Latest dashboard version

graphs.py

@@ -18,9 +18,9 @@ colors = {
     "ocean_green": "#3ba272",
 }
 device_colors = {
-    "x86": "#0071c5",
-    "nvidia": "#76b900",
-    "groq": "#F55036",
+    "x86": colors["blue"],
+    "nvidia": colors["green"],
+    "groq": colors["orange"],
 }
 
 
@@ -64,19 +64,19 @@ def stages_count_summary(current_df: pd.DataFrame, prev_df: pd.DataFrame) -> Non
     )
 
     kpi[1].metric(
-        label="Convert to ONNX",
+        label="Converts to ONNX",
         value=current.base_onnx,
         delta=current.base_onnx - prev.base_onnx,
     )
 
     kpi[2].metric(
-        label="Optimize ONNX file",
+        label="Optimizes ONNX file",
         value=current.optimized_onnx,
         delta=current.optimized_onnx - prev.optimized_onnx,
     )
 
     kpi[3].metric(
-        label="All ops supported",
+        label="Supports all ops",
         value=current.all_ops_supported,
         delta=current.all_ops_supported - prev.all_ops_supported,
     )
@@ -102,12 +102,13 @@ def stages_count_summary(current_df: pd.DataFrame, prev_df: pd.DataFrame) -> Non
     # Show Sankey graph with percentages
     sk_val = {
         "All models": "100%",
-        "Convert to ONNX": str(int(100 * current.base_onnx / current.all_models)) + "%",
-        "Optimize ONNX file": str(
+        "Converts to ONNX": str(int(100 * current.base_onnx / current.all_models))
+        + "%",
+        "Optimizes ONNX file": str(
             int(100 * current.optimized_onnx / current.all_models)
         )
         + "%",
-        "All ops supported": str(
+        "Supports all ops": str(
             int(100 * current.all_ops_supported / current.all_models)
         )
         + "%",
@@ -138,18 +139,18 @@ def stages_count_summary(current_df: pd.DataFrame, prev_df: pd.DataFrame) -> Non
                     "itemStyle": {"color": "white", "borderColor": "white"},
                 },
                 {
-                    "name": "Convert to ONNX",
-                    "value": sk_val["Convert to ONNX"],
+                    "name": "Converts to ONNX",
+                    "value": sk_val["Converts to ONNX"],
                     "itemStyle": {"color": "white", "borderColor": "white"},
                 },
                 {
-                    "name": "Optimize ONNX file",
-                    "value": sk_val["Optimize ONNX file"],
+                    "name": "Optimizes ONNX file",
+                    "value": sk_val["Optimizes ONNX file"],
                     "itemStyle": {"color": "white", "borderColor": "white"},
                 },
                 {
-                    "name": "All ops supported",
-                    "value": sk_val["All ops supported"],
+                    "name": "Supports all ops",
+                    "value": sk_val["Supports all ops"],
                     "itemStyle": {"color": "white", "borderColor": "white"},
                 },
                 {
@@ -179,21 +180,21 @@ def stages_count_summary(current_df: pd.DataFrame, prev_df: pd.DataFrame) -> Non
             "links": [
                 {
                     "source": "All models",
-                    "target": "Convert to ONNX",
+                    "target": "Converts to ONNX",
                     "value": current.base_onnx,
                 },
                 {
-                    "source": "Convert to ONNX",
-                    "target": "Optimize ONNX file",
+                    "source": "Converts to ONNX",
+                    "target": "Optimizes ONNX file",
                     "value": current.optimized_onnx,
                 },
                 {
-                    "source": "Optimize ONNX file",
-                    "target": "All ops supported",
+                    "source": "Optimizes ONNX file",
+                    "target": "Supports all ops",
                     "value": current.all_ops_supported,
                 },
                 {
-                    "source": "All ops supported",
+                    "source": "Supports all ops",
                     "target": "Converts to FP16",
                     "value": current.fp16_onnx,
                 },
@@ -489,14 +490,14 @@ def speedup_bar_chart(df: pd.DataFrame, baseline) -> None:
         )
 
