kylegallatin

comparing metrics from 2023-09-20T15:56:11Z to 2023-09-20T16:06:11Z

[search-xkgallatin0revert-13428-rever, 923 series]
debug: http://prometheus.search-infra.etsy-searchinfra-gke-dev.etsycloud.com/api/v1/series?match%5B%5D=%7Bnamespace%3D%22search-xkgallatin0revert-13428-rever%22,service%3D%22mmx-recsys-service%22%7D&start=2023-09-20T15:56:11Z&end=2023-09-20T16:06:11Z

[search-xkgallatin1main, 913 series]
debug: http://prometheus.search-infra.etsy-searchinfra-gke-dev.etsycloud.com/api/v1/series?match%5B%5D=%7Bnamespace%3D%22search-xkgallatin1main%22,service%3D%22mmx-recsys-service%22%7D&start=2023-09-20T15:56:11Z&end=2023-09-20T16:06:11Z

command: git diff --no-index /tmp/search-xkgallatin0revert-13428-rever-14651925583344607341.tmp /tmp/search-xkgallatin1main-16647560124810188357.tmp

kylegallatin

comparing metrics from 2023-09-20T15:52:48Z to 2023-09-20T16:02:48Z

[search-xkgallatin0revert-13428-rever, 851 series]
debug: http://prometheus.search-infra.etsy-searchinfra-gke-dev.etsycloud.com/api/v1/series?match%5B%5D=%7Bnamespace%3D%22search-xkgallatin0revert-13428-rever%22,service%3D%22mmx-recsys-service%22%7D&start=2023-09-20T15:52:48Z&end=2023-09-20T16:02:48Z

[search-xkgallatin1main, 840 series]
debug: http://prometheus.search-infra.etsy-searchinfra-gke-dev.etsycloud.com/api/v1/series?match%5B%5D=%7Bnamespace%3D%22search-xkgallatin1main%22,service%3D%22mmx-recsys-service%22%7D&start=2023-09-20T15:52:48Z&end=2023-09-20T16:02:48Z

command: git diff --no-index /tmp/search-xkgallatin0revert-13428-rever-17960227474054244828.tmp /tmp/search-xkgallatin1main-6809842399054133810.tmp

kylegallatin

comparing metrics from 2023-09-20T15:12:57Z to 2023-09-20T15:22:57Z

[search-xkgallatin0revert-13428-rever, 883 series]
debug: http://prometheus.search-infra.etsy-searchinfra-gke-dev.etsycloud.com/api/v1/label/__name__/values?match%5B%5D=%7Bnamespace%3D%22search-xkgallatin0revert-13428-rever%22,service%3D%22mmx-recsys-service%22%7D&start=2023-09-20T15:12:57Z&end=2023-09-20T15:22:57Z

[search-xkgallatin1main, 883 series]
debug: http://prometheus.search-infra.etsy-searchinfra-gke-dev.etsycloud.com/api/v1/label/__name__/values?match%5B%5D=%7Bnamespace%3D%22search-xkgallatin1main%22,service%3D%22mmx-recsys-service%22%7D&start=2023-09-20T15:12:57Z&end=2023-09-20T15:22:57Z

command: git diff --no-index /tmp/search-xkgallatin0revert-13428-rever-2840922538639804395.tmp /tmp/search-xkgallatin1main-12456199614687710159.tmp

kylegallatin

import sys
import grpc 

def get_grpc_connection(host_port: str, secure: bool):
    if secure:
        credentials = grpc.ssl_channel_credentials()
        channel = grpc.secure_channel(host_port, credentials)
    else:
        channel = grpc.insecure_channel(host_port)
    return channel

kylegallatin

from river import linear_model, compose, preprocessing, metrics

# create logistic regression model with a standard scaler
model = compose.Pipeline(
    preprocessing.StandardScaler(),
    linear_model.LogisticRegression()
)

# use ROCAUC to evaluate our model
metric = metrics.ROCAUC()

kylegallatin

import pickle
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression

x, y = make_classification(n_samples=100, 
                           n_features=3, 
                           n_informative=2, 
                           n_classes=2, 
                           n_redundant=0, 
                           n_repeated=0,)

kylegallatin

import time

class PerformanceMonitor:
    
    def __init__(self):
        self.inference_times = []
    
    def record(self, prediction_time: float) -> None:
        self.inference_times.append(prediction_time)
    

kylegallatin

def load_latest_model() -> sklearn.pipeline.Pipeline:
    latest_model_path = pd.read_csv("metadata_store.csv").query("timestamp == timestamp.max()")["model_path"].values[0]
    loaded_model = joblib.load(latest_model_path)
    return loaded_model

def model_predict(user_id: str) -> float:
    model = load_latest_model()
    features = feature_store.get_user_feature(user_id)
    return model.predict([[v for k,v in features.items() if k != "target"]])

kylegallatin

class TestExample:
    
    def test_assertion(self):
        assert True==True

kylegallatin

import time
import joblib
import csv
from datetime import datetime
import pandas as pd
import logging
from typing import Iterable, Callable, Dict

import sklearn.pipeline
from sklearn.pipeline import make_pipeline