Create app.py

app.py

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+import re
+import gradio as gr
+import pandas as pd
+
+GPU_TFLOPS_NONSPARSE = dict(
+    rtx_3070=40.6,
+    rtx_3070_ti=43.5,
+    rtx_3080_mobile=30,
+    rtx_3080=59.5,
+    rtx_3090=71,
+    rtx_4070=58.3,
+    rtx_4070_ti=80.2,
+    rtx_4080=97.5,
+    rtx_4090=165.2,
+    rtx_5070=61.7,
+    rtx_5070_ti=87.9,
+    rtx_5080=112.6,
+    rtx_5090=209.5,
+
+    rtx_a6000=154.8,
+    rtx_6000_ada=364,
+    rtx_6000_blackwell_max_q=438.9,
+    rtx_6000_blackwell=503.8,
+
+    a100=312,
+    h100_sxm5=1000,
+    h100_pcie=800,
+)
+
+# === Categorization rules ===
+categories = {
+    # consumer RTX cards (30xx/40xx/50xx series, including mobile)
+    "consumer_rtx": [
+        k for k in GPU_TFLOPS_NONSPARSE
+        if any(x in k for x in ["3070", "3080", "3090", "4070", "4080", "4090", "5070", "5080", "5090"])
+    ],
+    # workstation 6000 family (A6000, Ada, Blackwell variants)
+    "workstation_6000": [k for k in GPU_TFLOPS_NONSPARSE if "6000" in k],
+    # datacenter accelerators
+    "datacenter": [k for k in GPU_TFLOPS_NONSPARSE if any(x in k for x in ["a100", "h100"])],
+}
+# === Formatting function ===
+def prettify_name(key: str) -> str:
+    """Convert internal GPU key names to human-friendly titles."""
+    name = key.replace("_", " ").upper()
+    name = re.sub(r"RTX A", "RTX A", name)
+    name = name.replace("TI", "Ti")
+    # Title case except GPU model identifiers (keep uppercase RTX/A/H)
+    name = " ".join(word.capitalize() if not word.startswith(("RTX", "A", "H")) else word for word in name.split())
+    name = name.replace("Max Q", "Max-Q")
+    name = name.replace("Pcie", "PCIe")
+    
+    name = name.replace("Smx5", "SXM5")
+    return name
+
+def make_df(filtered_dict: dict) -> pd.DataFrame:
+    df = pd.DataFrame(
+        [(prettify_name(k), v) for k, v in filtered_dict.items()],
+        columns=["GPU", "TFLOPS (non-sparse)"]
+    )
+    return df.sort_values(by="TFLOPS (non-sparse)", ascending=False, ignore_index=True)
+
+def filter_table(hide_consumer: bool, hide_workstation: bool, hide_datacenter: bool) -> pd.DataFrame:
+    data = GPU_TFLOPS_NONSPARSE.copy()
+
+    if hide_consumer:
+        for key in categories["consumer_rtx"]:
+            data.pop(key, None)
+    if hide_workstation:
+        for key in categories["workstation_6000"]:
+            data.pop(key, None)
+    if hide_datacenter:
+        for key in categories["datacenter"]:
+            data.pop(key, None)
+
+    return make_df(data)
+
+DEFAULT_DF = filter_table(False, False, False)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# BF16 GPU TFLOPS Viewer\nToggle categories to hide/show entries.\nWhen calculating the 'usable' TFLOPs of a particular card, these are what we call non-sparse TFLOPs. Below contains data gathered for the most commonly used CUDA cards and their BF16 with FP32 accum (what we use in PyTorch) values")
+
+    with gr.Row():
+        hide_consumer = gr.Checkbox(label="Hide Consumer RTX", value=False)
+        hide_workstation = gr.Checkbox(label="Hide Professional Workstation (6000)", value=False)
+        hide_datacenter = gr.Checkbox(label="Hide Datacenter Cards", value=False)
+
+    table = gr.Dataframe(
+        value=DEFAULT_DF,
+        headers=["GPU", "TFLOPS (non-sparse)"],
+        datatype=["str", "number"],
+        interactive=False,
+        wrap=True,
+        max_height=1000
+    )
+
+    for cb in (hide_consumer, hide_workstation, hide_datacenter):
+        cb.change(
+            fn=filter_table,
+            inputs=[hide_consumer, hide_workstation, hide_datacenter],
+            outputs=table
+        )
+
+    demo.load(fn=filter_table, inputs=[hide_consumer, hide_workstation, hide_datacenter], outputs=table)
+
+if __name__ == "__main__":
+    demo.launch()