Suppress matplot debug logs

This commit is contained in:
Colter Downing
2025-10-25 16:18:02 -07:00
parent 4d9bf2048c
commit bcecd6df40
6 changed files with 519 additions and 33 deletions
+15 -9
View File
@@ -26,7 +26,8 @@ from lib.data_types import (
LogAction,
ApiPayload_T,
JsonDataException,
RequestMetrics
RequestMetrics,
BenchmarkResult
)
VERSION = "0.1.0"
@@ -332,18 +333,23 @@ class Backend:
for run in range(1, self.benchmark_handler.benchmark_runs + 1):
start = time.time()
tasks = []
total_workload = 0
benchmark_requests = []
for _ in range(concurrent_requests):
for i in range(concurrent_requests):
payload = self.benchmark_handler.make_benchmark_payload()
total_workload += payload.count_workload()
tasks.append(
self.__call_api(handler=self.benchmark_handler, payload=payload)
workload = payload.count_workload()
task = self.__call_api(handler=self.benchmark_handler, payload=payload)
benchmark_requests.append(
BenchmarkResult(request_idx=i, workload=workload, task=task)
)
responses = await gather(*tasks)
responses = await gather(*[br.task for br in benchmark_requests])
for br, response in zip(benchmark_requests, responses):
br.response = response
total_workload = sum(br.workload for br in benchmark_requests if br.is_successful)
time_elapsed = time.time() - start
successful_responses = sum([1 for br in benchmark_requests if br.is_successful])
throughput = total_workload / time_elapsed
sum_throughput += throughput
@@ -357,7 +363,7 @@ class Backend:
f"Run: {run}, concurrent_requests: {concurrent_requests}",
f"Total workload: {total_workload}, time_elapsed: {time_elapsed}s",
f"Throughput: {throughput} workload/s",
f"Successful responses: {len([r for r in responses if r.status == 200])}",
f"Successful responses: {successful_responses}/{concurrent_requests}",
"#" * 60,
]
)
+12 -1
View File
@@ -3,7 +3,7 @@ import logging
from dataclasses import dataclass, field
from enum import Enum
from abc import ABC, abstractmethod
from typing import Dict, Any, Union, Tuple, Optional, Set, TypeVar, Generic, Type
from typing import Dict, Any, Union, Tuple, Optional, Set, TypeVar, Generic, Type, Awaitable
from aiohttp import web, ClientResponse
import inspect
@@ -206,6 +206,17 @@ class RequestMetrics:
status: str
success: bool = False
@dataclass
class BenchmarkResult:
request_idx: int
workload: float
task: Awaitable[ClientResponse]
response: Optional[ClientResponse] = None
@property
def is_successful(self) -> bool:
return self.response is not None and self.response.status == 200
@dataclass
class ModelMetrics:
"""Model specific metrics"""
+6 -6
View File
@@ -292,12 +292,12 @@ def test_load_cmd(
args = arg_parser.parse_args()
if hasattr(args, "comfy_model"):
os.environ["COMFY_MODEL"] = args.comfy_model
server_url = dict(
prod="https://run.vast.ai",
alpha="https://run-alpha.vast.ai",
candidate="https://run-candidate.vast.ai",
local="http://localhost:8080",
)[args.instance]
server_url = {
"prod": "https://run.vast.ai",
"alpha": "https://run-alpha.vast.ai",
"candidate": "https://run-candidate.vast.ai",
"local": "http://localhost:8080",
}.get(args.instance, "http://localhost:8080")
run_test(
num_requests=args.num_requests,
requests_per_second=args.requests_per_second,
+43 -5
View File
@@ -1,5 +1,6 @@
import logging
from typing import Any, Dict, Optional
import time
from typing import Any, Dict, Optional, Tuple
import requests
@@ -16,6 +17,38 @@ class Endpoint:
Utility class for handling endpoint operations.
