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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

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How to install CUDA / cuDNN / TensorRT on Ubuntu

Install NVIDIA drivers

Update & upgrade

sudo apt update && sudo apt upgrade

Remove previous NVIDIA installation

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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

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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

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

jobs:
  build:
    name: Build docs

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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

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import matplotlib.pyplot as plt
import torch
from pathlib import Path
import torchvision
import torchvision.transforms as transforms
from loguru import logger
from torch import nn
import click