3 = st.tabs([":green[**Rendered**]", ":blue[**Template**]", ":orange[**Context**]"])
+#             with t1:
+#                 show_text(rd)
+#             with t2:
+#                 show_text(tpl, lang="django")
+#             with t3:
+#                 st.json(cxt)
+#     if "llm" in m["tag"]:
+#         obj = m["obj"]
+#         system = obj.get("system", None)
+#         user = obj["user"]
+#         resp = obj["resp"]
+#         with st.expander(f"**LLM**", expanded=False, icon="🤖"):
+#             t1, t2, t3 = st.tabs([":green[**Response**]", ":blue[**User**]", ":orange[**System**]"])
+#             with t1:
+#                 try:
+#                     rdict = json.loads(resp)
+#                     if "code" in rdict:
+#                         code = rdict["code"]
+#                         st.markdown(":red[**Code in response dict:**]")
+#                         st.code(code, language="python", wrap_lines=True, line_numbers=True)
+#                         rdict.pop("code")
+#                     elif "spec" in rdict:
+#                         spec = rdict["spec"]
+#                         st.markdown(":red[**Spec in response dict:**]")
+#                         st.markdown(spec)
+#                         rdict.pop("spec")
+#                     else:
+#                         # show model codes
+#                         showed_keys = []
+#                         for k, v in rdict.items():
+#                             if k.startswith("model_") and k.endswith(".py"):
+#                                 st.markdown(f":red[**{k}**]")
+#                                 st.code(v, language="python", wrap_lines=True, line_numbers=True)
+#                                 showed_keys.append(k)
+#                         for k in showed_keys:
+#                             rdict.pop(k)
+#                     st.write(":red[**Other parts (except for the code or spec) in response dict:**]")
+#                     st.json(rdict)
+#                 except:
+#                     st.json(resp)
+#             with t2:
+#                 show_text(user)
+#             with t3:
+#                 show_text(system or "No system prompt available")
+
+
 if total_pages:
     # 初始化 current_loop
     if "current_loop" not in st.session_state:

rdagent/utils/env.py

@@ -144,6 +144,8 @@ class DockerConf(ExtendedBaseSettings):
 
     running_timeout_period: int = 3600  # 1 hour
 
+    enable_cache: bool = True  # enable the cache mechanism
+
 
 class QlibDockerConf(DockerConf):
     model_config = ExtendedSettingsConfigDict(env_prefix="QLIB_DOCKER_")
@@ -227,6 +229,7 @@ class MLEBDockerConf(DockerConf):
     mem_limit: str | None = (
         "48g"  # Add memory limit attribute # new-york-city-taxi-fare-prediction may need more memory
     )
+    enable_cache: bool = False
 
 
 # physionet.org/files/mimic-eicu-fiddle-feature/1.0.0/FIDDLE_mimic3
@@ -458,7 +461,10 @@ class DockerEnv(Env[DockerConf]):
         )
 
         start = time.time()
-        out = self.cached_run(entry_add_timeout, local_path, env, running_extra_volume)
+        if self.conf.enable_cache:
+            out = self.cached_run(entry_add_timeout, local_path, env, running_extra_volume)
+        else:
+            out = self.__run(entry, local_path, env, running_extra_volume, remove_timestamp=False)
         end = time.time()
 
         if end - start + 1 >= self.conf.running_timeout_period:

rdagent/utils/workflow.py

@@ -111,29 +111,29 @@ class LoopBase:
                 li, si = self.loop_idx, self.step_idx
 
                 name = self.steps[si]
-                # with logger.tag(f"Loop_{li}.{name}"):
-                start = datetime.datetime.now(datetime.timezone.utc)
-                func = getattr(self, name)
-                try:
-                    self.loop_prev_out[name] = func(self.loop_prev_out)
-                    # TODO: Fix the error logger.exception(f"Skip loop {li} due to {e}")
-                except self.skip_loop_error as e:
-                    # FIXME: This does not support previous demo (due to their last step is not for recording)
-                    logger.warning(f"Skip loop {li} due to {e}")
-                    # NOTE: strong assumption!  The last step is responsible for recording information
-                    self.step_idx = len(self.steps) - 1  # directly jump to the last step.
-                    self.loop_prev_out[self.EXCEPTION_KEY] = e
-                    continue
-                finally:
-                    # make sure failure steps are displayed correclty
-                    end = datetime.datetime.now(datetime.timezone.utc)
-                    self.loop_trace[li].append(LoopTrace(start, end, step_idx=si))
+                with logger.tag(f"Loop_{li}.{name}"):
+                    start = datetime.datetime.now(datetime.timezone.utc)
+                    func = getattr(self, name)
+                    try:
+                        self.loop_prev_out[name] = func(self.loop_prev_out)
+                        # TODO: Fix the error logger.exception(f"Skip loop {li} due to {e}")
+                    except self.skip_loop_error as e:
+                        # FIXME: This does not support previous demo (due to their last step is not for recording)
+                        logger.warning(f"Skip loop {li} due to {e}")
+                        # NOTE: strong assumption!  The last step is responsible for recording information
+                        self.step_idx = len(self.steps) - 1  # directly jump to the last step.
+                        self.loop_prev_out[self.EXCEPTION_KEY] = e
+                        continue
+                    finally:
+                        # make sure failure steps are displayed correclty
+                        end = datetime.datetime.now(datetime.timezone.utc)
+                        self.loop_trace[li].append(LoopTrace(start, end, step_idx=si))
 
-                    # Update tqdm progress bar directly to step_idx
-                    pbar.n = si + 1
-                    pbar.set_postfix(
-                        loop_index=li, step_index=si + 1, step_name=name
-                    )  # step_name indicate  last finished step_name
+                        # Update tqdm progress bar directly to step_idx
+                        pbar.n = si + 1
+                        pbar.set_postfix(
+                            loop_index=li, step_index=si + 1, step_name=name
+                        )  # step_name indicate  last finished step_name
 
                 # index increase and save session
                 self.step_idx = (self.step_idx + 1) % len(self.steps)