Use PyTorch to Easily Access Your GPU

are fortunate sufficient to have entry to a system with an Nvidia Graphical Processing Unit (Gpu). Do you know there may be an absurdly straightforward technique to make use of your GPU’s capabilities utilizing a Python library supposed and predominantly used for machine studying (ML) purposes? 

Don’t fear in case you’re lower than velocity on the ins and outs of ML, since we gained’t be utilizing it on this article. As an alternative, I’ll present you easy methods to use the PyTorch library to entry and use the capabilities of your GPU. We’ll examine the run occasions of Python packages utilizing the favored numerical library NumPy, operating on the CPU, with equal code utilizing PyTorch on the GPU. 

Earlier than persevering with, let’s rapidly recap what a GPU and Pytorch are.

What’s a GPU?

A GPU is a specialised digital chip initially designed to quickly manipulate and alter reminiscence to speed up the creation of photos in a body buffer supposed for output to a show machine. Its utility as a speedy picture manipulation machine was based mostly on its means to carry out many calculations concurrently, and it’s nonetheless used for that objective.

Nonetheless, GPUs have just lately turn out to be invaluable in machine studying, giant language mannequin coaching and growth. Their inherent means to carry out extremely parallelizable computations makes them best workhorses in these fields, as they make use of complicated mathematical fashions and simulations.

What’s PyTorch?

PyTorch is an open-source machine studying library developed by Fb’s AI Analysis Lab (FAIR). It’s broadly used for pure language processing and laptop imaginative and prescient purposes. Two of the primary causes that Pytorch can be utilized for GPU operations are,

• Considered one of PyTorch’s core knowledge constructions is the Tensor. Tensors are just like arrays and matrices in different programming languages, however are optimised for operating on a GPU.
• Pytorch has CUDA help. PyTorch seamlessly integrates with CUDA, a parallel computing platform and programming mannequin developed by NVIDIA for basic computing on its GPUS. This permits PyTorch to entry the GPU {hardware} instantly, accelerating numerical computations. CUDA will allow builders to make use of PyTorch to put in writing software program that absolutely utilises GPU acceleration.

In abstract, PyTorch’s help for GPU operations by CUDA and its environment friendly tensor manipulation capabilities make it a wonderful software for growing GPU-accelerated Python features with excessive computational calls for. 

As we’ll present in a while, you don’t have to make use of PyTorch to develop machine studying fashions or prepare giant language fashions.

In the remainder of this text, we’ll arrange our growth setting, set up PyTorch and run by just a few examples the place we’ll examine some computationally heavy PyTorch implementations with the equal numpy implementation and see what, if any, efficiency variations we discover.

Pre-requisites

You want an Nvidia GPU in your system. To test your GPU, subject the next command at your system immediate. I’m utilizing the Home windows Subsystem for Linux (WSL).

$ nvidia-smi

>>
(base) PS C:Usersthoma> nvidia-smi
Fri Mar 22 11:41:34 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 551.61                 Driver Model: 551.61         CUDA Model: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Identify                     TCC/WDDM  | Bus-Id          Disp.A | Unstable Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Utilization/Cap |           Reminiscence-Utilization | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce RTX 4070 Ti   WDDM  |   00000000:01:00.0  On |                  N/A |
| 32%   24C    P8              9W /  285W |     843MiB /  12282MiB |      1%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Kind   Course of title                              GPU Reminiscence |
|        ID   ID                                                               Utilization      |
|=========================================================================================|
|    0   N/A  N/A      1268    C+G   ...tilityHPSystemEventUtilityHost.exe      N/A      |
|    0   N/A  N/A      2204    C+G   ...ekyb3d8bbwePhoneExperienceHost.exe      N/A      |
|    0   N/A  N/A      3904    C+G   ...calMicrosoftOneDriveOneDrive.exe      N/A      |
|    0   N/A  N/A      7068    C+G   ...CBS_cw5n
and so on ..

If that command isn’t recognised and also you’re positive you might have a GPU, it most likely means you’re lacking an NVIDIA driver. Simply observe the remainder of the directions on this article, and it ought to be put in as a part of that course of.

