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performance.py
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205 lines (175 loc) · 5.78 KB
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from e2b_code_interpreter import Sandbox
import time
import os
import statistics
import matplotlib.pyplot as plt
iterations_count = int(os.getenv("E2B_TESTS_BENCHMARK_ITERATIONS_COUNT", 20))
template = os.getenv("E2B_TESTS_TEMPLATE", "code-interpreter-v1")
# Lists to store metrics for each iteration
sandbox_creation_times = []
health_check_times = []
first_code_run_times = []
second_code_run_times = []
for i in range(iterations_count):
print(f"\n--- Iteration {i + 1}/{iterations_count} ---")
start_time = time.time()
sbx = Sandbox.create(template=template)
end_time = time.time()
sandbox_creation_time = (end_time - start_time) * 1000
sandbox_creation_times.append(sandbox_creation_time)
print(f"Sandbox creation time: {sandbox_creation_time:.2f} milliseconds")
start_time = time.time()
sbx.commands.run("curl http://0.0.0.0:49999/health")
end_time = time.time()
health_check_time = (end_time - start_time) * 1000
health_check_times.append(health_check_time)
print(f"Health check time: {health_check_time:.2f} milliseconds")
start_time = time.time()
sbx.run_code("print('Hello, world!')")
end_time = time.time()
first_code_run_time = (end_time - start_time) * 1000
first_code_run_times.append(first_code_run_time)
print(f"First code run time: {first_code_run_time:.2f} milliseconds")
start_time = time.time()
sbx.run_code("print('Hello, world!')")
end_time = time.time()
second_code_run_time = (end_time - start_time) * 1000
second_code_run_times.append(second_code_run_time)
print(f"Second code run time: {second_code_run_time:.2f} milliseconds")
sbx.kill()
# Calculate and print summary statistics
def print_metric_summary(metric_name, times):
if not times:
return
low = min(times)
high = max(times)
mean = statistics.mean(times)
median = statistics.median(times)
print(f"\n{metric_name} Summary:")
print(f" Low: {low:.2f} ms")
print(f" High: {high:.2f} ms")
print(f" Mean: {mean:.2f} ms")
print(f" Median: {median:.2f} ms")
print("\n" + "=" * 50)
print("PERFORMANCE SUMMARY")
print("=" * 50)
print_metric_summary("Sandbox Creation Time", sandbox_creation_times)
print_metric_summary("Health Check Time", health_check_times)
print_metric_summary("First Code Run Time", first_code_run_times)
print_metric_summary("Second Code Run Time", second_code_run_times)
def create_performance_plot(
template,
iterations_count,
sandbox_creation_times,
health_check_times,
first_code_run_times,
second_code_run_times,
):
"""Create and save a performance visualization plot."""
print("\nGenerating performance plot...")
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 10))
# Plot 1: All metrics over iterations
iterations = list(range(1, iterations_count + 1))
ax1.plot(
iterations,
sandbox_creation_times,
"b-o",
label="Sandbox Creation",
linewidth=1.5,
markersize=6,
markerfacecolor="blue",
markeredgecolor="darkblue",
markeredgewidth=1,
)
ax1.plot(
iterations,
health_check_times,
"g-s",
label="Health Check",
linewidth=1.5,
markersize=6,
markerfacecolor="green",
markeredgecolor="darkgreen",
markeredgewidth=1,
)
ax1.plot(
iterations,
first_code_run_times,
"r-^",
label="First Code Run",
linewidth=1.5,
markersize=6,
markerfacecolor="red",
markeredgecolor="darkred",
markeredgewidth=1,
)
ax1.plot(
iterations,
second_code_run_times,
"m-d",
label="Second Code Run",
linewidth=1.5,
markersize=6,
markerfacecolor="magenta",
markeredgecolor="darkmagenta",
markeredgewidth=1,
)
ax1.set_xlabel("Iteration")
ax1.set_ylabel("Time (ms)")
ax1.set_title(
f"Performance Metrics Over {iterations_count} Iterations - {template}"
)
ax1.legend()
ax1.grid(True, alpha=0.3)
# Set x-axis to show each iteration step
ax1.set_xticks(iterations)
ax1.set_xlim(0.5, iterations_count + 0.5)
# Plot 2: Box plot for distribution
all_metrics = [
sandbox_creation_times,
health_check_times,
first_code_run_times,
second_code_run_times,
]
metric_names = [
"Sandbox\nCreation",
"Health\nCheck",
"First Code\nRun",
"Second Code\nRun",
]
box_plot = ax2.boxplot(all_metrics, tick_labels=metric_names, patch_artist=True)
colors = ["lightblue", "lightgreen", "lightcoral", "plum"]
for patch, color in zip(box_plot["boxes"], colors):
patch.set_facecolor(color)
ax2.set_ylabel("Time (ms)")
ax2.set_title(f"Performance Distribution - {template}")
ax2.grid(True, alpha=0.3)
plt.tight_layout()
# Show summary statistics in the plot
stats_text = f"""Summary Statistics:
Sandbox Creation: {statistics.mean(sandbox_creation_times):.1f}ms avg
Health Check: {statistics.mean(health_check_times):.1f}ms avg
First Code Run: {statistics.mean(first_code_run_times):.1f}ms avg
Second Code Run: {statistics.mean(second_code_run_times):.1f}ms avg"""
fig.text(
0.02,
0.02,
stats_text,
fontsize=8,
verticalalignment="bottom",
bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.8),
)
# Save the plot
plot_filename = "performance_plot.png"
plt.savefig(plot_filename, dpi=300, bbox_inches="tight")
print(f"Performance plot saved as: {plot_filename}")
return plot_filename
# Create performance plot
create_performance_plot(
template,
iterations_count,
sandbox_creation_times,
health_check_times,
first_code_run_times,
second_code_run_times,
)