Note:
The docker package comes from the RHEL Extras repository (rhel-7-server-extras-rpms). RHEL Extras is only supported with the latest RHEL minor release. So now Extras requires RHEL 7.5.
Previous minor releases will fail on dependency errors. Need to download custom version of container-selinux, see below.
| using Microsoft.Xrm.Sdk; | |
| using System; | |
| using System.Collections.Generic; | |
| using System.Linq; | |
| using System.Text; | |
| using System.Threading.Tasks; | |
| namespace ManyManyRelPlugin | |
| { | |
| public class manymany : IPlugin |
| <?php | |
| /** Display verbose errors */ | |
| define( 'IMPORT_DEBUG', false ); | |
| // Load Importer API | |
| require_once ABSPATH . 'wp-admin/includes/import.php'; | |
| if ( ! class_exists( 'WP_Importer' ) ) { | |
| $class_wp_importer = ABSPATH . 'wp-admin/includes/class-wp-importer.php'; | |
| if ( file_exists( $class_wp_importer ) ) |
Your company's GPU computing strategy is essential whether you engage in 3D visualization, machine learning, AI, or any other form of intensive computing.
There was a time when businesses had to wait for long periods of time while deep learning models were being trained and processed. Because it was time-consuming, costly, and created space and organization problems, it reduced their output.
This problem has been resolved in the most recent GPU designs. Because of their high parallel processing efficiency, they are well-suited for handling large calculations and speeding up the training of your AI models.
When it comes to deep learning, GPUs can speed up the training of neural networks by a factor of 250 compared to CPUs, and the latest generation of cloud GPUs is reshaping data science and other emerging technologies by delivering even greater performance at a lower cost and with the added benefits of easy scalability and rapid deployment.
| #! /usr/bin/python3 | |
| # | |
| # File: SMJobBlessUtil.py | |
| # | |
| # Contains: Tool for checking and correcting apps that use SMJobBless. | |
| # | |
| # Written by: DTS | |
| # | |
| # Copyright: Copyright (c) 2012 Apple Inc. All Rights Reserved. | |
| # |
This gist is an exploration of how to validate email addresses with regex as close as possible to the specification in RFC 6531.
| # Make sure to stop Appwrite before this backup, | |
| # and make sure you have enough space on the machine. | |
| # After backing up, make sure there is a file in 'backups/backup-___.tar.gz'. | |
| # Also please check size of this file, it should be at least 5kb, even for small instances. | |
| docker run --rm \ | |
| -v appwrite_appwrite-mariadb:/backup/appwrite-mariadb \ | |
| -v appwrite_appwrite-redis:/backup/appwrite-redis \ | |
| -v appwrite_appwrite-cache:/backup/appwrite-cache \ |
| // | |
| // CacheAsyncImage.swift | |
| // | |
| // Created by Costantino Pistagna on 08/02/23. | |
| // | |
| import SwiftUI | |
| struct CacheAsyncImage<Content, Content2>: View where Content: View, Content2: View { | |
| private let url: URL? |
| """ | |
| Copyright 2019 Jason Hu <awaregit at gmail.com> | |
| 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 | |
| Unless required by applicable law or agreed to in writing, software |
