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MilkClouds
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cdjkim
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‎.github/assets/demand_supply.png‎

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‎.github/assets/demand_supply.svg‎

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‎.github/assets/gen_demand_supply_chart.py‎

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"""Generate the demand/supply throughput figure for README.
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Adapted from the paper figure (scripts/plot_multiline.py) for web display.
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Usage:
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uv run --with matplotlib python .github/assets/gen_demand_supply_chart.py
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Output:
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.github/assets/demand_supply.png
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.github/assets/demand_supply.svg
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"""
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from matplotlib.lines import Line2D
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plt.rcParams.update(
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{
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"figure.dpi": 200,
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"font.family": "sans-serif",
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"font.sans-serif": ["DejaVu Sans"],
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"axes.linewidth": 0.6,
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"axes.grid": True,
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"grid.alpha": 0.15,
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"grid.linewidth": 0.4,
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}
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)
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# --- Data (LIBERO + CogACT, H100 — from docs/tuning-guide.md) ---
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N = [1, 8, 16, 24, 32, 50, 64, 80, 100]
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lam = [11.2, 82.9, 152.1, 214.2, 267.1, 364.6, 410.2, 446.8, 389.4]
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# Supply ceilings: batch size B -> max throughput (obs/s)
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supply_lines = [(1, 165.2), (2, 255.4), (4, 347.3), (8, 423.8), (16, 468.2)]
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# Operating point
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N_star, lam_star = 50, 364.6
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# --- Colors (AI2 palette, matching paper) ---
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C_DEMAND = "#0a3235"
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C_SUPPLY = "#8b4049" # muted burgundy
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C_SUPPLY_TXT = "#6b2d35"
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C_OP = "#f0529c"
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C_OP_TXT = "#c0407d"
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fig, ax = plt.subplots(figsize=(7, 3.5))
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# Supply ceiling lines
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for b, m in supply_lines:
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ax.axhline(y=m, color=C_SUPPLY, linestyle="--", linewidth=1.0, alpha=0.85, zorder=1)
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ax.text(2, m + 6, f"$B$={b}", fontsize=8, color=C_SUPPLY_TXT, fontstyle="italic")
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# Demand curve: normal (up to operating point)
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ax.plot(
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N[:7],
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lam[:7],
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"o-",
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color=C_DEMAND,
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markersize=5,
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linewidth=1.6,
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markerfacecolor=C_DEMAND,
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markeredgecolor="white",
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markeredgewidth=0.5,
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zorder=3,
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)
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# Demand curve: oversaturated region
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ax.plot(
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N[6:], lam[6:], "o--", color=C_DEMAND, markersize=4, linewidth=0.9, markerfacecolor=C_DEMAND, alpha=0.35, zorder=3
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)
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# Operating point
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ax.plot(N_star, lam_star, "D", color=C_OP, markersize=9, zorder=5, markeredgecolor="white", markeredgewidth=0.7)
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ax.annotate(
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r"$N^*\!=\!50$",
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xy=(N_star, lam_star),
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xytext=(70, 260),
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fontsize=11,
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color=C_OP_TXT,
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ha="center",
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weight="bold",
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arrowprops=dict(arrowstyle="->", color=C_OP_TXT, lw=1.0),
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)
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ax.set_xlabel("Episode workers $N$", fontsize=11)
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ax.set_ylabel("Throughput (obs/s)", fontsize=11)
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ax.set_xlim(-2, 105)
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ax.set_ylim(0, 540)
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ax.tick_params(labelsize=10)
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legend_elements = [
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Line2D(
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[0],
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[0],
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color=C_DEMAND,
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marker="o",
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markersize=5,
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linewidth=1.4,
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markerfacecolor=C_DEMAND,
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label=r"Demand $\lambda(N)$",
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),
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Line2D([0], [0], color=C_SUPPLY, linestyle="--", linewidth=1.0, alpha=0.85, label=r"Supply $\mu(B)$ ceilings"),
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Line2D(
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[0], [0], color=C_OP, marker="D", markersize=6, linewidth=0, markeredgecolor="white", label="Operating point"
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),
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]
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ax.legend(handles=legend_elements, fontsize=9, loc="lower right", framealpha=0.95, edgecolor="#ddd")
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.set_axisbelow(True)
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plt.tight_layout()
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fig.savefig(".github/assets/demand_supply.png", dpi=200, bbox_inches="tight", facecolor="white")
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fig.savefig(".github/assets/demand_supply.svg", bbox_inches="tight", facecolor="white")
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print("Saved .github/assets/demand_supply.{png,svg}")

‎.github/assets/gen_speedup_chart.py‎

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"""Generate the speedup comparison bar chart for README.
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Adapted from the paper figure (scripts/gen_speedup_fig.py) for web display.
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Usage:
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uv run --with matplotlib python .github/assets/gen_speedup_chart.py
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Output:
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.github/assets/speedup_comparison.png
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.github/assets/speedup_comparison.svg
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"""
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import matplotlib.ticker as mticker
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import numpy as np
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plt.rcParams.update(
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{
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"figure.dpi": 200,
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"font.family": "sans-serif",
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"font.sans-serif": ["DejaVu Sans"],
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"axes.linewidth": 0.6,
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"axes.grid": True,
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"grid.alpha": 0.12,
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"grid.linewidth": 0.4,
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}
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)
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# --- Data (from paper Table / Section III-C) ---
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benchmarks = ["LIBERO", "CALVIN", "SimplerEnv"]
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sequential = [14, 8.6, 1.73] # hours
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parallel = [0.3, 0.55, 0.14] # hours
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speedup_labels = ["47\u00d7", "16\u00d7", "12\u00d7"]
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par_labels = ["18 min", "33 min", "8.5 min"]
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seq_labels = ["14 h", "8.6 h", "1.7 h"]
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x = np.arange(len(benchmarks))
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width = 0.34
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# --- Colors ---
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C_SEQ = "#1b5e5e"
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C_PAR = "#e24a8d"
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C_BADGE = "#da9679"
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fig, ax = plt.subplots(figsize=(7, 3))
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bars_seq = ax.bar(
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x - width / 2, sequential, width, color=C_SEQ, edgecolor="white", linewidth=0.5, label="Sequential", zorder=3
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)
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bars_par = ax.bar(
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x + width / 2, parallel, width, color=C_PAR, edgecolor="white", linewidth=0.5, label="Batch Parallel", zorder=3
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)
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# Sequential labels
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for bar, lbl in zip(bars_seq, seq_labels):
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y_pos = bar.get_height()
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if y_pos > 4:
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ax.text(
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bar.get_x() + bar.get_width() / 2,
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y_pos - 0.4,
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lbl,
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ha="center",
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va="top",
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fontsize=11,
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color="white",
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fontweight="bold",
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)
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else:
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ax.text(
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bar.get_x() + bar.get_width() / 2,
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y_pos + 0.15,
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lbl,
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ha="center",
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va="bottom",
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fontsize=9.5,
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color="#134a4a",
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fontweight="bold",
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)
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# Parallel labels
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for bar, lbl in zip(bars_par, par_labels):
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ax.text(
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bar.get_x() + bar.get_width() / 2,
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bar.get_height() + 0.25,
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lbl,
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ha="center",
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va="bottom",
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fontsize=9,
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color="#b83a70",
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fontweight="bold",
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)
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# Speedup badges
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badge_y = [sequential[0] + 0.6, sequential[1] + 0.6, 4.5]
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for i, s in enumerate(speedup_labels):
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ax.text(
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x[i],
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badge_y[i],
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s,
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ha="center",
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va="bottom",
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fontsize=13,
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fontweight="bold",
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color=C_BADGE,
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bbox=dict(boxstyle="round,pad=0.25", facecolor="#f5ddd0", edgecolor="#da9679", linewidth=0.8),
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)
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# Arrow from SimplerEnv badge to bar
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ax.annotate(
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"", xy=(x[2], sequential[2] + 0.3), xytext=(x[2], 4.4), arrowprops=dict(arrowstyle="->", color=C_BADGE, lw=0.9)
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)
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ax.set_ylabel("Wall-clock time (hours)", fontsize=11)
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ax.set_xticks(x)
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ax.set_xticklabels(benchmarks, fontsize=12)
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ax.set_ylim(0, 18)
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ax.yaxis.set_major_locator(mticker.MultipleLocator(4))
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ax.yaxis.grid(True, linestyle="-", alpha=0.12, zorder=0)
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ax.set_axisbelow(True)
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.tick_params(axis="both", labelsize=10)
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ax.legend(loc="upper right", fontsize=10, frameon=True, fancybox=False, edgecolor="#bbbbbb", framealpha=0.92)
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plt.tight_layout()
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fig.savefig(".github/assets/speedup_comparison.png", dpi=200, bbox_inches="tight", facecolor="white")
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fig.savefig(".github/assets/speedup_comparison.svg", bbox_inches="tight", facecolor="white")
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print("Saved .github/assets/speedup_comparison.{png,svg}")

‎.github/assets/speedup_comparison.png‎

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