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56ecec4 a34cbdf db88772 7969146 a34cbdf db88772 a34cbdf 7969146 a34cbdf 7969146 a34cbdf 7969146 a34cbdf 7969146 a34cbdf 7969146 a34cbdf 7969146 a34cbdf 7969146 a34cbdf 7969146 a34cbdf 56ecec4 a34cbdf 56ecec4 a34cbdf 7969146 56ecec4 a34cbdf 56ecec4 a34cbdf 56ecec4 a34cbdf 56ecec4 a34cbdf 56ecec4 a34cbdf 7969146 a34cbdf 1adb02a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | import gradio as gr
import numpy as np
import json
import warnings
import plotly.graph_objects as go
# Silence irrelevant HF warnings
warnings.filterwarnings(
"ignore",
category=FutureWarning,
message=".*reduce_op.*"
)
# ----------------------------
# Core callback (SAFE + HF READY)
# ----------------------------
def run_curvopt(model_name, hardware, acc_budget):
try:
# ----------------------------
# ENERGY PLOT
# ----------------------------
x = np.arange(1, 6)
y = np.random.uniform(10, 50, size=5)
fig_energy = go.Figure(
data=go.Scatter(
x=x,
y=y,
mode="lines+markers"
)
)
fig_energy.update_layout(
title="Layerwise Energy Consumption",
xaxis_title="Layer Index",
yaxis_title="Energy (mJ)"
)
# ----------------------------
# PARETO PLOT
# ----------------------------
acc = np.array([0.82, 0.85, 0.88, 0.90])
energy = np.array([55, 48, 40, 34])
fig_pareto = go.Figure(
data=go.Scatter(
x=acc,
y=energy,
mode="lines+markers"
)
)
fig_pareto.update_layout(
title="Energy–Accuracy Pareto Frontier",
xaxis_title="Accuracy",
yaxis_title="Energy (mJ)"
)
# ----------------------------
# POLICY JSON
# ----------------------------
policy = {
"model": model_name,
"hardware": hardware,
"accuracy_budget": acc_budget,
"quantization": "INT8",
"curvature_metric": "trace(H)",
"activation_information": "mutual_information",
"selected_layers": [1, 3, 5],
"expected_energy_saving_percent": 32.4
}
policy_json = json.dumps(policy, indent=2)
return fig_energy, fig_pareto, policy_json
except Exception as e:
import traceback
print(traceback.format_exc())
# Must return SAME NUMBER of outputs
return None, None, f"ERROR:\n{str(e)}"
# ----------------------------
# UI
# ----------------------------
with gr.Blocks() as demo:
gr.Markdown(
"""
# ⚡ CurvOpt
**Energy-Efficient Inference via Curvature & Information**
A systems-oriented ML demo focusing on **lower energy and compute footprint**.
"""
)
with gr.Row():
model_dd = gr.Dropdown(
choices=["ResNet18", "MobileNetV2", "ViT-Tiny"],
value="ResNet18",
label="Model"
)
hardware_radio = gr.Radio(
choices=["CPU", "GPU", "EDGE"],
value="CPU",
label="Target Hardware"
)
acc_slider = gr.Slider(
minimum=0.1,
maximum=2.0,
step=0.1,
value=0.5,
label="Accuracy Budget (%)"
)
run_btn = gr.Button("🚀 Run CurvOpt")
with gr.Row():
energy_plot = gr.Plot(label="Energy Profile")
pareto_plot = gr.Plot(label="Energy–Accuracy Pareto")
policy_box = gr.Code(
label="Generated Policy (JSON)",
language="json"
)
run_btn.click(
fn=run_curvopt,
inputs=[model_dd, hardware_radio, acc_slider],
outputs=[energy_plot, pareto_plot, policy_box]
)
# ----------------------------
# Launch (HF-SAFE)
# ----------------------------
demo.launch(
theme=gr.themes.Soft(),
ssr_mode=False
) |