As you probably know, deep learning is a hot topic right now. There are many ways to use GPUs to train your neural networks, and it’s important to know what settings work best in most situations. While there isn’t one clear winner when it comes to GPU types, NVIDIA usually gets good reviews.
That’s why we have gathered some tips and tricks here to help you choose the best gpu type for your needs! This article will go into more detail about each of these options including their pros and cons so that you can pick which one is the best fit for you. Once done, you’ll be able to start experimenting with deeper learning!
The key difference between all of these different gpu types is how they are configured during training and inference times. Make sure to check out our previous articles to learn more! They’re really helpful as you begin working with GPUs.
Run benchmarks in different configurations
A very common way to test your GPU is by using an online benchmark tool or software package that will have you create models and tests as needed!
There are many free and paid tools that can do this, so there’s no need to buy expensive software if something else works well for you.
Some of the more popular ones include Caffeine (free), Google Cloud Machine Vision (paid) and TensorFlow-gpu (open source).
These sites have you upload your own model either pre-trained or trained yourself, and then run it through their built-in testing systems to see how fast it runs on your computer.
Run benchmarks on different GPUs
The most efficient way to find your best performing GPU is by doing repeated benchmark runs with different cards. Because each card has its own internal settings, you will get different results every time!
For example, if you are looking at whether or not to buy an NVIDIA 1080ti then you cannot just compare it to another 1070 because of their different levels of acceleration. You would have to test both graphics cards under the same conditions to determine which one is better.
Likewise, you can’t simply compare the GTX1080 ti to the gtx1070 due to differences in how they perform. Both cards will be using similar amounts of power, so you won’t know what percentage comes from the GPU and what percent comes from the other parts (like the VRM).
There are many free and paid resources that do deep learning related benchmarks. Some give you full specifications while others focus more on comparing performance.
Create a benchmarking plan
As mentioned earlier, choosing your benchmarks is very important! There are many ways to choose your benchmarks, and it depends on what you’re trying to test and how much time you have to devote to this project.
Luckily, creating a benchmark is pretty easy! You can pick any of the below tests from anywhere in the internet or create your own if none exist. Some people even make their own!
Once you have your test, simply run it through your GPU and compare the results with the same software running using an i7 CPU. This will give you accurate numbers that tell you exactly how fast your GPU is compared to another one.
Run benchmarks on your own data
As mentioned earlier, there are many ways to use GPUs for deep learning. One of the most common is using GPU-accelerated software that uses the same neural network architectures as you would like to implement in your model. These types of gpu softwares typically have benchmark modes or features where you can test different settings against each other to determine which one is faster.
By and large these tools only offer very simple setups though so it may be hard to get exact numbers. What they do tend to give however is a good indication whether the speedup comes from CPU to GPU transfer, the GPU itself, or both!
While some vendors will also let you run their preconfigured models directly through their website, this usually doesn’t work unless you already have an account with them, and even then it isn’t free. Try out our article here on how to start investing in the best gpu equipment for deep learning!
Another way to evaluate your current setup is looking at the defaults their tool offers. Most commonly seen are if you want to compare CUDA vs OpenCL performance, what kind of batch sizes perform better on either device type, and what kernel size and architecture works best depending on the problem domain.
Learn about different Gpu benchmarks
There are many ways to evaluate the performance of your GPU. Most common ones include using software such as PyTorch or Caffe to test models, comparing the results from one model to another, and running some quick tests to see how fast you can get a result!
With so much information floating around, it becomes difficult to know what is accurate and reliable. That’s why we have compiled an article with all the important info about gpu benchmarking!
In this article, you will learn about:
The basics of evaluating gpus for deep learning
Benchmarks that are considered trustworthy
Which types of GPUs perform well in which areas
How to compare gpu speeds quickly
So let’s dive in!
Basics of Evaluating Graphics Processing Units (GPU)s For Deep Learning
First things first, what is a GPU? A graphics processing unit is an integrated circuit designed specifically to do computerized graphical tasks like rendering movies, games, and other graphical applications.
A GPU typically has several hundred thousand very small transistors spread across its surface area. These transistors work together to speed up graphical calculations by parallelizing them. This means that instead of having one big processor calculate everything sequentially, there are hundreds or even thousands of little processors working simultaneously.
This dramatically increases the efficiency of the device due to parallelization. It also allows more features to be added because additional space is available on the chip.
Run benchmarks with different models
When it comes down to it, there is no one way to pick your gpu lite! Because you will never run out of ways to test GPUs, our best advice is to try as many things as possible.
There are several free and paid online benchmarking services that can help you compare various GPU types. Some only offer comparisons within their own suite while others have wider comparison tools.
Most of these sites allow you to upload your model or use theirs. After uploading, they will give you a quick performance estimate and also tell you which gpu types performed the best in past tests. You can then choose whether to purchase that specific card type or not depending on if those results match what you want!
General suggestions: If you do not have any experience using gpu lites, we recommend starting off by trying the least expensive ones first. Simply spending 5 dollars on a gpu lite can sometimes be the key difference between an effective machine and a wasted investment.
Test your GPU with Keras
For starters, you can test your GPU in Python by using one of the most common libraries for neural network training- Keras. You will want to make sure that you have installed keras correctly before starting!
There are 2 main ways to use Keras to train deep learning models. The first is through TensorFlow which is more commonly used at this time. However, there is an excellent alternative built right into Keras itself called Sequential API.
The second way to use Keras comes after model compilation where the trained weights from the previous layer need to be saved until later. This process is done via the compile() function which takes layers as input and outputs another layer.
By creating these repeated layerns, you get the ability to add additional features or even whole new networks very easily. After this, we can call compile().start(), which starts the sequential execution process.
This way uses GPUs very well because they have fast memory areas that can store data quickly. To check if your GPU has enough space, run free -m in Linux or cmd -> ctrl + alt + del in Windows to see all available disk space. If it says “GeForce GTX” then your computer probably does not have enough. Try adding some RAM to your machine to increase the speed.
Test your GPU with TensorFlow
A common way to test how well your gpu is performing is by using an open source tool called Tensorflow. You can use this to train different networks, or to benchmark already trained networks!
Tensorflow was designed to make it easy to run deep learning algorithms. By having all of the heavy lifting done for you, you no longer have to worry about coding neural network functions or working out kinks in their settings.
The best way to start is to pick one of the popular architectures that require lots of parallel processing like VGG or ResNet and see what kind of results you get.
You can also try experimenting with different batch sizes and number of epochs (iterations) to see which works the fastest and if they are any better than each other under similar conditions.