Igor Brigadir igorbrigadir

Setup Traefik 2.2 with Let's Encrypt On LKE

If you searched for this article I chances are you know what LKE, Traefik and Let's Encrypt are, else here is a quick primer:

What is LKE?

"The Linode Kubernetes Engine (LKE) is a fully-managed container orchestration engine for deploying and managing containerized applications and workloads.". For more information please have a look at "Deploy and manage a cluster with Linode Kubernetes Engine"

A criticism of "On the Dangers of Stochastic Parrots: Can Languae Models be Too Big"

Yoav Goldberg, Jan 23, 2021.

The FAccT paper "On the Dangers of Stochastic Parrots: Can Languae Models be Too Big" by Bender, Gebru, McMillan-Major and Shmitchell has been the center of a controversary recently. The final version is now out, and, owing a lot to this controversary, would undoubtly become very widely read. I read an earlier draft of the paper, and I think that the new and updated final version is much improved in many ways: kudos for the authors for this upgrade. I also agree with and endorse most of the content. This is important stuff, you should read it.

However, I do find some aspects of the paper (and the resulting discourse around it and around technology) to be problematic. These weren't clear to me when initially reading the first draft several months ago, but they became very clear to me now. These points are for the most part

Cheat sheet: JavaScript Array methods

Deriving a new Array from an existing Array:

['■','●','▲'].slice(1, 3)          ⟼ ['●','▲']
['■','●','■'].filter(x => x==='■') ⟼ ['■','■']
    ['▲','●'].map(x => x+x)        ⟼ ['▲▲','●●']
    ['▲','●'].flatMap(x => [x,x])  ⟼ ['▲','▲','●','●']

	from tqdm import tqdm
	from tqdm.utils import CallbackIOWrapper

	file_path = os.path.abspath(__file__)
	upload_url = https://some-bucket.s3.amazonaws.com

	file_size = os.stat(file_path).st_size
	with open(file_path, "rb") as f:
	with tqdm(total=file_size, unit="B", unit_scale=True, unit_divisor=1024) as t:
	wrapped_file = CallbackIOWrapper(t.update, f, "read")

	from transformers import BertForQuestionAnswering
	import torch

	bert_name = "bert-large-uncased-whole-word-masking-finetuned-squad"

	model = BertForQuestionAnswering.from_pretrained(bert_name, torchscript=True)
	model.eval()

	inputs = [torch.ones(1, 2, dtype=torch.int64),
	torch.ones(1, 2, dtype=torch.int64),

	INTRODUCTION
	============
	This archive was generated at the request of the following user:
	- @username at the time the archive was generated: luca
	- Account ID: 11985982

	The easiest way to navigate your archive is to open the HTML renderer in a desktop web browser by double clicking the “Your archive” file included in the main folder once the archive is unzipped.

	Note that the HTML renderer only works if the archive is less than 50GB. Also note that the HTML renderer only includes a subset of the data included in the archive. To see all the data included in the archive, please navigate the JSON files located in the “data” folder.

	#!/usr/bin/env python3

	# Script to create directories and index.md files for publications
	# for Academic Kickstart or Research Group Kickstart, taking reference
	# info from a Google Scholar CSV export.

	# Templates found here, https://wowchemy.com/templates/

	# input: one csv file from command line e.g. from scholar export
	# output: a dir structure of the publications for import to kickstart

	import tweepy
	import json
	import time
	import datetime
	from calendar import monthrange
	from PIL import Image, ImageEnhance
	import pathlib
	import pylunar

	{"title":"AUTOGRAPH","definition":"The handwriting of any one.","related":["EX SCRIPTIS OLIM VISIS","MAYN L","WILL HOLOGRAPHIC","IDENTIFICATION","HOLOGRAFO","EX VISU SCRIPTIONIS","ONOMASTIC","HANDWRITING EXPERT","BON FR","COMPARISON OF HANDWRITING"]}
	{"title":"AUXILIUM REGIS","definition":"In English law. The king’s aid or money levied for the royal use and the public service, as taxes granted by parliament.","related":["BANCUS REGIS","VIDUA REGIS","AUXILIUM","SERVIENS DOMINI REGIS","DE NON RESIDENTIA CLERICI REGIS","AD JURA REGIS","CAMERA REGIS","MINISTRI REGIS","NON-RESIDENTIO PRO CLERICO REGIS","AUXILIUM CURIAE"]}
	{"title":"AVER SILVER","definition":"A custom or rent formerly so called.","related":["GREEN SILVER","AVER CORN","HERRING SILVER","ALE SILVER","SLOUGH SILVER","AVER, N","AVER, V","AVER","AVER PENNY","AVER LAND"]}

	import tensorflow as tf #We need tensorflow 2.x
	import numpy as np

	#The hashlength in bits
	hashLength = 256

	def buildModel():
	#we can set the seed to simulate the fact that this network is known and doesn't change between runs
	#tf.random.set_seed(42)
	model = tf.keras.Sequential()