Whereas PyTorch set up packages can embody CUDA libraries, your system should nonetheless set up the suitable NVIDIA GPU drivers. These drivers are vital to your working system to speak with the graphics processing unit (GPU) {hardware}. The CUDA toolkit consists of drivers, however in case you’re utilizing PyTorch’s bundled CUDA, you solely want to make sure that your GPU drivers are present.

Click on this link to go to the NVIDIA web site and set up the newest drivers suitable along with your system and GPU specs.

Organising our growth setting

As a greatest apply, we should always arrange a separate growth setting for every mission. I exploit conda, however use no matter technique fits you.

If you wish to go down the conda route and don’t have already got it, you could set up Miniconda (really helpful) or Anaconda first. 

Please be aware that, on the time of writing, PyTorch at present solely formally helps Python variations 3.8 to three.11.

#create our take a look at setting
(base) $ conda create -n pytorch_test python=3.11 -y

Now activate your new setting.

(base) $ conda activate pytorch_test

We now have to get the suitable conda set up command for PyTorch. This may rely in your working system, chosen programming language, most popular bundle supervisor, and CUDA model. 

Fortunately, Pytorch supplies a helpful net interface that makes this straightforward to arrange. So, to get began, head over to the Pytorch web site at…

https://pytorch.org

Click on on the Get Began hyperlink close to the highest of the display screen. From there, scroll down a little bit till you see this,

Click on on every field within the acceptable place to your system and specs. As you do, you’ll see that the command within the Run this Command output area modifications dynamically. If you’re completed making your selections, copy the ultimate command textual content proven and sort it into your command window immediate. 

For me, this was:-

(pytorch_test) $ conda set up pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia -y

We’ll set up Jupyter, Pandas, and Matplotlib to allow us to run our Python code in a pocket book with our instance code.

(pytroch_test) $ conda set up pandas matplotlib jupyter -y

Now kind in jupyter pocket book into your command immediate. It is best to see a jupyter pocket book open in your browser. If that doesn’t occur mechanically, you’ll possible see a screenful of knowledge after the jupyter pocket book command.

Close to the underside, there can be a URL that it is best to copy and paste into your browser to provoke the Jupyter Pocket book.

Your URL can be completely different to mine, but it surely ought to look one thing like this:-

http://127.0.0.1:8888/tree?token=3b9f7bd07b6966b41b68e2350721b2d0b6f388d248cc69da

Testing our setup

The very first thing we’ll do is take a look at our setup. Please enter the next right into a Jupyter cell and run it.

import torch
x = torch.rand(5, 3)
print(x)

It is best to see an analogous output to the next.

tensor([[0.3715, 0.5503, 0.5783],
        [0.8638, 0.5206, 0.8439],
        [0.4664, 0.0557, 0.6280],
        [0.5704, 0.0322, 0.6053],
        [0.3416, 0.4090, 0.6366]])

Moreover, to test in case your GPU driver and CUDA are enabled and accessible by PyTorch, run the next instructions:

import torch
torch.cuda.is_available()

This could output True if all is OK. 

If all the things is okay, we are able to proceed to our examples. If not, return and test your set up processes.

NB Within the timings beneath, I ran every of the Numpy and PyTorch processes a number of occasions in succession and took the very best time for every. This does favour the PyTorch runs considerably as there’s a small overhead on the very first invocation of every PyTorch run however, total, I feel it’s a fairer comparability.

Instance 1 — A easy array math operation.