"""
@staticmethod
def get_endpoint_info(
endpoint_name: str, account_api_key: str, instance: str
) -> Optional[Dict[str, Any]]:
headers = {"Authorization": f"Bearer {account_api_key}"}
url = f"{Endpoint.get_server_url(instance)}?autoscaler_instance={instance}"
# Retry a few times to smooth over transient propagation/network delays
for attempt in range(4):
try:
response = requests.get(url, headers=headers, timeout=8)
if response.status_code != 200:
# brief backoff and retry
time.sleep(0.3 * (attempt + 1))
continue
try:
data = response.json()
except Exception:
# JSON parse failed; backoff and retry
time.sleep(0.3 * (attempt + 1))
continue
result = data.get("results", []) if isinstance(data, dict) else []
endpoint = next(
(item for item in result if item.get("endpoint_name") == endpoint_name),
None,
)
if endpoint and endpoint.get("id") and endpoint.get("api_key"):
return {"id": endpoint.get("id"), "api_key": endpoint.get("api_key")}
except Exception:
# network or other transient error; retry
time.sleep(0.3 * (attempt + 1))
return None
@staticmethod
def get_autoscaler_server_url(instance: str) -> str:
endpoints = {
@@ -23,7 +56,10 @@ class Endpoint:
"candidate": "run-candidate",
"prod": "run",
}
return f"https://{endpoints[instance]}.vast.ai/"
host = endpoints.get(instance)
if host:
return f"https://{host}.vast.ai/"
return "http://localhost:8080"
@staticmethod
def get_server_url(instance: str) -> str:
@@ -32,7 +68,8 @@ class Endpoint:
"candidate": "candidate",
"prod": "console",
}
return f"https://{endpoints[instance]}.vast.ai/api/v0/endptjobs/"
host = endpoints.get(instance, "alpha")
return f"https://{host}.vast.ai/api/v0/endptjobs/"
@staticmethod
def get_endpoint_api_key(
@@ -55,6 +92,7 @@ class Endpoint:
response = requests.get(
f"{Endpoint.get_server_url(instance)}?autoscaler_instance={instance}",
headers=headers,
timeout=8,
)
if response.status_code != 200:
@@ -64,14 +102,14 @@ class Endpoint:
try:
data = response.json()
except requests.exceptions.JSONDecodeError as e:
except Exception as e:
log.debug(f"Failed to parse JSON response: {e}")
return None
result = data.get("results", [])
endpoint: Optional[Dict[str, Any]] = next(
(item for item in result if item["endpoint_name"] == endpoint_name),
(item for item in result if item.get("endpoint_name") == endpoint_name),
None,
)
if not endpoint:
+29 -4
View File
@@ -119,14 +119,25 @@ class GenericHandler(EndpointHandler[GenericData], ABC):
class CompletionsData(GenericData):
@classmethod
def for_test(cls) -> "CompletionsData":
prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
system_prompt = """You are a helpful AI assistant. You have access to the following knowledge base:
Zebras (US: /ˈziːbrəz/, UK: /ˈzɛbrəz, ˈziː-/)[2] (subgenus Hippotigris) are African equines
with distinctive black-and-white striped coats. There are three living species: Grévy's zebra
(Equus grevyi), the plains zebra (E. quagga), and the mountain zebra (E. zebra). Zebras share the
genus Equus with horses and asses, the three groups being the only living members of the family
Equidae. Zebra stripes come in different patterns, unique to each individual. Zebras inhabit eastern
and southern Africa and can be found in a variety of habitats such as savannahs, grasslands,
woodlands, shrublands, and mountainous areas.
Please answer the following question based on the above context."""
unique_question = " ".join(random.choices(WORD_LIST, k=int(100)))
model = os.environ.get("MODEL_NAME")
if not model:
raise ValueError("MODEL_NAME environment variable not set")
test_input = {
"model": model,
"prompt": prompt,
"prompt": f"{system_prompt}\n\n{unique_question}",
"temperature": 0.7,
"max_tokens": 500,
}
@@ -153,7 +164,18 @@ class ChatCompletionsData(GenericData):
@classmethod
def for_test(cls) -> "ChatCompletionsData":
prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
system_prompt = """You are a helpful AI assistant. You have access to the following knowledge base:
Zebras (US: /ˈziːbrəz/, UK: /ˈzɛbrəz, ˈziː-/)[2] (subgenus Hippotigris) are African equines
with distinctive black-and-white striped coats. There are three living species: Grévy's zebra
(Equus grevyi), the plains zebra (E. quagga), and the mountain zebra (E. zebra). Zebras share the
genus Equus with horses and asses, the three groups being the only living members of the family
Equidae. Zebra stripes come in different patterns, unique to each individual. Zebras inhabit eastern
and southern Africa and can be found in a variety of habitats such as savannahs, grasslands,
woodlands, shrublands, and mountainous areas.
