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title

Cloud GPUs: Servers, Providers & Everything You Would Ever Need

Ubuntu on Asus ROG Zephyrus G14 2021 (Setup guide)

Here is a way to do a robust install of Ubuntu (+ optional Windows 11 dual boot and LUKS encryption) on an Asus laptop, with minimal usable hardware support, without a significant amount of tinkering that may break in future or require frequent technical attention.

In summary, the key thing is to have an up to date kernel, which usually means disabling secure-boot and installing the latest stable (6.0+) using mainline or xanmod, and as well as making sure the latest nvidia driver and dkms is installed.

Specs:

Model Asus G14 2021 (GA401QC)
AMD R7 5800 8 core 16 thread (onboard Radeon graphics)
NVIDIA RTX 3050 4GB (60W +15W boost)

Distributed computation

Libraries:

Dask distributed is a lightweight library for distributed computing
Ray - is a distributed execution framework
Celery - Distributed Task Queue
Redis Queue - is a simple Python library for queueing jobs
Apache Flink - Stateful Computations over Data Streams
Apache Spark - Lightning-fast unified analytics engine
JobLib

Debugging Gitlab CI jobs locally

You can find a bunch of articles about debugging Gitlab CI jobs locally. My top results from google:

But in all that articles the described way is to install Docker and gitlab-runner executable, run gitlab-runner exec docker JOB_NAME_FROM_GITLAB_CI_YML after that in your repo.

I don't like this approach because I want a simpler way to run appropriate gitlab-runner executable and don't

$ juju deploy cs:bionic/ubuntu --constraints 'mem=30G cores=4 root-disk=50G'
Located charm "cs:ubuntu-12".
Deploying charm "cs:ubuntu-12".

$ juju ssh ubuntu/0

ubuntu@ip-172-31-10-253:~$ snap list
Name              Version    Rev   Tracking  Publisher   Notes
amazon-ssm-agent  2.2.800.0  495   stable/…  aws✓        classic

Vue: Pass Slots through from Parent to Child Components

The Situation

We've got some components A, B and C which provide different slots.

const A = {
  template: `<div><slot name="a">Default A Content</slot></div>`
}

const B = {

Mistakes to Avoid: Docker Antipatterns

Whichever route you take to implementing containers, you’ll want to steer clear of common pitfalls that can undermine the efficiency of your Docker stack.

Don’t run too many processes inside a single container

The beauty of containers—and an advantage of containers over virtual machines—is that it is easy to make multiple containers interact with one another in order to compose a complete application. There is no need to run a full application inside a single container. Instead, break your application down as much as possible into discrete services, and distribute services across multiple containers. This maximizes flexibility and reliability.

Don’t install operating systems inside Docker containers

It is possible to install a complete Linux operating system inside a container. In most cases, however, this is not necessary. If your goal is to host just a single application or part of an application in the container, you need to install only the essential

	#!/bin/bash
	## This gist contains instructions about cuda v11.2 and cudnn 8.1 installation in Ubuntu 18.04 for PyTorch

	#############################################################################################
	##### forked by : https://gist.github.com/Mahedi-61/2a2f1579d4271717d421065168ce6a73 ########
	#############################################################################################


	### steps ####
	# verify the system has a cuda-capable gpu

	import json
	import logging

	from flask import Flask, g
	from flask_oidc import OpenIDConnect
	import requests

	logging.basicConfig(level=logging.DEBUG)

	app = Flask(__name__)