Leigh Johnson leigh-johnson

Database transactions

`pymysql`

Defaults to autocommit=False

connection = pymysql.connect(user='user', db='test')
cursor = connection.cursor()
cursor.execute('insert into test (value) values (10)')

This is roughly the same test run in both casper and nightwatch.

GCC compiler optimization for ARM-based systems

2017-03-03 fm4dd

The gcc compiler can optimize code by taking advantage of CPU specific features. Especially for ARM CPU's, this can have impact on application performance. ARM CPU's, even under the same architecture, could be implemented with different versions of floating point units (FPU). Utilizing full FPU potential improves performance of heavier operating systems such as full Linux distributions.

-mcpu, -march: Defining the CPU type and architecture

These flags can both be used to set the CPU type. Setting one or the other is sufficient.

Bitbake Cheatsheet

Verbose as possible

bitbake -vDDD your-recipe

List recipes

bitbake -s

Architecture

Papers

Chips

An Energy-Efficient Configurable Lattice Cryptography Processor for the Quantum-Secure Internet of Things. ISSCC-2019
A 28nm Bulk-CMOS 4-to-8GHz ¡2mW Cryogenic Pulse Modulator for Scalable Quantum Computing. ISSCC-2019
A Scalable Quantum Magnetometer in 65nm CMOS with Vector-Field Detection Capability. ISSCC-2019
A 48GHz 5.6mW Gate-Level-Pipelined Multiplier Using Single-Flux Quantum Logic. ISSCC-2019

	/*
	A script to generate a Google BigQuery-complient JSON-schema from a JSON object.

	Make sure the JSON object is complete before generating, null values will be skipped.

	References:
	https://cloud.google.com/bigquery/docs/data
	https://cloud.google.com/bigquery/docs/personsDataSchema.json
	https://gist.github.com/igrigorik/83334277835625916cd6
	... and a couple of visits to StackOverflow

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	#!/bin/bash

	### steps ####
	# Verify the system has a cuda-capable gpu
	# Download and install the nvidia cuda toolkit and cudnn
	# Setup environmental variables
	# Verify the installation
	###

	### to verify your gpu is cuda enable check

	default['sshd']['sshd_config']['AuthenticationMethods'] = 'publickey,keyboard-interactive:pam'
	default['sshd']['sshd_config']['ChallengeResponseAuthentication'] = 'yes'
	default['sshd']['sshd_config']['PasswordAuthentication'] = 'no'