garg-aayush

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

maedoc

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))

denguir

How to install CUDA / cuDNN / TensorRT on Ubuntu

Install NVIDIA drivers

Update & upgrade

sudo apt update && sudo apt upgrade

Remove previous NVIDIA installation

hrzn

# 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, 

grantmwilliams

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]:

svpino

#!/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

nkhitrov

"""
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

Treeki

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

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

namespace sead
{
class Random
{

cmod

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 compatible with Fuse fuzzy search library.

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

lifedispenser

class Person extends IdyllModel
    belongs_to :working_population

    define_attribute :stress, type: integer, min: 0, max: 100, init: random
    define_attribute :environment, type: enum, ['high income', 'low income', 'normal']
    define_attribute :group, type: classification, |model| => { case model.stress; when 50..100: "high risk"; when 0..49: "low risk" }

    def next_step
        self.stress += 5 if self.environment == "high income"
        self.stress -= 5 if self.environment == "low income"