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 meant and predominantly used for machine studying (ML) purposes? 

Don’t fear for those who’re less than pace on the ins and outs of ML, since we gained’t be utilizing it on this article. As a substitute, I’ll present you how you can use the PyTorch library to entry and use the capabilities of your GPU. We’ll evaluate the run occasions of Python applications utilizing the favored numerical library NumPy, operating on the CPU, with equal code utilizing PyTorch on the GPU. 

Earlier than persevering with, let’s shortly 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 pictures in a body buffer meant for output to a show machine. Its utility as a fast picture manipulation machine was primarily based on its means to carry out many calculations concurrently, and it’s nonetheless used for that goal.

Nonetheless, GPUs have not too long ago 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 very 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 extensively used for pure language processing and pc imaginative and prescient purposes. Two of the primary causes that Pytorch can be utilized for GPU operations are,

• One in every of PyTorch’s core knowledge buildings 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 assist. PyTorch seamlessly integrates with CUDA, a parallel computing platform and programming mannequin developed by NVIDIA for common computing on its GPUS. This enables PyTorch to entry the GPU {hardware} immediately, accelerating numerical computations. CUDA will allow builders to make use of PyTorch to write down software program that totally utilises GPU acceleration.

In abstract, PyTorch’s assist for GPU operations by way of CUDA and its environment friendly tensor manipulation capabilities make it a superb software for creating GPU-accelerated Python features with excessive computational calls for. 

As we’ll present afterward, you don’t have to make use of PyTorch to develop machine studying fashions or practice giant language fashions.

In the remainder of this text, we’ll arrange our growth atmosphere, set up PyTorch and run by way of a couple of examples the place we’ll evaluate 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 examine your GPU, difficulty 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 | Risky 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 many others ..

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

Whereas PyTorch set up packages can embrace CUDA libraries, your system should nonetheless set up the suitable NVIDIA GPU drivers. These drivers are needed on your working system to speak with the graphics processing unit (GPU) {hardware}. The CUDA toolkit consists of drivers, however for those who’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 most recent drivers appropriate along with your system and GPU specs.

Establishing our growth atmosphere

As a greatest follow, we should always arrange a separate growth atmosphere for every undertaking. I take advantage of 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 (advisable) or Anaconda first. 

Please notice 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 atmosphere
(base) $ conda create -n pytorch_test python=3.11 -y

Now activate your new atmosphere.

(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 well-liked bundle supervisor, and CUDA model. 

Fortunately, Pytorch gives 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. From there, scroll down just a little till you see this,

Click on on every field within the applicable place on your system and specs. As you do, you’ll see that the command within the Run this Command output area adjustments dynamically. If you’re executed making your decisions, 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 sort 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 robotically, you’ll seemingly see a screenful of knowledge after the jupyter pocket book command.

Close to the underside, there will probably be a URL that you need to copy and paste into your browser to provoke the Jupyter Pocket book.

Your URL will probably be totally different to mine, nevertheless it 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 the same 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 examine 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 every part is okay, we will proceed to our examples. If not, return and examine your set up processes.

NB Within the timings under, I ran every of the Numpy and PyTorch processes a number of occasions in succession and took the most effective 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, equivalent one-dimensional arrays and carry out a easy addition to every array factor.

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 because 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) 
    #watch for 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 information to the GPU reminiscence, we have to add the machine='cuda' directive when creating the tensor. Let’s try 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 adjustments 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 better than 10x pace up. 

Instance 2—A barely extra complicated array operation.

For this instance, we’ll multiply multi-dimensional matrices utilizing the built-in matmul operations accessible within the PyTorch and Numpy libraries. Every array will probably be 10000 x 10000 and comprise 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, dimension=(10000, 10000)).astype(np.float32)
B = np.random.uniform(low=1.0, excessive=100.0, dimension=(10000, 10000)).astype(np.float32)
# Carry out matrix multiplication
begin = timer() 
C = np.matmul(A, B)

# As a result of giant dimension of the matrices, it isn't sensible to print them completely.
# As a substitute, 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() 

# As a result of giant dimension of the matrices, it isn't sensible to print them completely.
# As a substitute, 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.

Generally, not your whole processing might be executed on a GPU. An on a regular basis use case for that is graphing knowledge. Positive, you possibly can manipulate your knowledge utilizing the GPU, however usually the following step is to see what your ultimate dataset seems to be like utilizing a plot.

You may’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 shifting giant chunks of information from the GPU to the CPU? Let’s discover out.

On this instance, we’ll remedy 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 maths. 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 a couple of million values of x and y, Numpy can remedy this in milliseconds, so to make it a bit extra attention-grabbing, 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 components
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 information
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’ve guessed beforehand that it could appear like this? I certain 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

# Ensure 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 info plotting took across the identical time utilizing each the PyTorch and NumPy variations, which was anticipated because the 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 how you can 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 normal 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 have to be doing machine studying to learn from PyTorch. For those who can entry an NVIDIA GPU, PyTorch gives a easy and efficient technique to considerably pace up computationally intensive numerical operations—even in general-purpose Python code.