jeanmidevacc

# Load the dependencies
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.firefox.firefox_binary import FirefoxBinary
from bs4 import BeautifulSoup as bs
from time import sleep

# Define the main url (where to log the location)
url_main_page = "https://turo.com/en-us?locale=en_US"

jeanmidevacc

# Load the dependencies
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.firefox.firefox_binary import FirefoxBinary
from bs4 import BeautifulSoup as bs
from time import sleep
import requests

# Url to scrap
url_toscrap = "https://turo.com/ca/en-us/car-rental/montreal-qc/ford/mustang/702436?searchId=OD83L624"

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import mlflow.sagemaker as mfs

# Define mlflow parameter
experimentid = 1
runid = "xxxxxxx"

# AWS setup
awsid = "xxxxxx"# id of the AWS user that will deploy the system
region "xxxxx" # AWS region to deploy the API
arn = f"arn:aws:iam::{awsid}:role/xxxxx" # Arn of the role that will be used to do the deployment on sagemaker

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import boto3
import json

# Name of the app that you defined during the deployment on sagemaker
app_name = "xxxxx"
# AWS region of the deployment of the app on sagemaker
region = "xxxxx"

# Function to collect data from the endpoint on sagemaker
def query_endpoint(input_json):

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from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score

import mlflow
import mlflow.sklearn

import numpy as np

# Launch the experiment on mlflow
experiment_name = "electricityconsumption-forecast"

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from PIL import Image

def build_mlimage(path, config_resize = (100,50), is_bw = True):
    # Access the image
    img = Image.open(path)
    
    # Resizing and conversion in black and white (if necessary)
    if is_bw:
        newimg = img.resize(config_resize, Image.ANTIALIAS).convert('L')
    else:

jeanmidevacc

"""
pipeline.py
Script to test the different decorator on the metaflow framework
"""

import random
from metaflow import FlowSpec, step, Parameter, conda, conda_base

@conda_base(disabled = False ,python="3.7.4", libraries={"pandas" : "0.25.2"})
class ExampleFlow(FlowSpec):

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informations = []
for i,run in enumerate(runs):
    
    if run.successful:
        # collect some details on the fisrt and last step of the flow
        step_start = Step(f"{flowname}/{run.id}/start")
        step_end = run.end_task
        
        # Collect the number of cards picked for the features computation
        nbr_cardsselected = step_start.task.data.limittopcards

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"""
Based on https://www.tensorflow.org/tutorials/images/cnn
"""

import pathlib
import tensorflow as tf

# Definition of the constant
BATCH_SIZE = 30
EPOCHS = 5

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import tensorflow as tf

model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),