cheeyeo

#image processing resources
from skimage.io import imread, imshow
from skimage.filters import gaussian, threshold_otsu
from skimage.feature import canny
from skimage.transform import probabilistic_hough_line, rotate

#testing 
import numpy as np
import os

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from urllib.parse import urlparse
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.providers.amazon.aws.operators.s3 import S3FileTransformOperator


def file_from_s3(prefix):
    """
    Fetches S3 processed file from remote to local /tmp
    to allow inference to run on it
    """

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from airflow.models.dag import DAG
from airflow.api.client.local_client import Client
from airflow.providers.amazon.aws.sensors.sqs import SqsSensor


def analyze_data(ti):
    airflow_client = Client(None, None)
    messages = ti.xcom_pull(task_ids="sqs_sensor", key="messages")
    for msg in messages:
        records = json.loads(msg['Body'])

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# Change these variables as necessary.
MAIN_PACKAGE_PATH := ./cmd/example
BINARY_NAME := example

# ==================================================================================== #
# HELPERS
# ==================================================================================== #

## help: print this help message
.PHONY: help

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// Interval time to check if runtime is disconnected
interval = 1000 * 60;

// Busy/Reconnect button top-right
reloadButton = document.querySelector('#connect > paper-button > span')

setInterval(() => {
    if (reloadButton.innerText == 'Reconnect') {
        reloadButton.click();
        console.log('Restarting');

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raise ClientError(operation_name='InvalidKeyPair.Duplicate', error_response={
    'Error': {
        'Code': 'Duplicate', 
        'Message': 'This is a custom message'
       }
     }
)

cheeyeo

To add a value to a secret we need to ensure it is base64 encoded without whitespace

E.g.

echo -n 'true' | base64 -w0

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# example multistage process docker build script from https://blog.phusion.nl/2016/08/31/efficiently-and-conveniently-building-ruby-and-node-js-application-docker-containers-for-production-2


set -e

export IMAGE_NAME=tinco/express-example
export RUN_BUILD="docker run -it --rm -v $PWD:/usr/src/app -w /usr/src/app node:6"
export TEST_COMMAND="./node_modules/mocha/bin/mocha"

function run_image() {

cheeyeo

# Keras tokenizer lacks serialization. Therefore I created the below to address this without changing the API.
# (Since I don't know how long it'll take for keras to support it)
# The Tokenizer __init__ should be modified to take the word_stats dictionary as a kwarg, 
# and a method added to the class to return the stats
# Expiermentally this works, but I am not sure of any nuances in the Tokenizer class.

def test_tokenizer():
	texts = ["It was the best of times, it was the worst of times, it was the age of wisdom",
      "it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, ",
      "it was the season of Light, it was the season of Darkness, it was the spring of hope, ",

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from keras.models import Sequential
from keras.layers import Dense
from keras.utils.io_utils import HDF5Matrix
import numpy as np

def create_dataset():
    import h5py
    X = np.random.randn(200,10).astype('float32')
    y = np.random.randint(0, 2, size=(200,1))
    f = h5py.File('test.h5', 'w')