Update files

app.py

@@ -7,15 +7,16 @@ import gradio as gr
 
 DATASETS = []
 BENCHMARKS = {
-    # Name: (device, AMP, compile)
-    "Parameters": (None, None, None),
-    "GPU Memory": (None, None, None),
-    "CPU rate": ("cpu", False, False),
-    "CPU rate with compile": ("cpu", False, True),
-    "CPU rate AMP with compile": ("cpu", True, True),
-    "CUDA rate": ("cuda", False, False),
-    "CUDA rate with compile": ("cuda", False, True),
-    "CUDA rate AMP with compile": ("cuda", True, True),
+    # Name: (device, AMP, compile, single thread)
+    "Parameters": (None, None, None, None),
+    "GPU Memory": (None, None, None, None),
+    "CPU rate": ("cpu", False, False, False),
+    "CPU rate single core": ("cpu", False, False, True),
+    "CPU rate with compile": ("cpu", False, True, False),
+    "CPU rate AMP with compile": ("cpu", True, True, False),
+    "CUDA rate": ("cuda", False, False, False),
+    "CUDA rate with compile": ("cuda", False, True, False),
+    "CUDA rate AMP with compile": ("cuda", True, True, False),
 }
 
 
@@ -89,11 +90,13 @@ def plot_acc_rate(rate_compare_results_df: pl.DataFrame, width: int = 1000, heig
         compiled = rate_compare_results_df["compile"][0]
         batch_size = rate_compare_results_df["batch_size"][0]
         amp = rate_compare_results_df["amp"][0]
+        single_thread = rate_compare_results_df["single_thread"][0]
     else:
         device = ""
         compiled = ""
         batch_size = ""
         amp = ""
+        single_thread = False
 
     df = rate_compare_results_df.select(
         "Model name",
@@ -126,8 +129,17 @@ def plot_acc_rate(rate_compare_results_df: pl.DataFrame, width: int = 1000, heig
     )
 
     chart = base + text + frontier
+
+    if single_thread is True:
+        single_thread_title = " Single Core"
+    else:
+        single_thread_title = ""
+
     return chart.properties(
-        title=f"Accuray vs {device.upper()} Rate (compile={compiled}, batch size={batch_size}, amp={amp})",
+        title=(
+            f"Accuracy vs {device.upper()}{single_thread_title} Rate (compile={compiled}, "
+            f"batch size={batch_size}, amp={amp})"
+        ),
         width=width,
         height=height,
     ).configure_scale(zero=False)
@@ -149,7 +161,7 @@ def update_data(
 
     # Parameter count
     if benchmark == "Parameters":
-        param_compare_results_df = compare_results_df.unique(subset=["Model name"]).sort(
+        param_compare_results_df = compare_results_df.unique(subset=["Model name", "Resolution"]).sort(
             "Parameters (M)", descending=False
         )
         param_compare_results_df = param_compare_results_df.with_columns(
@@ -168,7 +180,7 @@ def update_data(
     # Peak memory
     elif benchmark == "GPU Memory":
         memory_compare_results_df = compare_results_df.drop_nulls(subset=["Peak GPU memory (MB)"])
-        memory_compare_results_df = memory_compare_results_df.unique(subset=["Model name"]).sort(
+        memory_compare_results_df = memory_compare_results_df.unique(subset=["Model name", "Resolution"]).sort(
             "Peak GPU memory (MB)", descending=False
         )
         memory_compare_results_df = memory_compare_results_df.with_columns(
@@ -183,10 +195,10 @@ def update_data(
 
     # Rate
     else:
-        (device, amp_enabled, compiled) = BENCHMARKS[benchmark]
+        (device, amp_enabled, compiled, single_thread) = BENCHMARKS[benchmark]
         df = compare_results_df.drop_nulls(subset=["ms / sample"])
-        df = df.filter(device=device, amp=amp_enabled, compile=compiled)
-        device_compare_results_df = df.unique(subset=["Model name"]).sort("ms / sample", descending=False)
+        df = df.filter(device=device, amp=amp_enabled, compile=compiled, single_thread=single_thread)
+        device_compare_results_df = df.unique(subset=["Model name", "Resolution"]).sort("ms / sample", descending=False)
         device_compare_results_df = device_compare_results_df.drop("Peak GPU memory (MB)")
         device_compare_results_df = device_compare_results_df.with_columns(
             pl.col("Accuracy").cum_max().alias("Pareto frontier (ms)")
@@ -209,7 +221,7 @@ def update_data(
         ]
     )
 
-    return (chart, output_df.drop("Mistakes", "Samples"))
+    return (chart, output_df.drop("Mistakes", "Samples", "torch_version"))
 
 
 def app() -> None:
@@ -226,6 +238,7 @@ def app() -> None:
 
                     * GPU: A5000 ADA Generation
                     * CPU: AMD Ryzen Threadripper PRO 7975WX
+                    * PyTorch version: 2.5.1+cu124
                     """
                 )