Please answer the following question based on the above context."""
unique_question = " ".join(random.choices(WORD_LIST, k=int(100)))
model = os.environ.get("MODEL_NAME")
if not model:
raise ValueError("MODEL_NAME environment variable not set")
@@ -161,7 +183,10 @@ class ChatCompletionsData(GenericData):
# Chat completions use messages format instead of prompt
test_input = {
"model": model,
"messages": [{"role": "user", "content": prompt}],
"messages": [
{"role": "system", "content": system_prompt}, # Shared prefix
{"role": "user", "content": unique_question} # Unique per request
],
"temperature": 0.7,
"max_tokens": 500,
}
+414 -8
View File
@@ -1,8 +1,395 @@
from lib.test_utils import test_load_cmd, test_args
from lib.test_utils import test_args
from utils.endpoint_util import Endpoint
from utils.ssl import get_cert_file_path
from lib.data_types import AuthData
from .data_types.server import CompletionsData
import os
WORKER_ENDPOINT = "/v1/completions"
import os
import time
import threading
import requests
from dataclasses import dataclass
from collections import Counter
from urllib.parse import urljoin, urlparse
import re
# Headless plotting
import matplotlib
matplotlib.use("Agg")
import logging
logging.getLogger("matplotlib.font_manager").setLevel(logging.WARNING)
import matplotlib.pyplot as plt
import numpy as np
from concurrent.futures import ThreadPoolExecutor, wait, FIRST_COMPLETED
from requests.adapters import HTTPAdapter
def get_incremented_path(path: str) -> str:
base, ext = os.path.splitext(path)
if not os.path.exists(path):
return path
i = 1
while os.path.exists(f"{base}-{i}{ext}"):
i += 1
return f"{base}-{i}{ext}"
WORKER_ENDPOINT = "/v1/completions" # This will return the full text output at once. Latency metrics reflect that (ie not measuring TTFT)
@dataclass
class ReqResult:
worker_url: str
route_ms: float
worker_ms: float
total_ms: float
ok: bool
error: str = ""
status_code: int = 0
t_start: float = 0.0
t_end: float = 0.0
workload: float = 0.0
def do_one(endpoint_name: str,
endpoint_id: int,
endpoint_api_key: str,
server_url: str,
worker_endpoint: str,
payload,
results_list,
t0,
status_samples,
route_session,
worker_session):
try:
workload = payload.count_workload()
route_payload = {"endpoint": endpoint_name, "api_key": endpoint_api_key, "cost": workload}
headers = {"Authorization": f"Bearer {endpoint_api_key}"}
start = time.time()
r0 = route_session.post(urljoin(server_url, "/route/"), json=route_payload, headers=headers, timeout=4)
t_after_route = time.time()
if r0.status_code != 200:
results_list.append(ReqResult(worker_url="",
route_ms=(t_after_route - start) * 1000.0,
worker_ms=0.0,
total_ms=(t_after_route - start) * 1000.0,
ok=False,
error=f"route error {r0.reason} {r0.text}",
status_code=r0.status_code,
t_start=start - t0,
t_end=t_after_route - t0,
workload=workload))
return
msg = r0.json()
# 1) Check if we got a worker back from route
worker_url = msg.get("url", "")
if not worker_url:
status = msg.get("status", "")
m = re.search(r"total workers:\s*(\d+).*loading workers:\s*(\d+).*standby workers:\s*(\d+).*error workers:\s*(\d+)", status, re.I | re.S)
if m:
tot, loading, standby, err = map(int, m.groups())
idle = max(tot - loading - standby - err, 0)
status_samples.append((time.time() - t0, idle))
# 2) If we got a worker, send the request
if worker_url:
req = dict(payload=payload.__dict__, auth_data=AuthData.from_json_msg(msg).__dict__)
t_before_worker = time.time()
r1 = worker_session.post(
urljoin(worker_url, worker_endpoint),
json=req,
verify=get_cert_file_path(),
timeout=(4, 120),
)
