UniDeploy Cloud — Managed Cloud Sandboxes & AI Model Deployment

runner.pyPYTHON

import matplotlib.pyplot as plt
import numpy as np

# UniDeploy Cloud Sandbox — Persistent Python Data Science Kernel
# Unlike Google Colab, your execution state never disconnects unexpectedly mid-analysis.

time_steps = np.linspace(0, 10, 100)
signal = np.sin(time_steps) * np.exp(-0.1 * time_steps)
noise = np.random.normal(0, 0.05, 100)
measured = signal + noise

plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(8, 4), dpi=130)

ax.plot(time_steps, signal, color='#22C55E', linewidth=2.5, label='Damped Wave (Ground Truth)')
ax.scatter(time_steps, measured, color='#60a5fa', s=16, alpha=0.7, label='Measured Data Points')

ax.set_title('UniDeploy Persistent Python Compute Engine', color='#f3f4f6', fontsize=12, pad=12, fontweight='bold')
ax.set_xlabel('Time (seconds)', color='#9ca3af', fontsize=10)
ax.set_ylabel('Amplitude', color='#9ca3af', fontsize=10)
ax.grid(True, linestyle='--', alpha=0.2)
ax.legend(facecolor='#161F16', edgecolor='#202E22')

fig.tight_layout()
plt.show()

print(f"Computed {len(time_steps)} steps with mean signal amplitude: {np.mean(signal):.4f}")
print("Status: Kernel session active and persistent across re-runs.")

2 vCPUs · 2GB RAM|

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Debian 13 Firecracker MicroVM

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Full Hardware Virtualization

Every execution runs in a dedicated Firecracker microVM with its own isolated kernel. Zero host disk exposure, zero cross-contamination.

Drop-In REST & Webhooks

Execute customer-submitted code in your SaaS, generate charts from Make/n8n, or evaluate code tests with a simple JSON call.

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Plug UniDeploy Sandbox directly into Claude Desktop or Cursor to allow AI to safely browse, run terminal commands, and verify code.

Cloud MicroVM Sandbox & Python Runner

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