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Intel for display, NVIDIA for computing

This guide will show you how to use Intel graphics for rendering display and NVIDIA graphics for CUDA computing on Ubuntu 18.04 desktop.

I made this work on an ordinary gaming PC with two graphics devices, an Intel UHD Graphics 630 plus an NVIDIA GeForce GTX 1080 Ti. Both of them can be shown via lspci | grep VGA.

00:02.0 VGA compatible controller: Intel Corporation Device 3e92
01:00.0 VGA compatible controller: NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1)

	import numpy as np
	from scipy import stats

	n_samples = 1001
	samples = np.random.randn(n_samples)

	print("Example samples: ", samples[:10])

	# Compute the mean and standard deviation of the samples
	mean = np.mean(samples)

	import matplotlib.pyplot as plt
	import torch
	from celluloid import Camera
	from torch import nn

	linear = nn.Linear(2, 1, bias=False)

	camera = Camera(plt.figure())

	optimizer = torch.optim.Adam(linear.parameters(), 1)

	import argparse
	import torch
	import numpy as np
	import torch.nn as nn
	import torch.nn.functional as F
	from tqdm import tqdm, trange
	from torch.nn.utils.weight_norm import weight_norm
	from collections import defaultdict
	from torch.utils.data import DataLoader
	from torchvision import transforms

	function registerPlugin(plugin) {
	function registerPluginForIFrame() {
	iframe = $('iframe[id="speedgrader_iframe"]');
	iframe.load(() => {
	plugin(iframe.contents());
	});
	}

	// as the iframe of speed grader reloads for each different student,
	// we need to register the listener for each iframe

	import torch
	import torch.nn as nn
	from einops import rearrange


	class MatrixMultiplication(nn.Module):
	def __init__(self):
	super().__init__()

	def forward(self, x, y):

	import torch
	import torch.nn as nn
	from torchvision.models import resnet18


	def sample_mask(xl, p):
	mask = []
	for l in xl:
	idx = torch.randperm(l)
	idx = idx[: int(l * p)]