t_after_worker = time.time()
if r1.status_code != 200:
results_list.append(ReqResult(worker_url=worker_url,
route_ms=(t_after_route - start) * 1000.0,
worker_ms=(t_after_worker - t_before_worker) * 1000.0,
total_ms=(t_after_worker - start) * 1000.0,
ok=False,
error=f"worker inference error {r1.reason} {r1.text}",
status_code=r1.status_code,
t_start=start - t0,
t_end=t_after_worker - t0,
workload=workload))
return
# Success case
results_list.append(ReqResult(worker_url=worker_url,
route_ms=(t_after_route - start) * 1000.0,
worker_ms=(t_after_worker - t_before_worker) * 1000.0,
total_ms=(t_after_worker - start) * 1000.0,
ok=True,
error="",
status_code=200,
t_start=start - t0,
t_end=t_after_worker - t0,
workload=workload))
# 3) If so, sample via /get_endpoint_workers/ for eligible (idle) worker tracking
if worker_url:
try:
r_status = route_session.post(
urljoin(server_url, "/get_endpoint_workers/"),
json={"id": endpoint_id},
headers={"Authorization": f"Bearer {endpoint_api_key}"},
timeout=3,
)
if r_status.status_code == 200:
workers = r_status.json()
idle = 0
for w in workers:
st = str(w.get("status", "")).lower()
if (st in ("idle")):
idle += 1
status_samples.append((time.time() - t0, idle))
except Exception:
pass
except Exception as e:
t = time.time()
results_list.append(ReqResult(worker_url="",
route_ms=0.0,
worker_ms=0.0,
total_ms=0.0,
ok=False,
error=f"unknown error {e}",
status_code=0,
t_start=t - t0,
t_end=t - t0,
workload=0.0))
def run_load_with_metrics(num_requests: int,
requests_per_second: float,
endpoint_group_name: str,
account_api_key: str,
server_url: str,
worker_endpoint: str,
instance: str,
out_path: str):
ep_info = Endpoint.get_endpoint_info(endpoint_name=endpoint_group_name,
account_api_key=account_api_key,
instance=instance)
if not ep_info or not ep_info.get("api_key") or not ep_info.get("id"):
print(f"Endpoint {endpoint_group_name} not found for API key")
return
endpoint_id = int(ep_info["id"])
endpoint_api_key = ep_info["api_key"]
t0 = time.time()
results = []
status_samples = []
max_concurrency = int(os.environ.get("MAX_CONCURRENCY", "8192"))
submit_queue_factor = 2 # cap queued tasks to reduce memory
# Shared HTTP sessions with connection pooling (persistent connections)
def make_session(pool_connections: int, pool_maxsize: int) -> requests.Session:
sess = requests.Session()
adapter = HTTPAdapter(pool_connections=pool_connections, pool_maxsize=pool_maxsize, max_retries=0)
sess.mount("https://", adapter)
sess.mount("http://", adapter)
return sess
# Router: mostly single host, small connection pool is sufficient
route_session = make_session(pool_connections=1, pool_maxsize=max_concurrency)
# Workers: many hosts; allow many pools and per-host concurrency up to max_concurrency
worker_session = make_session(pool_connections=64, pool_maxsize=max_concurrency // 8)
# Fire requests using a thread pool, scheduling at requested RPS
inflight = set()
with ThreadPoolExecutor(max_workers=max_concurrency) as executor:
for i in range(num_requests):
# Pace submissions to RPS
target_time = t0 + i / max(requests_per_second, 1e-9)
sleep_s = target_time - time.time()
if sleep_s > 0:
time.sleep(min(sleep_s, 0.5)) # sleep in chunks to stay responsive
payload = CompletionsData.for_test()
fut = executor.submit(
do_one,
endpoint_group_name,
endpoint_id,
endpoint_api_key,
server_url,
worker_endpoint,
payload,
results,
t0,
status_samples,
route_session,
worker_session,
)
inflight.add(fut)
# Prevent unbounded queue growth
if len(inflight) >= max_concurrency * submit_queue_factor:
done, not_done = wait(inflight, return_when=FIRST_COMPLETED)
