AI Model Training Resources Stream

import matplotlib.pyplot as plt
import numpy as np

COLORS = {
    'background': '#0a0a0f',
    'text': '#ffffff',
    'grid': '#333333',
}

np.random.seed(1010)
epochs = np.arange(0, 100)

# Training resources
gpu_compute = 60 + 20 * (1 - np.exp(-epochs / 20)) + np.random.normal(0, 5, 100)
memory = 30 + 10 * np.log1p(epochs) + np.random.normal(0, 3, 100)
io_bandwidth = 20 + 10 * np.sin(epochs * np.pi / 25) + np.random.normal(0, 3, 100)
cpu_util = 15 + 5 * np.cos(epochs * np.pi / 30) + np.random.normal(0, 2, 100)
network = 10 + 8 * (epochs > 50) + np.random.normal(0, 2, 100)

data = [np.clip(d, 1, None) for d in [gpu_compute, memory, io_bandwidth, cpu_util, network]]
resource_colors = ['#6CF527', '#27D3F5', '#F5276C', '#F5B027', '#4927F5']

fig, ax = plt.subplots(figsize=(14, 6), facecolor=COLORS['background'])
ax.set_facecolor(COLORS['background'])

ax.stackplot(epochs, *data, colors=resource_colors, alpha=0.85, baseline='sym',
             labels=['GPU Compute', 'Memory', 'I/O Bandwidth', 'CPU', 'Network'])

ax.axhline(0, color=COLORS['grid'], linewidth=0.5, alpha=0.5)
ax.set_xlim(0, 99)

ax.set_title('AI Training Resource Utilization', color=COLORS['text'], fontsize=14, fontweight='bold', pad=15)
ax.set_xlabel('Training Epoch', color=COLORS['text'], fontsize=11)
ax.set_ylabel('Resource Usage (%)', color=COLORS['text'], fontsize=11)

ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.12), frameon=False, labelcolor=COLORS['text'], fontsize=9, ncol=5)

for spine in ax.spines.values():
    spine.set_visible(False)
ax.tick_params(colors=COLORS['text'], labelsize=9)

plt.tight_layout()
plt.subplots_adjust(bottom=0.18)
plt.show()