| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # Copyright 2009-2015 MongoDB, Inc. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| |
| to check if the server works - https://webrtc.github.io/samples/src/content/peerconnection/trickle-ice | |
| stun: | |
| stun.l.google.com:19302, | |
| stun1.l.google.com:19302, | |
| stun2.l.google.com:19302, | |
| stun3.l.google.com:19302, | |
| stun4.l.google.com:19302, | |
| stun.ekiga.net, | |
| stun.ideasip.com, |
| import { v4 as uuid } from 'uuid'; | |
| export function generateId() { | |
| return uuid(); | |
| } | |
| const v4 = new RegExp(/^[0-9A-F]{8}-[0-9A-F]{4}-4[0-9A-F]{3}-[89AB][0-9A-F]{3}-[0-9A-F]{12}$/i); | |
| console.log(generateId().match(v4)); | |
| //console.log(generateId().length) |
| import functools | |
| def force_async(fn): | |
| ''' | |
| turns a sync function to async function using threads | |
| ''' | |
| from concurrent.futures import ThreadPoolExecutor | |
| import asyncio | |
| pool = ThreadPoolExecutor() |
[ Update 2025-03-24: Commenting is disabled permanently. Previous comments are archived at web.archive.org. ]
Most of the terminal emulators auto-detect when a URL appears onscreen and allow to conveniently open them (e.g. via Ctrl+click or Cmd+click, or the right click menu).
It was, however, not possible until now for arbitrary text to point to URLs, just as on webpages.
Kong, Traefik, Caddy, Linkerd, Fabio, Vulcand, and Netflix Zuul seem to be the most common in microservice proxy/gateway solutions. Kubernetes Ingress is often a simple Ngnix, which is difficult to separate the popularity from other things.
This is just a picture of this link from March 2, 2019
Originally, I had included some other solution
| import pandas as pd | |
| import pandas.io.sql as sqlio | |
| import psycopg2 | |
| conn = psycopg2.connect("host='{}' port={} dbname='{}' user={} password={}".format(host, port, dbname, username, pwd)) | |
| sql = "select count(*) from table;" | |
| dat = sqlio.read_sql_query(sql, conn) | |
| conn = None |
| cat /proc/self/cgroup | grep -o -e "docker-.*.scope" | head -n 1 | sed "s/docker-\(.*\).scope/\\1/" |
Python is the primary language in which TensorFlow models are typically developed and trained. TensorFlow does have bindings for other programming languages. These bindings have the low-level primitives that are required to build a more complete API, however, lack much of the higher-level API richness of the Python bindings, particularly for defining the model structure.
This file demonstrates taking a model (a TensorFlow graph) created by a Python program and running the training loop in C++.
