Kechikish, to'plam va ishchilar

import pandas as pd
show(pd.DataFrame([
    {"": "Nimani o'lchaydi", "Kechikish": "Bitta so'rov vaqti",
     "O'tkazuvchanlik": "Sekundiga so'rovlar"},
    {"": "Kim sezadi", "Kechikish": "Foydalanuvchi", "O'tkazuvchanlik": "Server hisobi"},
    {"": "Yaxshilash usuli", "Kechikish": "Tezroq model, kamroq ish",
     "O'tkazuvchanlik": "To'plam, ko'proq ishchi"},
    {"": "To'plam ta'siri", "Kechikish": "**Yomonlashadi**",
     "O'tkazuvchanlik": "**Yaxshilanadi**"},
]))

python3 -c "
import sys, pathlib; sys.path.insert(0, 'src')
if not pathlib.Path('model.onnx').exists():
    import pandas as pd
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    from quvur.xususiyat import XUSUSIYATLAR, tayyorla
    from quvur.format import onnx_ga
    d = pd.read_csv('../datasets/ijara.csv')
    x, y = tayyorla(d), d['broker']
    Xtr, _, ytr, _ = train_test_split(x, y, test_size=0.25, stratify=y, random_state=42)
    m = RandomForestClassifier(n_estimators=150, min_samples_leaf=3,
                               random_state=42, n_jobs=1).fit(Xtr, ytr)
    onnx_ga(m, len(XUSUSIYATLAR), 'model.onnx')
    print('model.onnx qayta yaratildi (28-bobdagidek)')
else:
    print('model.onnx bor')
"
docker rm -f kitob-servis > /dev/null 2>&1
if ! docker images mlops-servis:v1 -q | grep -q .; then
  docker build -q -f Dockerfile.servis -t mlops-servis:v1 . > /dev/null 2>&1
fi
docker run -d --name kitob-servis -p 18100:8000 mlops-servis:v1 > /dev/null 2>&1
for i in $(seq 1 40); do
  curl -s http://127.0.0.1:18100/healthz > /dev/null 2>&1 && { echo "servis tayyor (${i} urinish)"; break; }
  sleep 0.5
done
curl -s http://127.0.0.1:18100/ready; echo ""

python3 -c "
import json, time, urllib.request
import numpy as np

ELON = {'xonalar': 2, 'maydon': 55, 'qavat': 3, 'jami_qavat': 9, 'narx_mln': 4.5,
        'tavsif_uzunligi': 300, 'rasmlar_soni': 8, 'telefon_takrorlanishi': 4,
        'kunlik_elonlar': 2, 'egasi_tasdiqlangan': 0}
xom = json.dumps(ELON).encode()

def sorov():
    t0 = time.perf_counter()
    r = urllib.request.Request('http://127.0.0.1:18100/bashorat', data=xom,
                               headers={'Content-Type': 'application/json'})
    with urllib.request.urlopen(r, timeout=10) as j: j.read()
    return (time.perf_counter() - t0) * 1000

for _ in range(20): sorov()
v = np.array([sorov() for _ in range(300)])
print(f'BITTA FOYDALANUVCHI ({len(v)} sorov)')
print(f'  p50 : {np.percentile(v,50):6.2f} ms')
print(f'  p99 : {np.percentile(v,99):6.2f} ms')
print(f'  otkazuvchanlik: {1000/np.mean(v):6.0f} sorov/s')
"

python3 -c "
import json, time, urllib.request
from concurrent.futures import ThreadPoolExecutor
import numpy as np

ELON = {'xonalar': 2, 'maydon': 55, 'qavat': 3, 'jami_qavat': 9, 'narx_mln': 4.5,
        'tavsif_uzunligi': 300, 'rasmlar_soni': 8, 'telefon_takrorlanishi': 4,
        'kunlik_elonlar': 2, 'egasi_tasdiqlangan': 0}
xom = json.dumps(ELON).encode()

def sorov(_=None):
    t0 = time.perf_counter()
    r = urllib.request.Request('http://127.0.0.1:18100/bashorat', data=xom,
                               headers={'Content-Type': 'application/json'})
    with urllib.request.urlopen(r, timeout=30) as j: j.read()
    return (time.perf_counter() - t0) * 1000

print(f'{\"parallel\":>9s} {\"p50\":>9s} {\"p99\":>9s} {\"sorov/s\":>10s}')
print('-' * 42)
for n in (1, 10, 50):
    with ThreadPoolExecutor(max_workers=n) as ex:
        list(ex.map(sorov, range(n * 3)))          # isitish
        t0 = time.perf_counter()
        v = np.array(list(ex.map(sorov, range(n * 10))))
        dt = time.perf_counter() - t0
    print(f'{n:>9d} {np.percentile(v,50):>8.1f}ms {np.percentile(v,99):>8.1f}ms {len(v)/dt:>10.0f}')
"

"""To'plamli bashorat: bitta so'rovda ko'p e'lon."""
from typing import Any

import numpy as np
import pandas as pd

from quvur.format import onnx_ehtimol
from quvur.xususiyat import XOM_USTUNLAR, tayyorla


def toplam_ehtimol(sessiya, elonlar: list[dict[str, Any]]) -> list[float]:
    """Ro'yxatni BITTA marta modelga beradi.

    Diqqat: 3-bobdagi qoida — xususiyat kodi o'sha `tayyorla()`.
    Bu yerda yangi hisob yozilmaydi.
    """
    d = pd.DataFrame(elonlar, columns=XOM_USTUNLAR)
    x = tayyorla(d)
    return [float(p) for p in onnx_ehtimol(sessiya, x.values)]

python3 -c "
import sys, time; sys.path.insert(0, 'src')
import numpy as np
from quvur.format import onnx_sessiya, onnx_ehtimol
from quvur.xususiyat import XOM_USTUNLAR, tayyorla
from xizmat.toplam import toplam_ehtimol
import pandas as pd

s = onnx_sessiya('model.onnx')
d = pd.read_csv('../datasets/ijara.csv').head(500)
elonlar = d[XOM_USTUNLAR].to_dict('records')

# 1) bittalab
t0 = time.perf_counter()
for e in elonlar:
    x = tayyorla(pd.DataFrame([e], columns=XOM_USTUNLAR))
    onnx_ehtimol(s, x.values)
bitta = (time.perf_counter() - t0) * 1000

# 2) toplam bilan
t0 = time.perf_counter()
toplam_ehtimol(s, elonlar)
toplam = (time.perf_counter() - t0) * 1000

print(f'{len(elonlar)} ta elon:')
print(f'  bittalab : {bitta:8.1f} ms  ({bitta/len(elonlar):.3f} ms/elon)')
print(f'  toplam   : {toplam:8.1f} ms  ({toplam/len(elonlar):.3f} ms/elon)')
print(f'  toplam {bitta/toplam:.0f}x tez')
"

python3 -c "
import numpy as np

# oddiy model: toplam hajmi va kutish vaqti
BITTA_MS = 2.6        # bitta sorovni alohida bajarish
TOPLAM_ASOS = 1.5     # toplam uchun tayyorgarlik
TOPLAM_QATOR = 0.02   # har qoshimcha qator
KELISH_MS = 5.0       # sorovlar orasidagi ortacha vaqt

print(f'{\"toplam\":>8s} {\"kutish\":>9s} {\"hisob\":>9s} {\"JAMI p50\":>10s} {\"sorov/s\":>9s}')
print('-' * 50)
for n in (1, 4, 16, 64):
    kutish = (n - 1) * KELISH_MS / 2        # ortacha kutish
    hisob = TOPLAM_ASOS + n * TOPLAM_QATOR if n > 1 else BITTA_MS
    jami = kutish + hisob
    otkaz = n / (hisob / 1000)
    print(f'{n:>8d} {kutish:>8.1f}ms {hisob:>8.1f}ms {jami:>9.1f}ms {otkaz:>9.0f}')
"

docker rm -f kitob-servis4 > /dev/null 2>&1
docker run -d --name kitob-servis4 -p 18102:8000 mlops-servis:v1 \
  python3 -m uvicorn xizmat.servis:app --host 0.0.0.0 --port 8000 --workers 4 > /dev/null 2>&1
for i in $(seq 1 40); do curl -s http://127.0.0.1:18102/healthz > /dev/null 2>&1 && break; sleep 0.5; done

python3 -c "
import json, time, urllib.request
from concurrent.futures import ThreadPoolExecutor
import numpy as np

ELON = {'xonalar': 2, 'maydon': 55, 'qavat': 3, 'jami_qavat': 9, 'narx_mln': 4.5,
        'tavsif_uzunligi': 300, 'rasmlar_soni': 8, 'telefon_takrorlanishi': 4,
        'kunlik_elonlar': 2, 'egasi_tasdiqlangan': 0}
xom = json.dumps(ELON).encode()

def qil(port):
    def s(_=None):
        t0 = time.perf_counter()
        r = urllib.request.Request(f'http://127.0.0.1:{port}/bashorat', data=xom,
                                   headers={'Content-Type': 'application/json'})
        with urllib.request.urlopen(r, timeout=30) as j: j.read()
        return (time.perf_counter() - t0) * 1000
    return s

print(f'{\"ishchi\":>8s} {\"p50\":>9s} {\"p99\":>9s} {\"sorov/s\":>10s}')
print('-' * 40)
for nom, port in (('1', 18100), ('4', 18102)):
    f = qil(port)
    with ThreadPoolExecutor(max_workers=30) as ex:
        list(ex.map(f, range(60)))
        t0 = time.perf_counter()
        v = np.array(list(ex.map(f, range(300))))
        dt = time.perf_counter() - t0
    print(f'{nom:>8s} {np.percentile(v,50):>8.1f}ms {np.percentile(v,99):>8.1f}ms {len(v)/dt:>10.0f}')
"

docker rm -f kitob-servis kitob-servis4 > /dev/null 2>&1 && echo "konteynerlar o'chirildi"
docker ps --format '{{.Names}}' | grep -v k3d