rename memory usage to peak memory

src/components/filters.py

@@ -52,6 +52,7 @@ def render_column_visibility() -> Set[str]:
             "Platform",
             "CPU Cores",
             "Total Memory (GB)",
+            "Peak Memory (GB)",
             "Memory Usage (%)",
         ],
         "Benchmark Info": [

src/components/visualizations.py

@@ -12,7 +12,7 @@ def create_performance_plot(
         return None
 
     if hover_data is None:
-        hover_data = ["CPU Cores", "Memory Usage (GB)"]
+        hover_data = ["CPU Cores", "Peak Memory (GB)"]
 
     fig = px.bar(
         df,
@@ -126,8 +126,8 @@ def render_performance_plots(df: pd.DataFrame, filters: Dict):
     # Include memory metrics if available
     if "Memory Usage (%)" in filtered_df.columns:
         agg_dict["Memory Usage (%)"] = "mean"
-    if "Memory Usage (GB)" in filtered_df.columns:
-        agg_dict["Memory Usage (GB)"] = "mean"
+    if "Peak Memory (GB)" in filtered_df.columns:
+        agg_dict["Peak Memory (GB)"] = "mean"
 
     # Include device info if available
     if "CPU Cores" in filtered_df.columns:
@@ -158,7 +158,7 @@ def render_performance_plots(df: pd.DataFrame, filters: Dict):
         "Token Generation": "TG Avg (t/s)",
         # "Token Generation (std)": "TG Std (t/s)",
         "Memory Usage (%) (mean)": "Memory Usage (%)",
-        "Memory Usage (GB) (mean)": "Memory Usage (GB)",
+        "Peak Memory (GB) (mean)": "Peak Memory (GB)",
         "PP Config (first)": "PP Config",
         "TG Config (first)": "TG Config",
         "Model Size (first)": "Model Size",
@@ -178,8 +178,8 @@ def render_performance_plots(df: pd.DataFrame, filters: Dict):
     hover_data = []
     if "CPU Cores" in plot_group.columns:
         hover_data.append("CPU Cores")
-    if "Memory Usage (GB)" in plot_group.columns:
-        hover_data.append("Memory Usage (GB)")
+    if "Peak Memory (GB)" in plot_group.columns:
+        hover_data.append("Peak Memory (GB)")
 
     # Create plots
     col1, col2 = st.columns(2)
@@ -228,7 +228,7 @@ def render_leaderboard_table(df: pd.DataFrame, filters: Dict):
             "Prompt Processing": ["mean", "std"],
             "Token Generation": ["mean", "std"],
             # "Memory Usage (%)": "mean",
-            "Memory Usage (GB)": "mean",  # For a given model, device, platform, mem should be the same.
+            "Peak Memory (GB)": "mean",  # For a given model, device, platform, mem should be the same.
             "Total Memory (GB)": "first",  # For a given model, device, platform, mem should be the same.
             "CPU Cores": "first",  # For a given model, device, platform, cpu cores should be the same.
             "Model Size": "first",  # model size should be the same for all.
@@ -288,7 +288,6 @@ def render_leaderboard_table(df: pd.DataFrame, filters: Dict):
             by=sort_cols, ascending=[False] + [True] * (len(sort_cols) - 1)
         )
 
-
     # Rename columns for display
     column_mapping = {
         "Prompt Processing (mean)": "PP Avg (t/s)",
@@ -297,7 +296,7 @@ def render_leaderboard_table(df: pd.DataFrame, filters: Dict):
         "Token Generation (mean)": "TG Avg (t/s)",
         "Token Generation (std)": "TG Std (t/s)",
         "Memory Usage (%) (mean)": "Memory Usage (%)",
-        "Memory Usage (GB) (mean)": "Memory Usage (GB)",
+        "Peak Memory (GB) (mean)": "Peak Memory (GB)",
         "PP Config (first)": "PP Config",
         "TG Config (first)": "TG Config",
         "Model Size (first)": "Model Size",
@@ -323,6 +322,7 @@ def render_leaderboard_table(df: pd.DataFrame, filters: Dict):
             "Platform": "Platform",
             "CPU Cores": "CPU Cores",
             "Total Memory (GB)": "Total Memory (GB)",
+            "Peak Memory (GB)": "Peak Memory (GB)",
             "Memory Usage (%)": "Memory Usage (%)",
             "PP Config": "PP Config",
             "TG Config": "TG Config",

src/services/firebase.py

@@ -79,7 +79,7 @@ def format_leaderboard_data(submissions: List[dict]) -> pd.DataFrame:
                     "Memory Usage (%)": benchmark_result.get("peakMemoryUsage", {}).get(
                         "percentage"
                     ),
-                    "Memory Usage (GB)": (
+                    "Peak Memory (GB)": (
                         round(
                             benchmark_result.get("peakMemoryUsage", {}).get("used", 0)
                             / (1024**3),