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Connect to your Google Colab Notebook via SSH

Using this amazing project tmate.io you can SSH tunnel to your colab notebook machine. You can with few lines of code download the binary, run an instance in the background and output the generated SSH address.

!wget -nc https://github.com/tmate-io/tmate/releases/download/2.4.0/tmate-2.4.0-static-linux-i386.tar.xz &> /dev/null
!tar --skip-old-files -xvf tmate-2.4.0-static-linux-i386.tar.xz &> /dev/null
!rm -f nohup.out; bash -ic 'nohup ./tmate-2.4.0-static-linux-i386/tmate -S /tmp/tmate.sock new-session -d & disown -a' >/dev/null 2>&1
!./tmate-2.4.0-static-linux-i386/tmate -S /tmp/tmate.sock wait tmate-ready

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# https://github.com/numba/llvmlite/issues/693#issuecomment-909501195
brew install llvm@11   
LLVM_CONFIG="/opt/homebrew/Cellar/llvm@11/11.1.0_4/bin/llvm-config" arch -arm64 pip install llvmlite

pip install umap-learn

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function layout() {
    return {
        name: "Grid",
        getFrameAssignments: (windows, screenFrame) => {
            const numWindows = windows.length;
            const numRows = Math.floor(Math.sqrt(numWindows));
            const numCols = Math.ceil(Math.sqrt(numWindows));

            const cellWidth = screenFrame.width / numCols;
            const cellHeight = screenFrame.height / numRows;

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from pygame.locals import KEYDOWN, K_ESCAPE, K_q
import pygame
import cv2
import sys

camera = cv2.VideoCapture(1)
pygame.init()
pygame.display.set_caption("OpenCV camera stream on Pygame")
screen = pygame.display.set_mode([1280, 720])

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import json
import requests
from PIL import Image
import io
import re
from time import time

API_TOKEN = ""  # token in case you want to use private API
headers = {
    # "Authorization": f"Bearer {API_TOKEN}",

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Bookmark this javascript code to make the minimal Stable Diffusion UI thanks to @huggingface inference API pic.twitter.com/i1slI4ObdH

— Radamés Ajna (@radamar) February 1, 2023

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

Bookmark this code

javascript:p=prompt("Enter your prompt:");fetch("https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5",{method:"POST",headers:{"content-type":"application/json","X-Wait-For-Model":"true"},body:JSON.stringify({inputs:p})}).then(e=>e.blob()).then(e=>window.open(URL.createObjectURL(e),"_self"))

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import pygame
from pygame.locals import *
import cv2
import numpy as np
import time
import picamera
import picamera.array

screen_width = 640

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//Planet_Kronos_x8 The Kazimier Dezember 2015 
//The MIT License (MIT)

#include <SPI.h>
#include <Ethernet.h>
#include <EthernetUdp.h>
#include <OSCBundle.h>

#define DEBUG 1

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long lastTime = 0;
long minutes = 0;
long hours = 0;

void setup() {

}

void loop() {

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import cv2

cap = cv2.VideoCapture(0)
cap.set(3, 640)  # WIDTH
cap.set(4, 480)  # HEIGHT

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_eye.xml")

while(True):