Split the visualization module and optimize the operator page

dashboard/pages/communication.py

@@ -12,9 +12,9 @@
     plot_metric_vs_size,
     plot_comparison_matrix,
     create_gauge_chart,
-    create_summary_table,
     plot_timeseries_auto,
     create_summary_table_infer,
+    create_summary_table_comm,
 )
 
 init_page("通信测试分析 | InfiniMetrics", "🔗")
@@ -293,7 +293,7 @@ def main():
                     f"{run['operation']} ({run['device_used']} GPUs) - 配置详情"
                 ):
                     # Create summary table
-                    summary_df = create_summary_table(run.get("data", {}))
+                    summary_df = create_summary_table_comm(run.get("data", {}))
                     st.dataframe(
                         summary_df,
                         use_container_width=True,

dashboard/pages/operator.py

@@ -9,6 +9,7 @@
 from utils.visualizations import (
     create_summary_table_ops,
     plot_timeseries_auto,
+    render_operator_performance_charts,
 )
 
 init_page("算子测试分析 | InfiniMetrics", "⚡")
@@ -37,6 +38,10 @@ def main():
         only_success = st.checkbox("仅显示成功测试", value=True)
         y_log = st.checkbox("Y轴对数刻度（可选）", value=False)
 
+        st.markdown("---")
+        st.markdown("### 📊 图表选项")
+        show_performance_charts = st.checkbox("显示性能仪表盘", value=True)
+
     filtered = [r for r in ops_runs if (not only_success or r.get("success"))]
 
     st.caption(f"找到 {len(filtered)} 个算子测试")
@@ -63,35 +68,54 @@ def main():
         ri["data"] = data
         selected_runs.append(ri)
 
-    tab1, tab2 = st.tabs(["📌 概览", "📈 曲线/原始数据"])
+    tab1, tab2, tab3 = st.tabs(["📈 性能图表", "📌 概览", "📊 原始数据"])
 
     with tab1:
+        # Use the new performance chart function
+        render_operator_performance_charts(
+            selected_runs, y_log, show_performance_charts
+        )
+
+    with tab2:
         for run in selected_runs:
             with st.expander(f"{run.get('run_id')} - 概览"):
                 st.dataframe(
                     create_summary_table_ops(run["data"]),
                     use_container_width=True,
                     hide_index=True,
                 )
-                st.markdown("**config**")
+                st.markdown("**完整配置**")
                 st.json(run["data"].get("config", {}))
 
-    with tab2:
-        # If operators have timeseries CSVs, automatically plot them
+                env = run["data"].get("environment", {})
+                if env:
+                    st.markdown("**环境信息**")
+                    try:
+                        acc = env["cluster"][0]["machine"]["accelerators"][0]
+                        st.write(f"- 加速卡: {acc.get('model', 'Unknown')}")
+                        st.write(f"- 显存: {acc.get('memory_gb_per_card', '?')} GB")
+                        st.write(f"- CUDA版本: {acc.get('cuda', 'Unknown')}")
+                    except Exception:
+                        st.json(env)
+
+    with tab3:
+        # Original data
         for run in selected_runs:
-            with st.expander(f"{run.get('run_id')} - metrics"):
+            with st.expander(f"{run.get('run_id')} - 原始数据"):
                 for m in run["data"].get("metrics", []):
                     df = m.get("data")
                     if df is not None and len(df.columns) >= 2:
+                        st.markdown(f"**{m.get('name', 'metric')}**")
                         fig = plot_timeseries_auto(
                             df, title=m.get("name", "metric"), y_log_scale=y_log
                         )
                         st.plotly_chart(fig, use_container_width=True)
                     else:
                         # scalar
                         if m.get("type") == "scalar":
-                            st.write(
-                                f"- {m.get('name')}: {m.get('value')} {m.get('unit','')}"
+                            st.metric(
+                                label=m.get("name", ""),
+                                value=f"{m.get('value', '')} {m.get('unit', '')}",
                             )
 
 

dashboard/pages/training.py

@@ -12,7 +12,7 @@
     load_selected_runs,
     create_training_summary,
 )
-from utils.training_plots import (
+from utils.visualizations import (
     render_performance_curves,
     render_throughput_comparison,
     render_data_tables,

dashboard/utils/visualizations/__init__.py

@@ -4,37 +4,83 @@
 This package provides visualization utilities organized by test type:
 - base: Common/legacy visualization functions
 - hardware: Hardware test visualizations (memory sweep, cache bandwidth)
-- (future) communication: Communication test visualizations
-- (future) inference: Inference test visualizations
-- (future) operator: Operator test visualizations
+- communication: Communication test visualizations
+- inference: Inference test visualizations
+- operator: Operator test visualizations
+- training: Training test visualizations
+- summary_tables: Summary tables for different test types
 """
 
+# Base functions (common)
 from .base import (
-    plot_metric_vs_size,
-    plot_comparison_matrix,
-    create_summary_table,
     create_gauge_chart,
     plot_timeseries_auto,
+)
+
+# Communication functions
+from .communication import (
+    plot_metric_vs_size,
+    plot_comparison_matrix,
+)
+
+# Inference functions
+from .inference import (
+    render_inference_metrics,
+    render_memory_gauge,
+)
+
+# Summary tables
+from .summary_tables import (
+    create_summary_table_comm,
     create_summary_table_infer,
     create_summary_table_ops,
 )
+
+# Hardware functions
 from .hardware import (
     create_summary_table_hw,
     plot_hw_mem_sweep,
     plot_hw_cache,
 )
 
+# Operator functions
+from .operator import (
+    extract_operator_metrics,
+    render_operator_performance_charts,
+)
+
+# Training functions
+from .training import (
+    render_performance_curves,
+    render_throughput_comparison,
+    render_data_tables,
+    render_config_details,
+)
+
 __all__ = [
-    # Base (common/legacy)
-    "plot_metric_vs_size",
-    "plot_comparison_matrix",
-    "create_summary_table",
+    # Base
     "create_gauge_chart",
     "plot_timeseries_auto",
+    # Communication
+    "plot_metric_vs_size",
+    "plot_comparison_matrix",
+    # Inference
+    "render_inference_metrics",
+    "render_memory_gauge",
+    # Summary tables
+    "create_summary_table_comm",
     "create_summary_table_infer",
     "create_summary_table_ops",
     # Hardware
     "create_summary_table_hw",
     "plot_hw_mem_sweep",
     "plot_hw_cache",
+    # Operator
+    "extract_operator_metrics",
+    "render_operator_performance_charts",
+    # Training
+    "render_performance_curves",
+    "render_throughput_comparison",
+    "render_data_tables",
+    "render_config_details",
 ]