On this instance, we arrange two giant, an identical one-dimensional arrays and carry out a easy addition to every array aspect.

import numpy as np
import torch as pt

from timeit import default_timer as timer   
  

#func1 will run on the CPU   
def func1(a):                                 
    a+= 1  
    
#func2 will run on the GPU
def func2(a):                                 
    a+= 2

if __name__=="__main__": 
    n1 = 300000000                          
    a1 = np.ones(n1, dtype = np.float64) 

    # needed to make this array a lot smaller than
    # the others as a result of gradual loop processing on the GPU
    n2 = 300000000                     
    a2 = pt.ones(n2,dtype=pt.float64)

    begin = timer() 
    func1(a1) 
    print("Timing with CPU:numpy", timer()-start)     
      
    begin = timer() 
    func2(a2) 
    #anticipate all calcs on the GPU to finish
    pt.cuda.synchronize()
    print("Timing with GPU:pytorch", timer()-start) 
    print()

    print("a1 = ",a1)
    print("a2 = ",a2)

Timing with CPU:numpy 0.1334826999955112
Timing with GPU:pytorch 0.10177790001034737

a1 =  [2. 2. 2. ... 2. 2. 2.]
a2 =  tensor([3., 3., 3.,  ..., 3., 3., 3.], dtype=torch.float64)

We see a slight enchancment when utilizing PyTorch over Numpy, however we missed one essential level. We haven’t used the GPU as a result of our PyTorch tensor knowledge continues to be in CPU reminiscence. 

To maneuver the info to the GPU reminiscence, we have to add the machine='cuda' directive when creating the tensor. Let’s do this and see if it makes a distinction.

# Identical code as above besides 
# to get the array knowledge onto the GPU reminiscence
# we modified

a2 = pt.ones(n2,dtype=pt.float64)

# to

a2 = pt.ones(n2,dtype=pt.float64,machine='cuda')

After re-running with the modifications we get,

Timing with CPU:numpy 0.12852740001108032
Timing with GPU:pytorch 0.011292399998637848

a1 =  [2. 2. 2. ... 2. 2. 2.]
a2 =  tensor([3., 3., 3.,  ..., 3., 3., 3.], machine='cuda:0', dtype=torch.float64)

That’s extra prefer it, a higher than 10x velocity up. 

Instance 2—A barely extra complicated array operation.

For this instance, we’ll multiply multi-dimensional matrices utilizing the built-in matmul operations out there within the PyTorch and Numpy libraries. Every array can be 10000 x 10000 and include random floating-point numbers between 1 and 100.

# NUMPY first
import numpy as np
from timeit import default_timer as timer

# Set the seed for reproducibility
np.random.seed(0)
# Generate two 10000x10000 arrays of random floating level numbers between 1 and 100
A = np.random.uniform(low=1.0, excessive=100.0, measurement=(10000, 10000)).astype(np.float32)
B = np.random.uniform(low=1.0, excessive=100.0, measurement=(10000, 10000)).astype(np.float32)
# Carry out matrix multiplication
begin = timer() 
C = np.matmul(A, B)

# Because of the giant measurement of the matrices, it isn't sensible to print them solely.
# As an alternative, we print a small portion to confirm.
print("A small portion of the end result matrix:n", C[:5, :5])
print("With out GPU:", timer()-start)

A small portion of the end result matrix:
 [[25461280. 25168352. 25212526. 25303304. 25277884.]
 [25114760. 25197558. 25340074. 25341850. 25373122.]
 [25381820. 25326522. 25438612. 25596932. 25538602.]
 [25317282. 25223540. 25272242. 25551428. 25467986.]
 [25327290. 25527838. 25499606. 25657218. 25527856.]]

With out GPU: 1.4450852000009036

Now for the PyTorch model.

import torch
from timeit import default_timer as timer

# Set the seed for reproducibility
torch.manual_seed(0)

# Use the GPU
machine = 'cuda'

# Generate two 10000x10000 tensors of random floating level 
# numbers between 1 and 100 and transfer them to the GPU
#
A = torch.FloatTensor(10000, 10000).uniform_(1, 100).to(machine)
B = torch.FloatTensor(10000, 10000).uniform_(1, 100).to(machine)

# Carry out matrix multiplication
begin = timer()
C = torch.matmul(A, B)

# Await all present GPU operations to finish (synchronize)
torch.cuda.synchronize() 

# Because of the giant measurement of the matrices, it isn't sensible to print them solely.
# As an alternative, we print a small portion to confirm.
print("A small portion of the end result matrix:n", C[:5, :5])
print("With GPU:", timer() - begin)

A small portion of the end result matrix:
 [[25145748. 25495480. 25376196. 25446946. 25646938.]
 [25357524. 25678558. 25675806. 25459324. 25619908.]
 [25533988. 25632858. 25657696. 25616978. 25901294.]
 [25159630. 25230138. 25450480. 25221246. 25589418.]
 [24800246. 25145700. 25103040. 25012414. 25465890.]]

