sahirshahryar

    public static String romanNumerals (int value) {
        String[] values = {
            "M", "CM", "D", "CD", "C", "XC",
            "L", "XL", "X", "IX", "V", "IV", "I"
        };
        
        int[] correspondents = {
            1000, 900, 500, 400, 100, 90,
            50, 40, 10, 9, 5, 4, 1
        }

arxenix

package your-package;

import org.bukkit.ChatColor;
import org.bukkit.entity.Player;

import java.awt.*;
import java.awt.image.BufferedImage;
import java.util.HashMap;

/**

Garris0n-

    // https://github.com/Bukkit/CraftBukkit/blob/7e1ac0a77129b169704c1e222ff2deb3ab6cd2d2/src/main/java/net/minecraft/server/EntityPlayer.java#L596
    //Method to open an anvil inventory to a player
    public static void openAnvil(Player player, Inventory inventory){

        //Get our EntityPlayer
        EntityPlayer p = ((CraftPlayer) player).getHandle();

        //Create the AnvilContainer
        AnvilContainer container = new AnvilContainer(p);

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

TengdaHan

Multi-node-training on slurm with PyTorch

What's this?

• A simple note for how to start multi-node-training on slurm scheduler with PyTorch.
• Useful especially when scheduler is too busy that you cannot get multiple GPUs allocated, or you need more than 4 GPUs for a single job.
• Requirement: Have to use PyTorch DistributedDataParallel(DDP) for this purpose.
• Warning: might need to re-factor your own code.
• Warning: might be secretly condemned by your colleagues because using too many GPUs.