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f0e5200 | 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 | import plotly.graph_objects as go
from plotly.subplots import make_subplots
# ==========================================
# 1. YOUR DATA
# ==========================================
models = ['FP32 (Base)', 'INT8 Dynamic', 'INT8 Decoder-Only']
# Accuracy (multiplied by 100 for percentage)
accuracy = [36.0, 36.0, 38.0]
# Latency metrics
lat_mean = [3.11, 1.65, 1.66]
lat_p50 = [2.94, 1.54, 1.56]
lat_p90 = [4.64, 2.44, 2.48]
# ==========================================
# 2. SET UP THE SIDE-BY-SIDE LAYOUT
# ==========================================
fig = make_subplots(
rows=1, cols=2,
subplot_titles=(
"<b>Model Accuracy (Execution)</b>",
"<b>Inference Latency Profile</b>"
),
horizontal_spacing=0.1
)
# ==========================================
# 3. LEFT CHART: ACCURACY
# ==========================================
fig.add_trace(go.Bar(
x=models,
y=accuracy,
name="Execution Accuracy",
marker_color=['#94a3b8', '#38bdf8', '#10b981'], # Gray, Blue, Green
text=[f"{val:.1f}%" for val in accuracy],
textposition='auto',
textfont=dict(size=14, color='white', family="Arial Black"),
showlegend=False
), row=1, col=1)
# ==========================================
# 4. RIGHT CHART: LATENCY PROFILE
# ==========================================
# P50 Latency
fig.add_trace(go.Bar(
x=models, y=lat_p50,
name="Median (P50)",
marker_color="#ece80a" # Light Blue
), row=1, col=2)
# Mean Latency
fig.add_trace(go.Bar(
x=models, y=lat_mean,
name="Mean Latency",
marker_color="#3b4da9" # Standard Blue
), row=1, col=2)
# P90 Latency
fig.add_trace(go.Bar(
x=models, y=lat_p90,
name="90th Percentile (P90)",
marker_color="#d974e2" # Dark Blue
), row=1, col=2)
# ==========================================
# 5. APPLY ULTRA-MODERN STYLING
# ==========================================
fig.update_layout(
title=dict(
text="<b>Task 5: FP32 vs. INT8 Quantization Performance</b>",
font=dict(size=22, color='#1e293b'),
x=0.5
),
plot_bgcolor='white',
paper_bgcolor='white',
barmode='group',
legend=dict(
orientation="h",
yanchor="bottom", y=1.05,
xanchor="center", x=0.8,
bgcolor='rgba(255,255,255,0.8)'
),
font=dict(family="-apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif"),
margin=dict(t=120, b=60, l=60, r=40)
)
# Style Left Axes
fig.update_yaxes(title_text="<b>Accuracy (%)</b>", range=[0, 45], gridcolor='#f1f5f9', row=1, col=1)
fig.update_xaxes(tickfont=dict(weight='bold'), row=1, col=1)
# Style Right Axes
fig.update_yaxes(title_text="<b>Seconds per Query</b>", gridcolor='#f1f5f9', row=1, col=2)
fig.update_xaxes(tickfont=dict(weight='bold'), row=1, col=2)
# ==========================================
# 6. RENDER AND SAVE
# ==========================================
html_file = "task5_quantization_dashboard.html"
fig.write_html(html_file)
print(f"✅ Interactive Plotly Dashboard saved to: {html_file}")
fig.show() |