With GPU: 0.07081239999388345

The PyTorch run was 20 occasions higher this time than the NumPy run. Nice stuff.

Instance 3 — Combining CPU and GPU code.

Typically, not your entire processing could be completed on a GPU. An on a regular basis use case for that is graphing knowledge. Certain, you may manipulate your knowledge utilizing the GPU, however typically the following step is to see what your closing dataset seems to be like utilizing a plot.

You possibly can’t plot knowledge if it resides within the GPU reminiscence, so you could transfer it again to CPU reminiscence earlier than calling your plotting features. Is it well worth the overhead of transferring giant chunks of knowledge from the GPU to the CPU? Let’s discover out.

On this instance, we’ll resolve this polar equation for values of θ between 0 and 2π in (x, y) coordinate phrases after which plot out the ensuing graph.

Don’t get too hung up on the mathematics. It’s simply an equation that, when transformed to make use of the x, y coordinate system and solved, seems to be good when plotted.

For even just a few million values of x and y, Numpy can resolve this in milliseconds, so to make it a bit extra fascinating, we’ll use 100 million (x, y) coordinates.

Right here is the numpy code first.

%%time
import numpy as np
import matplotlib.pyplot as plt
from time import time as timer

begin = timer()

# create an array of 100M thetas between 0 and 2pi
theta = np.linspace(0, 2*np.pi, 100000000)

# our unique polar system
r = 1 + 3/4 * np.sin(3*theta)

# calculate the equal x and y's coordinates 
# for every theta
x = r * np.cos(theta)
y = r * np.sin(theta)

# see how lengthy the calc half took
print("Completed with calcs ", timer()-start)

# Now plot out the info
begin = timer()
plt.plot(x,y)

# see how lengthy the plotting half took
print("Completed with plot ", timer()-start)

Right here is the output. Would you might have guessed beforehand that it will seem like this? I positive wouldn’t have!

Now, let’s see what the equal PyTorch implementation seems to be like and the way a lot of a speed-up we get.

%%time
import torch as pt
import matplotlib.pyplot as plt
from time import time as timer

# Be sure PyTorch is utilizing the GPU
machine = 'cuda'

# Begin the timer
begin = timer()

# Creating the theta tensor on the GPU
theta = pt.linspace(0, 2 * pt.pi, 100000000, machine=machine)

# Calculating r, x, and y utilizing PyTorch operations on the GPU
r = 1 + 3/4 * pt.sin(3 * theta)
x = r * pt.cos(theta)
y = r * pt.sin(theta)

# Transferring the end result again to CPU for plotting
x_cpu = x.cpu().numpy()
y_cpu = y.cpu().numpy()

pt.cuda.synchronize()
print("Completed with calcs", timer() - begin)

# Plotting
begin = timer()
plt.plot(x_cpu, y_cpu)
plt.present()

print("Completed with plot", timer() - begin)

And our output once more.

The calculation half was about 10 occasions greater than the numpy calculation. The information plotting took across the identical time utilizing each the PyTorch and NumPy variations, which was anticipated for the reason that knowledge was nonetheless in CPU reminiscence then, and the GPU performed no additional half within the processing.

However, total, we shaved about 40% off the entire run-time, which is superb.

Abstract

This text has demonstrated easy methods to leverage an NVIDIA GPU utilizing PyTorch—a machine studying library sometimes used for AI purposes—to speed up non-ML numerical Python code. It compares commonplace NumPy (CPU-based) implementations with GPU-accelerated PyTorch equivalents to point out the efficiency advantages of operating tensor-based operations on a GPU.

You don’t should be doing machine studying to learn from PyTorch. When you can entry an NVIDIA GPU, PyTorch supplies a easy and efficient approach to considerably velocity up computationally intensive numerical operations—even in general-purpose Python code.