 
-def kpi_to_markdown(compute_ratio, device, is_baseline=False, color="#FFFFFF"):
+def kpi_to_markdown(compute_ratio, device, is_baseline=False, color="blue"):
 
     title = f"""<br><br>
     <p style="font-family:sans-serif; font-size: 20px;text-align: center;">Median {device} Acceleration ({len(compute_ratio)} models):</p>"""
     if is_baseline:
         return (
             title
-            + f"""<p style="font-family:sans-serif; color:{color}; font-size: 26px;text-align: center;"> {1}x (Baseline)</p>"""
+            + f"""<p style="font-family:sans-serif; color:{colors[color]}; font-size: 26px;text-align: center;"> {1}x (Baseline)</p>"""
         )
 
     if len(compute_ratio) > 0:
@@ -510,8 +511,8 @@ def kpi_to_markdown(compute_ratio, device, is_baseline=False, color="#FFFFFF"):
 
     return (
         title
-        + f"""<p style="font-family:sans-serif; color:{color}; font-size: 26px;text-align: center;"> {kpi_median}x</p>
-    <p style="font-family:sans-serif; color:{color}; font-size: 20px;text-align: center;"> min {kpi_min}x; max {kpi_max}x</p>
+        + f"""<p style="font-family:sans-serif; color:{colors[color]}; font-size: 26px;text-align: center;"> {kpi_median}x</p>
+    <p style="font-family:sans-serif; color:{colors[color]}; font-size: 20px;text-align: center;"> min {kpi_min}x; max {kpi_max}x</p>
     """
     )
 
@@ -536,19 +537,19 @@ def speedup_text_summary(df: pd.DataFrame, baseline) -> None:
     x86_text = kpi_to_markdown(
         x86_compute_ratio,
         device="Intel(R) Xeon(R) X40 CPU @ 2.00GHz",
-        color=device_colors["x86"],
+        color="blue",
         is_baseline=baseline == "x86",
     )
     groq_text = kpi_to_markdown(
         groq_compute_ratio,
         device="GroqChip 1",
-        color=device_colors["groq"],
+        color="orange",
         is_baseline=baseline == "groq",
     )
     nvidia_text = kpi_to_markdown(
         nvidia_compute_ratio,
         device="NVIDIA A100-PCIE-40GB",
-        color=device_colors["nvidia"],
+        color="green",
         is_baseline=baseline == "nvidia",
     )
 
@@ -650,10 +651,10 @@ def device_funnel(df: pd.DataFrame) -> None:
     # Show Sankey graph with percentages
     sk_val = {
         "All models": f"{summ.all_models} models - 100%",
-        "Convert to ONNX": f"{summ.base_onnx} models - "
+        "Converts to ONNX": f"{summ.base_onnx} models - "
         + str(int(100 * summ.base_onnx / summ.all_models))
         + "%",
-        "Optimize ONNX file": f"{summ.optimized_onnx} models - "
+        "Optimizes ONNX file": f"{summ.optimized_onnx} models - "
         + str(int(100 * summ.optimized_onnx / summ.all_models))
         + "%",
         "Converts to FP16": f"{summ.fp16_onnx} models - "
@@ -693,13 +694,13 @@ def device_funnel(df: pd.DataFrame) -> None:
                     "itemStyle": {"color": "white", "borderColor": "white"},
                 },
                 {
-                    "name": "Convert to ONNX",
-                    "value": sk_val["Convert to ONNX"],
+                    "name": "Converts to ONNX",
+                    "value": sk_val["Converts to ONNX"],
                     "itemStyle": {"color": "white", "borderColor": "white"},
                 },
                 {
-                    "name": "Optimize ONNX file",
-                    "value": sk_val["Optimize ONNX file"],
+                    "name": "Optimizes ONNX file",
+                    "value": sk_val["Optimizes ONNX file"],
                     "itemStyle": {"color": "white", "borderColor": "white"},
                 },
                 {
@@ -743,16 +744,16 @@ def device_funnel(df: pd.DataFrame) -> None:
             "links": [
                 {
                     "source": "All models",
-                    "target": "Convert to ONNX",
+                    "target": "Converts to ONNX",
                     "value": summ.all_models,
                 },
                 {
-                    "source": "Convert to ONNX",
-                    "target": "Optimize ONNX file",
+                    "source": "Converts to ONNX",
+                    "target": "Optimizes ONNX file",
                     "value": summ.optimized_onnx,
                 },
                 {
-                    "source": "Optimize ONNX file",
+                    "source": "Optimizes ONNX file",
                     "target": "Converts to FP16",
                     "value": summ.fp16_onnx,
                 },