| import SwiftUI | |
| import UIKit | |
| import PhoneNumberKit | |
| struct PhoneNumberTextFieldView: UIViewRepresentable { | |
| @Binding var phoneNumber: String | |
| private let textField = PhoneNumberTextField() | |
| func makeUIView(context: Context) -> PhoneNumberTextField { | |
| textField.withExamplePlaceholder = true |
| # This file may be used to create an environment using: | |
| # $ conda create --name <env> --file <this file> | |
| # platform: osx-arm64 | |
| # Set the platform to arm64 with `conda env config vars set CONDA_SUBDIR=osx-arm64` | |
| # For HuggingFace see https://huggingface.co/docs/accelerate/usage_guides/mps | |
| accelerate=0.16.0=pyhd8ed1ab_0 | |
| aiohttp=3.8.3=pypi_0 | |
| aiosignal=1.3.1=pypi_0 | |
| async-timeout=4.0.2=pypi_0 |
| """ | |
| $ python update.py | |
| Updates an AWS Lambda function with all the latest goodies. | |
| To use, create a directory containing all the files and requirements required to | |
| execute your Lambda function. Supply the directory, ARN of your Lambda function, | |
| and AWS profile (defaults to `default`) as arguments to `update.py`, e.g., | |
| python update.py <directory> <lambda_arn> <aws_profile> |
First, launch the Nvidia Gaming PC for Ubuntu 18.04 AMI from https://aws.amazon.com/marketplace/pp/B07SSKJMBJ?ref=cns_1clkPro.
After the AMI boots, SSH in and check nvidia-smi to confirm you have NVIDIA drivers by default. If you don't see something similar to the below, you've launched the wrong instance.
+-----------------------------------------------------------------------------+
| $LocalTempDir = $env:TEMP; $ChromeInstaller = "ChromeInstaller.exe"; (new-object System.Net.WebClient).DownloadFile('http://dl.google.com/chrome/install/375.126/chrome_installer.exe', "$LocalTempDir\$ChromeInstaller"); & "$LocalTempDir\$ChromeInstaller" /silent /install; $Process2Monitor = "ChromeInstaller"; Do { $ProcessesFound = Get-Process | ?{$Process2Monitor -contains $_.Name} | Select-Object -ExpandProperty Name; If ($ProcessesFound) { "Still running: $($ProcessesFound -join ', ')" | Write-Host; Start-Sleep -Seconds 2 } else { rm "$LocalTempDir\$ChromeInstaller" -ErrorAction SilentlyContinue -Verbose } } Until (!$ProcessesFound) |
| playerShip = { | |
| position = { | |
| x = 100, | |
| y = 60 | |
| }, | |
| rotation = 0, | |
| velocity = { | |
| speed = 12.0, | |
| direction = 0 | |
| }, |
#Mac OS X
| import os, sys | |
| import cv2 | |
| import numpy as np | |
| """ | |
| Get source video, then: | |
| 1. Convert to grayscale | |
| 2. Gaussian blur | |
| 3. Detect edges |
| """ | |
| This script populates a DynamoDB table with seed data. Add seeds into a JSON file, named the same as your table. Place the file in a `./seeds/` directory, e.g., | |
| /seeds/PetCategories.json. | |
| The file should contain data that matches your database schema. | |
| [ | |
| { | |
| "petCategory": 1, |
| ''' | |
| A trigger function for AWS Lambda that listens for object creation in an S3 bucket, | |
| takes images, finds faces, and tags with metadata into a DDB table. | |
| ''' | |
| import boto3 | |
| from decimal import Decimal | |
| import json | |
| import urllib |