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簡介

GROKKING NOTES

I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.

So below I made a list of leetcode problems that are as close to grokking problems as possible.

Pattern: Sliding Window

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

RKE2 commands

Install

curl -sL https://get.rke2.io | sh
systemctl daemon-reload
systemctl start rke2-server

I had a large dataset in postgis and wanted to avoid the hassle of first exporting it to a several GB geojson file before tiling it with Tippecanoe.

ogr2ogr -f GeoJSON /dev/stdout \                                                                            
PG:"host=localhost dbname=postgres user=postgres password=thepassword" \
-sql "select * from a, roi where a.geom && roi.geom" \
| docker run -i -v ${PWD}:/data tippecanoe:latest tippecanoe \
--output=/data/yourtiles.mbtiles

Intro Docs

DPU-Based Hardware Acceleration: A Software Perspective

Setup

BlueField-2 Quickstart Guide for Clemson R7525s
NVIDIA Mellanox Bluefield-2 SmartNIC Hands-On Tutorial: Host setup is little outdate. See Host setup bellow.
NVIDIA Mellanox Bluefield-2 SmartNIC Hands-On Tutorial: “Rig for Dive” — Part III: Ultimate Cloudlab Setup
NVIDIA Mellanox Bluefield-2 SmartNIC Hands-On Tutorial: “Rig for Dive” — Part V: Install the Latest Bluefield OS with DPDK and DOCA

	#!/bin/bash

	### steps ####
	# verify the system has a cuda-capable gpu
	# download and install the nvidia cuda toolkit and cudnn
	# setup environmental variables
	# verify the installation
	###

	### to verify your gpu is cuda enable check

	wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub \| sudo apt-key add -
	sudo sh -c 'echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google-chrome.list'
	sudo apt-get update
	sudo apt-get install google-chrome-stable

	// 3D Dom viewer, copy-paste this into your console to visualise the DOM as a stack of solid blocks.
	// You can also minify and save it as a bookmarklet (https://www.freecodecamp.org/news/what-are-bookmarklets/)
	(() => {
	const SHOW_SIDES = false; // color sides of DOM nodes?
	const COLOR_SURFACE = true; // color tops of DOM nodes?
	const COLOR_RANDOM = false; // randomise color?
	const COLOR_HUE = 190; // hue in HSL (https://hslpicker.com)
	const MAX_ROTATION = 180; // set to 360 to rotate all the way round
	const THICKNESS = 20; // thickness of layers
	const DISTANCE = 10000; // ¯\\_(ツ)_/¯

	<?php

	if (!function_exists('interface_exists')) {
	die('PHP version too old');
	}
	$throwables = listThrowableClasses();
	$throwablesPerParent = splitInParents($throwables);
	printTree($throwablesPerParent);
	if (count($throwablesPerParent) !== 0) {
	die('ERROR!!!');