rtindru

%%writefile sentiment_analysis_service.py
# Now let's modify our service to use prometheus to measure custom metrics

import bentoml

from bentoml.frameworks.sklearn import SklearnModelArtifact
from bentoml.service.artifacts.common import PickleArtifact
from bentoml.adapters import JsonInput

import sentry_sdk

rtindru

%%writefile sentiment_analysis_service.py
# Now let's modify our service to use prometheus to measure latency

import bentoml

from bentoml.frameworks.sklearn import SklearnModelArtifact
from bentoml.service.artifacts.common import PickleArtifact
from bentoml.adapters import JsonInput

import sentry_sdk

rtindru

%%writefile sentiment_analysis_service.py
# Now let's modify our service to use sentry

import bentoml

from bentoml.frameworks.sklearn import SklearnModelArtifact
from bentoml.service.artifacts.common import PickleArtifact
from bentoml.adapters import JsonInput

# Edit 1: Import sentry

rtindru

# 1) import the custom BentoService defined above
from sentiment_analysis_service import SKSentimentAnalysis

# 2) `pack` it with required artifacts, i.e. the trained model from step 1
bento_service = SKSentimentAnalysis()
bento_service.pack('model', sentiment_lr)

# 3) save your BentoSerivce to file archive
saved_path = bento_service.save()

rtindru

%%writefile sentiment_analysis_service.py

import bentoml

from bentoml.frameworks.sklearn import SklearnModelArtifact
from bentoml.service.artifacts.common import PickleArtifact
from bentoml.adapters import JsonInput

@bentoml.artifacts([PickleArtifact('model')])  # picke your trained model so that it can run on the server
@bentoml.env(pip_packages=["scikit-learn", "pandas"])  # specify the packages that your model depends on

rtindru

import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, roc_auc_score, roc_curve
from sklearn.pipeline import Pipeline


%%bash

rtindru

from scipy import stats

"""
Imagining a programming language where basic types are represented as a Gaussian distribution. Would make for some pretty interesting (read: hair-pulling) programming experience, lol.
. The default assumptions in Deep Learning these days. But in a way, this may be how we should model the world around us. There are no statics or constants, everything is a probability distribution function.
"""

class GaussianFloat(object):

    def __init__(self, val, scale=None):

rtindru

BOARD = dict()
EMPTY_CHAR = "*"
WIN_CHAR = "@"

# Store all possible win combinations
WIN_COMBOS = [
    (1, 2, 3), 
    (4, 5, 6),
    (7, 8, 9),
    (1, 4, 7),