inflight = not_done
# Wait for all outstanding tasks
if inflight:
wait(inflight)
# Close sessions
try:
route_session.close()
finally:
worker_session.close()
# Aggregate results
oks = [r for r in results if r.ok]
errs = [r for r in results if not r.ok]
total_reqs = len(results)
succ = len(oks)
total_ms = np.array([r.total_ms for r in oks]) if succ else np.array([])
worker_ms = np.array([r.worker_ms for r in oks]) if succ else np.array([])
route_ms = np.array([r.route_ms for r in oks]) if succ else np.array([])
avg_total = float(np.mean(total_ms)) if succ else 0.0
avg_worker = float(np.mean(worker_ms)) if succ else 0.0
avg_route = float(np.mean(route_ms)) if succ else 0.0
p50_total, p95_total = (float(np.percentile(total_ms, 50)), float(np.percentile(total_ms, 95))) if succ else (0.0, 0.0)
# Distribution over workers (by host:port)
hosts = [urlparse(r.worker_url).netloc for r in oks if r.worker_url]
dist = Counter(hosts)
# Idle over time (mode per second)
idle_ts, idle_vals = [], []
if status_samples:
buckets = {}
for ts, idle in status_samples:
k = int(ts)
buckets.setdefault(k, []).append(idle)
keys = sorted(buckets.keys())
idle_ts = keys
# Use the most frequent sampled value per second (mode) to keep integer counts
idle_vals = []
for k in keys:
vals_k = [int(v) for v in buckets[k]]
if vals_k:
cnt = Counter(vals_k)
idle_vals.append(cnt.most_common(1)[0][0])
else:
idle_vals.append(0)
print(f"\nResults: total={total_reqs} success={succ} errors={len(errs)}")
print(f"Avg latency (ms): {avg_total:.1f} p50: {p50_total:.1f} p95: {p95_total:.1f}")
print(f"Avg route latency (ms): {avg_route:.1f} Avg worker latency (ms): {avg_worker:.1f}")
if errs:
print("Sample errors:")
for e in errs[:5]:
print(f" {e.status_code} {e.error}")
# Plot: 2x3 grid
fig, axes = plt.subplots(2, 3, figsize=(15, 8))
fig.suptitle(f"Load test: {endpoint_group_name} n={total_reqs}, rps={requests_per_second}, success={succ}")
# Dist per worker
ax0 = axes[0, 0]
if dist:
items = sorted(dist.items(), key=lambda kv: kv[1], reverse=True)
labels, counts = zip(*items)
ax0.bar(range(len(labels)), counts)
ax0.set_xticks(range(len(labels)))
ax0.set_xticklabels(labels, rotation=45, ha="right", fontsize=8)
ax0.set_title("Request distribution over workers")
ax0.set_ylabel("count")
# Latency histogram (total)
ax1 = axes[0, 1]
if succ:
ax1.hist(total_ms, bins=30)
ax1.set_title("Total latency (ms)")
ax1.set_xlabel("ms")
ax1.set_ylabel("freq")
# Eligible workers over time
ax_idle = axes[0, 2]
if idle_ts:
ax_idle.plot(idle_ts, idle_vals, "-o", ms=3)
ax_idle.set_title("Eligible workers over time")
ax_idle.set_xlabel("time (s)")
ax_idle.set_ylabel("eligible count")
# Throughput over time (completions/sec)
ax_idle = axes[1, 0]
ax_idle.clear()
if succ:
per_sec = {}
for r in oks:
s = int(r.t_end)
per_sec[s] = per_sec.get(s, 0) + 1
ts = sorted(per_sec.keys())
vals = [per_sec[t] for t in ts]
ax_idle.plot(ts, vals, "-o", ms=3)
ax_idle.set_title("Completions per second")
ax_idle.set_xlabel("time (s)")
ax_idle.set_ylabel("completions / sec")
# Summary text
ax3 = axes[1, 1]
ax3.axis("off")
text = (
f"Total requests: {total_reqs}\n"
f"Success: {succ} Errors: {len(errs)}\n"
f"Avg total latency: {avg_total:.1f} ms\n"
f"p50: {p50_total:.1f} ms p95: {p95_total:.1f} ms\n"
f"Avg route latency: {avg_route:.1f} ms\n"
f"Avg worker latency: {avg_worker:.1f} ms\n"
f"300 errors: {len([r for r in errs if r.status_code >= 300 and r.status_code < 400])}\n"
f"429 errors: {len([r for r in errs if r.status_code == 429])}\n"
f"500 errors: {len([r for r in errs if r.status_code >= 500])}\n"
f"Other errors: {len([r for r in errs if r.status_code not in [300, 429, 500]])}\n"
)
ax3.set_title("Summary")
ax3.text(0.02, 0.98, text, va="top", ha="left", fontsize=11, transform=ax3.transAxes)
# Error count over time
ax_errors = axes[1, 2]
all_end_times = [int(r.t_end) for r in results if r.t_end > 0]
if all_end_times:
min_second = min(all_end_times)
max_second = max(all_end_times)
# Count errors per second
errors_per_second = {}
for result in errs:
second = int(result.t_end)
errors_per_second[second] = errors_per_second.get(second, 0) + 1
# Create complete timeline including zeros
time_seconds = list(range(min_second, max_second + 1))
error_counts = [errors_per_second.get(sec, 0) for sec in time_seconds]
ax_errors.plot(time_seconds, error_counts, "-o", ms=3)
ax_errors.set_title("Errors per second")
ax_errors.set_xlabel("time (s)")
ax_errors.set_ylabel("errors / sec")
# Ensure unique output path and create directory if needed
final_out_path = get_incremented_path(out_path)
out_dir = os.path.dirname(final_out_path)
if out_dir:
os.makedirs(out_dir, exist_ok=True)
plt.tight_layout(rect=[0, 0, 1, 0.96])
plt.savefig(final_out_path, dpi=120)
print(f"Saved report to: {final_out_path}")
# Per-worker latency boxplot (top 12 by volume)
groups = {}
for r in oks:
host = urlparse(r.worker_url).netloc
groups.setdefault(host, []).append(r.total_ms)
items = sorted(groups.items(), key=lambda kv: len(kv[1]), reverse=True)[:12]
if items:
labels, data = zip(*items)
fig2, axb = plt.subplots(1, 1, figsize=(12, 5))
axb.boxplot(data, showfliers=False)
axb.set_xticklabels(labels, rotation=45, ha="right", fontsize=8)
axb.set_title("Per-worker latency (ms)")
axb.set_ylabel("ms")
plt.tight_layout()
extra_out = get_incremented_path(os.path.splitext(out_path)[0] + "-workers.png")
plt.savefig(extra_out, dpi=120)
fig2.tight_layout()
fig2.savefig(extra_out, dpi=120)
print(f"Saved worker latency plot to: {extra_out}")
if __name__ == "__main__":
# Check if MODEL_NAME environment variable is set
@@ -16,13 +403,32 @@ if __name__ == "__main__":
help="Model to use for completions request (required if MODEL_NAME env var not set)",
)
# Parse known args to get model early, before test_load_cmd adds its args
# Parse known args to get model early, before adding load args
known_args, _ = test_args.parse_known_args()
# Set environment variable if model was provided
if hasattr(known_args, "model") and known_args.model:
os.environ["MODEL_NAME"] = known_args.model
print(f"Set MODEL_NAME environment variable to: {known_args.model}")
# Now call test_load_cmd normally - it will add its own args and re-parse
test_load_cmd(CompletionsData, WORKER_ENDPOINT, arg_parser=test_args)
# Load test args
test_args.add_argument("-n", dest="num_requests", type=int, required=True, help="total number of requests")
test_args.add_argument("-rps", dest="requests_per_second", type=float, required=True, help="requests per second")
test_args.add_argument("--out", dest="out_path", type=str, default="load_test_report.png", help="path to save the report image")
args = test_args.parse_args()
server_url = {
"prod": "https://run.vast.ai",
"alpha": "https://run-alpha.vast.ai",
"candidate": "https://run-candidate.vast.ai",
"local": "http://localhost:8080"
}.get(args.instance, "http://localhost:8080")
run_load_with_metrics(
num_requests=args.num_requests,
requests_per_second=args.requests_per_second,
endpoint_group_name=args.endpoint_group_name,
account_api_key=args.api_key,
server_url=server_url,
worker_endpoint=WORKER_ENDPOINT,
instance=args.instance,
out_path=args.out_path,
)