File size: 5,169 Bytes
33962ff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | """Measure a CPU container; only creates/removes its own uniquely named container.
Reports CPU app startup/HTTP workload, not live inference. Requires Docker CLI.
"""
import concurrent.futures
import json
import os
import subprocess
import time
import uuid
name='visual-mentor-measure-'+uuid.uuid4().hex[:8]
image=os.environ.get('VM_RUNTIME_IMAGE','visual-mentor:validation')
def command(*args):
if args[0]=='docker':args=('docker','--host=unix:///var/run/docker.sock',*args[1:])
return subprocess.check_output(args,text=True).strip()
probe="import urllib.request,json; print(json.load(urllib.request.urlopen('http://127.0.0.1:7860/api/health',timeout=2))['status'])"
rss_function = """
def app_rss():
import glob
processes={}
for p in glob.glob('/proc/[0-9]*/status'):
try:
fields={line.split(':',1)[0]:line.split(':',1)[1].strip() for line in open(p) if ':' in line}
processes[int(fields['Pid'])]=(int(fields['PPid']),int(fields.get('VmRSS','0 kB').split()[0])*1024)
except (OSError,ValueError):pass
included={1}
for _ in range(10):
included.update(pid for pid,(parent,rss) in processes.items() if parent in included)
return sum(rss for pid,(parent,rss) in processes.items() if pid in included)
"""
resource = "import json,importlib.util\n" + rss_function + "\nprint(json.dumps({'rss_bytes':app_rss(),'cpu_usage_usec':int(dict(line.split() for line in open('/sys/fs/cgroup/cpu.stat'))['usage_usec']),'forbidden_packages':[m for m in ['torch','transformers','sentence_transformers','vllm'] if importlib.util.find_spec(m)]}))"
try:
start=time.monotonic()
command('docker','run','-d','--name',name,'--memory=16g','--cpus=2','--network=none',image)
for _ in range(100):
try:
p = subprocess.run(['docker','--host=unix:///var/run/docker.sock','exec',name,'python','-c',probe],text=True,capture_output=True)
if p.returncode == 0 and p.stdout.strip() == 'ok':break
except subprocess.CalledProcessError:pass
time.sleep(.1)
else:raise RuntimeError('container never became healthy')
ready=time.monotonic()-start
idle=json.loads(command('docker','exec',name,'python','-c',resource))
workload=rss_function+"""
import concurrent.futures,urllib.request,threading,time,json,subprocess,os
import numpy as np
latencies=[]
ffmpeg_started=time.monotonic()
media=subprocess.Popen(['ffmpeg','-hide_banner','-loglevel','error','-i','/app/web/.next/standalone/public/sample/video.mp4','-vf','scale=480:-2','-c:v','libx264','-threads','1','-preset','veryfast','-y','/tmp/benchmark.mp4'])
rng=np.random.default_rng(0);matrix=rng.normal(size=(10000,768)).astype('float32');matrix/=np.linalg.norm(matrix,axis=1,keepdims=True)
vector=matrix[0].copy()
def numpy_work():
for _ in range(50):
scores=matrix@vector
assert int(np.argmax(scores))==0
stop=threading.Event(); samples=[]
def monitor():
while not stop.is_set():
samples.append(app_rss());time.sleep(.025)
thread=threading.Thread(target=monitor);thread.start()
paths=['/live','/models','/','/search','/ask','/api/demo/videos','/api/demo/stats','/api/health','/api/openapi.json']
def request(i):
started=time.monotonic()
path=paths[i%len(paths)]
try:
with urllib.request.urlopen('http://127.0.0.1:7860'+path,timeout=15) as r:
assert r.status==200
r.read()
latencies.append((time.monotonic()-started)*1000)
except Exception as e:raise RuntimeError(path+' failed: '+str(e))
try:
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as pool:
bench=pool.submit(numpy_work)
list(pool.map(request,range(180)))
bench.result()
assert media.wait(timeout=120)==0
finally:
stop.set();thread.join()
print(json.dumps({'requests':180,'peak_process_rss_bytes':max(samples),'http_p50_ms':round(float(np.percentile(latencies,50)),2),'http_p95_ms':round(float(np.percentile(latencies,95)),2),'ffmpeg_and_http_elapsed_s':round(time.monotonic()-ffmpeg_started,3),'synthetic_matrix_shape':[10000,768],'matrix_searches':50}))
"""
workload_started=time.monotonic()
result=json.loads(command('docker','exec','-e','OPENBLAS_NUM_THREADS=1',name,'python','-c',workload))
after=json.loads(command('docker','exec',name,'python','-c',resource))
peak=int(command('docker','exec',name,'cat','/sys/fs/cgroup/memory.peak'))
output={'image_id':command('docker','inspect',name,'--format','{{.Image}}'),'image_bytes':int(command('docker','image','inspect',image,'--format','{{.Size}}')),'startup_seconds':round(ready,3),'idle_process_rss_bytes':idle['rss_bytes'],'post_workload_process_rss_bytes':after['rss_bytes'],'sampled_peak_process_rss_bytes':max(idle['rss_bytes'],after['rss_bytes'],result['peak_process_rss_bytes']),'cgroup_peak_bytes':peak,'workload_cpu_seconds':round((after['cpu_usage_usec']-idle['cpu_usage_usec'])/1000000,3),'workload_wall_seconds_including_probes':round(time.monotonic()-workload_started,3),'forbidden_packages':idle['forbidden_packages'],'workload':result,'limits':'2 CPUs / 16 GiB; no GPU; no providers configured'}
print(json.dumps(output,indent=2))
finally:
subprocess.run(['docker','--host=unix:///var/run/docker.sock','rm','-f',name],stdout=subprocess.DEVNULL,check=False)
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