This guide will show you how to use Intel graphics for rendering display and NVIDIA graphics for CUDA computing on Ubuntu 18.04 / 20.04 desktop.
I made this work on an ordinary gaming PC with two graphics devices, an Intel UHD Graphics 630 plus an NVIDIA GeForce GTX 1080 Ti.
Both of them can be shown via lspci | grep VGA.
00:02.0 VGA compatible controller: Intel Corporation Device 3e92
01:00.0 VGA compatible controller: NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1)
| #macro PROMISE_MAX_TIME (1/60 * 1_000 * 1_000) * (1/16) //the max time in milli seconds to spend on the promises, default is 1/16 of frame time of a 60 fps game | |
| enum PROMISE_STATE { | |
| PENDING, | |
| RESOLVED, | |
| REJECTED, | |
| PAUSED, | |
| }; | |
| function __PromiseNamespace__() { |
| # (C) Mathieu Blondel, November 2013 | |
| # License: BSD 3 clause | |
| import numpy as np | |
| def ranking_precision_score(y_true, y_score, k=10): | |
| """Precision at rank k | |
| Parameters |
JavaScript does not bother you too much with types (at first), which is both a blessing and a cure. But we all know the Boolean type. Boolean variables can either be true or false. Yes or no.
Every value in JavaScript can be translated into a boolean, true or false. Values that translate to true are truthy, values that translate to false are falsy. Simple.
This is about two ways to make that translation.
| // Convert mp3 files recursively to wav using [fluent-ffmpeg](https://github.com/fluent-ffmpeg/node-fluent-ffmpeg) for [node.js](https://nodejs.org) | |
| // | |
| // 1. Install fluent-ffmpeg: `npm install fluent-ffmpeg` | |
| // 2. Run this script: `node mp3ToWav.js [path/to/file/or/folder]` | |
| convertMp3ToWav = function (input) { | |
| let segments = input.split('/'); | |
| let filename = segments[segments.length - 1]; | |
| let extension = filename.split('.')[1]; |
| import json | |
| import logging | |
| from flask import Flask, g | |
| from flask_oidc import OpenIDConnect | |
| import requests | |
| logging.basicConfig(level=logging.DEBUG) | |
| app = Flask(__name__) |
| import numpy as np | |
| from tqdm import trange | |
| def get_neighbour_matrix(x, L, R): | |
| dx = np.subtract.outer(x[:, 0], x[:, 0]) | |
| dy = np.subtract.outer(x[:, 1], x[:, 1]) | |
| dx[dx > (L / 2) ** 2] -= (L / 2) ** 2 | |
| dy[dy > (L / 2) ** 2] -= (L / 2) ** 2 | |
| pair_dist = dx ** 2 + dy ** 2 |
See how a minor change to your commit message style can make a difference.
Tip
Have a look at git-conventional-commits , a CLI util to ensure these conventions and generate verion and changelogs
For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.
Generally, properly configured nginx can handle up to 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon with HyperThreading enabled, but it can work without problem on slower machines.
You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.
Here are the steps to follow in order to read your cards. Your goal is to find as many keys as possible. The keys unlock sections of your card for the Flipper to read them - you must have a card. Once you read enough sections, you can use an emulated or cloned card at the original card reader to unlock it (sometimes even without finding all of the keys!).
Steps:
