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Setup PyTorch with CUDA in WSL-Ubuntu on Windows 11

On Windows 11:

1. Download and install Nvidia GPU Driver for Windows: https://www.nvidia.com/download/index.aspx
2. Download and install CUDA Toolkit for Windows: https://developer.nvidia.com/cuda-downloads

NOTE: Nvidia Official doc says, the GPU Driver includes "CUDA Support", which is very confusing. It doesn't mean CUDA is included in Driver. What included is only some support files, not CUDA itself. You still need to install CUDA separately.

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// ==UserScript==
// @name         LinkedIn Improve
// @namespace    http://tampermonkey.net/
// @version      2024-02-28
// @description  try to take over the world!
// @author       You
// @match        https://www.linkedin.com/messaging/thread/*
// @icon         https://www.google.com/s2/favicons?sz=64&domain=linkedin.com
// @grant        GM_addStyle
// ==/UserScript==

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from pathlib import Path

FILE = Path(__file__).resolve()
NAME = FILE.stem
HERE = FILE.parent

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create db and user:

app=$1

dbuser=$app
dbname=$app

sudo su --login postgres << EOF

createuser --echo ${dbuser};

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include .env and use envvar:

in your .env:

foo=bar

in your Makefile:

include .env

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usage syntax:

ffmpeg input(s) [codec options] output(s)

convert video format:

ffmpg -i input.mov -c copy output.mp4

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Linux

• On Linux, tmpfs == shm == shared memory.
• you can mount tmpfs to a dir, and use that dir as normal dir but super fast.
• Default path is at /dev/shm/.

when you create a file there, it actually lives in memory. Thus, it will be super fast to read/write, and applications/processes can use it to communicate.

By default, the size limit of /dev/shm is half of your physical memory. E.g.: my linux PC has 32G mem:

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on AWS, you may see some instance types have instance storage attr with it. E.g.:

g4dn.xlarge	1	4	16	1 x 125 NVMe SSD --> Instance Storage in GB

This means, when you boot a instance with this type, you will have 2 volumes attached:

• boot volume, default 8G, system installed on it
• instance storage volume, 125G, or 116.4Gi, spare, not in use by default.

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provider "aws" {
  region = "us-east-1"
}

resource "aws_s3_bucket" "bucket" {
  # change this to your own bucket name
  bucket = "my-test-bucket"
  # allow to destroy bucket even when not empty
  force_destroy = true
}

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#!/usr/bin/env ansible-playbook
---
- hosts: localhost
  gather_facts: no
  tasks:
    - name: sleep 10s
      command: "sleep 10"
      loop: "{{range(3)|list}}"
      async: 11
      poll: 0