LM emptymalei

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

Super fast, keyboard-optimized, client side Hugo search

This is a fork of and builds upon the work of Eddie Webb's search and Matthew Daly's search explorations.

It's built for the Hugo static site generator, but could be adopted to function with any json index.

To see it in action, go to craigmod.com and press CMD-/ and start typing.

	# This is the workround before https://github.com/pydantic/pydantic-ai/issues/224 is fixed
	from pydantic_ai.models.openai import (
	OpenAIModel,
	OpenAIModelSettings,
	ModelRequestParameters,
	)

	from itertools import chain
	from openai import NOT_GIVEN, AsyncOpenAI, AsyncStream
	from openai.types import ChatModel, chat

	import numpy as np
	import torch


	class MyModule(torch.nn.Module):

	def add_param(self, key, shape):
	val = torch.randn(*shape)/np.prod(shape)
	setattr(self, key, torch.nn.Parameter(val))

	# First, some imports:
	import numpy as np
	from darts.utils import timeseries_generation as tg

	np.random.seed(42)

	LENGTH = 3 * 365 # 3 years of daily data

	# Melting: a sine with yearly periodicity and additive white noise
	melting = (tg.sine_timeseries(length=LENGTH,

	import s3fs
	import pyarrow as pa
	import pyarrow.parquet as pq


	from itertools import chain
	from typing import Tuple, Any

	def iter_parquet(s3_uri: str, columns = None, batch_size=1_000) -> Tuple[Any]:

	#!/bin/bash

	set -e

	sudo -u ec2-user -i <<'EOF'

	ENVIRONMENT=python38
	VERSION=3.8

	conda create -y -n "$ENVIRONMENT" python="$VERSION" tensorflow-gpu numpy opencv pandas pyyaml

	"""
	WARNING: dont use loguru, use structlog
	https://gist.github.com/nkhitrov/38adbb314f0d35371eba4ffb8f27078f


	Configure handlers and formats for application loggers.
	"""
	import logging
	import sys
	from pprint import pformat

	#include <stdint.h>
	#include <stdio.h>
	#include <stdlib.h>

	// munged from https://github.com/simontime/Resead

	namespace sead
	{
	class Random
	{