Use Your CPU Instead of a GPU with TensorFlow

Use Your CPU Instead of a GPU with TensorFlow

TensorFlow is a powerful tool that can be used without a GPU. Read on to find out how to use your CPU to train models with TensorFlow.

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TensorFlow is a powerful tool for machine learning, but it can be difficult to get started with. One of the biggest issues is that TensorFlow requires a GPU in order to run efficiently. This can be a problem for people who don’t have access to a GPU, or who don’t want to spend the money on one.

Luckily, there is a way to use TensorFlow without a GPU. This is possible because TensorFlow is designed to be run on multiple CPUs. by using multiple CPUs, you can still get all of the benefits of TensorFlow without needing a GPU.

There are some trade-offs to using TensorFlow on CPUs instead of GPUs. CPUs are generally not as fast as GPUs when it comes to machine learning. However, they are usually much cheaper, and they can be more widely available. In addition, using CPUs can sometimes be easier than using GPUs, because you don’t have to worry about installing special drivers or configuring your system in order to use them.

If you’re interested in using TensorFlow without a GPU, there are a few things you need to know. First, you need to install TensorFlow on your computer. Second, you need to configure TensorFlow to use multiple CPUs instead of aGPU. And finally, you need to make sure that your code is written in such a way that it will take advantage of multiple CPUs.

Installing TensorFlow is fairly straightforward. The first step is to download the installation package from the TensorFlow website ( Next, you need to unzip the installation package and navigate to the “tensorflow” folder inside it. Finally, you need to run the “configure” script inside the “tensorflow” folder. This will prompt you for various options; choose the “multi_cpu” option when prompted about which type of CPU architecture you want to use.

Configuring TensorFlow to use multiple CPUs is just as easy as installing it; all you need do is add one line of code to your program (or script). The line of code you need is: config = tf . ConfigProto ( device_count = { “CPU”: 4 }) This line tells TensorFlow that you want it too use four CPU cores instead of just one or two (the default).

OnceTensorFlowis installed and configuredto usemultipleCPUs,youwill needto makea fewchangesto your codeto takeadvantageof themultiplecores . The bestwayto dothisisdividetheworkloadintomultiplepiecesandhave eachpieceusetwo or threeof theCPU cores . For example , ifyouha vea trainingalgorithmthatneedsto iterateover1 million datapointswould normallybe slowonasinglecore ,butifyoudivideitinto1 0pieces ,each piececan iterateover100 000 datapointsindependentlyon its owncore ,and thenall 10piecescan come backtogetherand average their resultstogettothe finalanswerquickerthanif theyhad all triedto doittoogetherononecore .

What is TensorFlow?

TensorFlow is an open source machine learning platform that allows users to build and train neural networks. It is commonly used for deep learning applications such as image and video recognition, natural language processing, and pattern recognition. TensorFlow can be used on a CPU or GPU, but it is typically faster on a GPU.

What is a CPU?

A CPU (central processing unit) is a computer processor that can execute code and perform calculations. CPUs are typically found in desktop and laptop computers, but they can also be found in servers, mobile devices, and embedded systems. TensorFlow is a platform for machine learning that can be used with CPUs.

What is a GPU?

A Graphics Processing Unit (GPU) is a type of processor that is designed specifically for computer graphics and video processing. GPUs are usually found in computers with a dedicated graphics card, but they can also be found in some high-end CPUs.

GPUs are designed to perform many operations in parallel, which makes them well suited for tasks that require large amounts of data to be processed quickly, such as video games and video editing. However, this parallel processing power can also be harnessed for other purposes, such as machine learning.

Machine learning is a type of artificial intelligence that involves training computers to recognize patterns in data. This data can be anything from images to text to audio signals. The more data that is used for training, the better the results will be.

GPUs are well suited for machine learning because they can perform many operations in parallel. This means that more data can be processed in a shorter amount of time, which leads to better results.

TensorFlow is a machine learning platform that was developed by Google. It includes a library of tools that allow developers to create and train machine learning models. TensorFlow also has the ability to run on GPUs, which makes it a good choice for anyone who wants to use their CPU for other tasks while still being able to harness the power of GPUs for machine learning.

Why use a CPU over a GPU with TensorFlow?

Computers are normally built with a Graphics Processing Unit (GPU) as well as a Central Processing Unit (CPU). GPUs were originally designed to speed up computer graphics but they turn out to be very efficient at processing large amounts of data in parallel.

TensorFlow is a library for numerical computation that is often used in Machine Learning. TensorFlow can take advantage of the GPU for faster computation but sometimes it may be beneficial to use the CPU instead.

There are a few reasons why you might want to use the CPU over the GPU with TensorFlow:
-The CPU can sometimes be faster than the GPU for certain operations.
-The CPU can be easier to work with because it has more flexibility and is more widely available than GPUs.
-The CPU can be more power efficient than the GPU.

How to use a CPU with TensorFlow?


If you’re just getting started with TensorFlow, then it’s probably best to stick with using the CPU for now. It’s easy to set up and you don’t need a powerful GPU to get started. In this article, we’ll show you how to use a CPU with TensorFlow.

First, let’s take a look at why you might want to use a CPU with TensorFlow. A CPU is good for training simple models or for experimentation. If you’re just trying out TensorFlow, then it’s probably best to use a CPU. A CPU is also good if you’re training a small model or if you don’t have a lot of data.

Now that we’ve looked at some of the reasons why you might want to use a CPU with TensorFlow, let’s take a look at how to set it up. First, we need to install TensorFlow. You can do this using pip:

pip install tensorflow==2.0.0-alpha0

Once TensorFlow is installed, we can start using it. We’ll create a simple model that we can train on the MNIST dataset:

CPU vs GPU with TensorFlow

GPUs are designed to have a large number of cores so they can handle parallel tasks efficiently. This is why GPUs are good for deep learning tasks that involve matrix operations on large data sets. However, CPUs are more versatile and can handle a wider range of tasks.

TensorFlow is a tool that allows you to create complex algorithms and run them on different hardware devices, including CPUs and GPUs. In most cases, you will want to use a GPU for training your models because it will be faster than using a CPU. However, there are some cases where using a CPU may be better.

If you need to use a model that is not compatible with GPUs or if you want to use a model that is not yet supported by TensorFlow, then you will need to use a CPU. You may also want to use a CPU if you are training on a small data set or if you want to train your model faster.


In this article, we explored how to use TensorFlow on a CPU instead of a GPU. We looked at the pros and cons of using a CPU vs. a GPU and saw that CPUs can offer several advantages, including:

– More control over resource utilization
– More flexibility when it comes to development and debugging
– Potentially lower cost

Further Reading

If you want to learn more about using your CPU instead of a GPU with TensorFlow, here are some great resources:

-The Official TensorFlow Documentation on Using CPUs:
-A Tutorial on Using CPUs for Deep Learning:
-Stack Overflow Discussion on Using CPUs for Deep Learning:



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