LM emptymalei

WHO Growth Charts Data files

Weight-for-age charts, Birth to 24 Months, LMS parameters and selected smoothed weight percentiles in kilograms, by age

Source: https://www.cdc.gov/growthcharts/who-data-files.htm

Setting Up Your Personal GPU Server: A Step-by-Step Guide

I've been using a GPU workstation with an RTX 4090 for almost a year now, and it's been one of the best decisions I've made. With a personal GPU server, you no longer need to rely on cloud-based GPU instances from services like RunPod or Vast.ai every time you want to run a job or try new models. The best part? No stress about recurring GPU instance costs! :-)

However, I rarely work directly on my workstation. Instead, I prefer the flexibility of accessing the GPU remotely using my MacBook, whether I'm working from different locations within my home, from a co-working space, or a cozy cafe in another part of town.

In this blog, I will walk you through the steps to configure a personal GPU Ubuntu server.

For this guide, I assume you already have a workstation running Ubuntu with a GPU and it is connected to your local network

How to install CUDA / cuDNN / TensorRT on Ubuntu

Install NVIDIA drivers

Update & upgrade

sudo apt update && sudo apt upgrade

Remove previous NVIDIA installation

	date	avg_sunspot_area
	1875-01-01	213.133333333333
	1876-01-01	109.283333333333
	1877-01-01	92.8583333333333
	1878-01-01	22.2166666666667
	1879-01-01	36.3333333333333
	1880-01-01	446.75
	1881-01-01	679.491666666667
	1882-01-01	968.025
	1883-01-01	1148.90833333333

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	Cal("init", "just-chat", {origin:"https://cal.com"});

	Cal.ns["just-chat"]("inline", {
	elementOrSelector:"#my-cal-inline",
	calLink: "leima/just-chat",
	layout: "month_view"

	import numpy as np
	import plotly.offline as pyo
	import plotly.graph_objs as go

	# Generate a random signal
	np.random.seed(42)
	random_signal = np.random.normal(size=100)

	# Offset the line length by the marker size to avoid overlapping
	marker_offset = 0.04

	import torch
	from torch.utils.data import Dataset


	class FakeTimeSeriesDataset(Dataset):
	def __init__(self, sequence_length, input_length, prediction_length, nodes) -> None:
	super().__init__()

	self.sequence_length = sequence_length
	self.prediction_length = prediction_length

	name: Docs
	on:
	push:
	branches: [ master ]
	pull_request:
	branches: [ master ]

	jobs:
	build:
	name: Build docs

	import json
	import os
	import random

	import requests
	from loguru import logger
	from requests.adapters import HTTPAdapter
	from requests.packages.urllib3.util.retry import Retry