feat: initial commit with vLLM production stack and benchmark tools

2026-09-09 18:22:40 +02:00
commit 20ae3cfb48
10 changed files with 331 additions and 0 deletions
@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+"""
+Benchmark de Concurrencia para Servidores de Inferencia LLM
+Compara comportamiento ante ráfagas concurrentes de peticiones.
+"""
+
+import asyncio
+import time
+import aiohttp
+import statistics
+import argparse
+
+PROMPT = "Explica en tres párrafos técnicos qué es la memoria virtual y cómo se gestionan las páginas de memoria en el kernel de Linux."
+
+async def send_request(session, url, model, headers, req_id):
+    # Envia una peticion individual y mide su latencia
+    payload = {
+        "model": model,
+        "messages": [{"role": "user", "content": PROMPT}],
+        "max_tokens": 150,
+        "temperature": 0.7
+    }
+    
+    start_time = time.perf_counter()
+    try:
+        async with session.post(url, json=payload, headers=headers, timeout=aiohttp.ClientTimeout(total=60)) as resp:
+            data = await resp.json()
+            latency = time.perf_counter() - start_time
+            if resp.status == 200:
+                tokens = data["usage"]["completion_tokens"]
+                return {"id": req_id, "success": True, "latency": latency, "tokens": tokens}
+            else:
+                return {"id": req_id, "success": False, "latency": latency, "error": resp.status}
+    except Exception as e:
+        latency = time.perf_counter() - start_time
+        return {"id": req_id, "success": False, "latency": latency, "error": str(e)}
+
+async def run_benchmark(url, model, concurrency, auth_header):
+    # Ejecuta peticiones concurrentes y calcula metricas
+    headers = {"Content-Type": "application/json"}
+    if auth_header:
+        headers["Authorization"] = f"Bearer {auth_header}"
+
+    print("\n=======================================================")
+    print(f"Iniciando Benchmark: {concurrency} peticiones CONCURRENTES")
+    print(f"Target: {url} | Modelo: {model}")
+    print("=======================================================")
+
+    async with aiohttp.ClientSession() as session:
+        t0 = time.perf_counter()
+        tasks = [send_request(session, url, model, headers, i) for i in range(concurrency)]
+        results = await asyncio.gather(*tasks)
+        total_wall_time = time.perf_counter() - t0
+
+    successful = [r for r in results if r["success"]]
+    failed = [r for r in results if not r["success"]]
+
+    if successful:
+        latencies = [r["latency"] for r in successful]
+        total_tokens = sum(r["tokens"] for r in successful)
+        avg_latency = statistics.mean(latencies)
+        p95_latency = statistics.quantiles(latencies, n=20)[18] if len(latencies) >= 20 else max(latencies)
+        throughput_tokens_sec = total_tokens / total_wall_time
+
+        print("\nRESULTADOS:")
+        print(f" - Peticiones exitosas: {len(successful)}/{concurrency}")
+        print(f" - Fallidas / Timeout:  {len(failed)}")
+        print(f" - Tiempo total del test: {total_wall_time:.2f} s")
+        print(f" - Throughput global:     {throughput_tokens_sec:.2f} tokens/segundo")
+        print(f" - Latencia promedio:     {avg_latency:.2f} s")
+        print(f" - Latencia P95:          {p95_latency:.2f} s")
+    else:
+        print(f"\nTodas las peticiones fallaron. Errores: {[r.get('error') for r in failed]}")
+
+if __name__ == "__main__":
+    # Parser de argumentos por linea de comandos
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--url", default="http://localhost:4000/v1/chat/completions", help="Endpoint OpenAI-compatible")
+    parser.add_argument("--model", default="production-model", help="Nombre del modelo")
+    parser.add_argument("--concurrency", type=int, default=20, help="Numero de peticiones concurrentes")
+    parser.add_argument("--key", default="sk-production-admin-key", help="API Key si aplica")
+    args = parser.parse_args()
+
+    asyncio.run(run_benchmark(args.url, args.model, args.concurrency, args.key))