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How To Use CPU Instead Of GPU in Tensorflow?

TensorFlow, a famous open-source library, can be used to do high-performance numerical calculations. Applications may be deployed on a variety of platforms, from CPUs and GPUs to smartphones and edge devices, thanks to its flexible architecture (CPUs and GPUs, and from desktop clusters to servers). But before going into the details, let’s clear the basics first.

What is Tensorflow?

TensorFlow is a free, open-source end-to-end framework for developing Machine Learning applications. It is a symbolic math toolbox that uses dataflow and differentiable programming to handle various tasks, including deep neural network training and inference. It allows programmers to build machine learning applications using various tools, frameworks, and community resources.

TensorFlow, developed by Google, is the most prominent deep learning package globally. Google uses machine learning in all its products to improve search, translation, picture captions, and recommendations.

How Does It Work?

TensorFlow allows you to design dataflow graphs and structures to express how data goes through a chart by taking inputs as a multi-dimensional array called Tensor. It will enable you to draw a flowchart of possible processes on these inputs, which flow in one way and out the other.

What Do You Need To Run Tensorflow?

There are three types of TensorFlow hardware and software requirements.

During the development phase, the learned type of AI. A PC or laptop is used for the majority of training. After the training process, Tensorflow can be employed on a variety of platforms. It can run on a Windows, macOS, or Linux desktop, as a web service in the cloud, and on mobile platforms like iOS and Android. You can train it on multiple machines and then execute it on a different system after it’s finished.

The model may be trained and run on both GPUs and CPUs. GPUs were explicitly designed for video games. In late 2010, Stanford researchers discovered that GPUs are also particularly good at matrix operations and algebra, making them extremely fast for specific jobs. Deep learning makes extensive use of matrix multiplication. TensorFlow is particularly fast at matrix multiplication because it was written in C++. Despite being created in C++, TensorFlow may be accessed and controlled using a variety of languages, the most popular of which is Python.

Finally, TensorFlow relies heavily on the TensorBoard. The TensorBoard can graphically and visually monitor TensorFlow.

CPU vs. GPU For Machine Learning

The decision between a CPU and a GPU for machine learning is based on your budget, the types of jobs you want to perform, and the amount of data you have. GPUs are ideal for deep learning training, particularly when dealing with large-scale challenges.

Although AI accelerators are designed specifically for machine learning applications, CPUs are always the cheapest. Not everyone can afford AI hardware accelerators for learning or deploying machine learning algorithms. CPUs are preferred by some machine learning algorithms over GPUs.

How To Use CPU Instead Of GPU Tensorflow?

If you are using the Tensorflow CPU, it will work directly on the CPU without your indulding.

To make sure that the GPU version of Tensorflow is running on the CPU:

import os
os.environ["CUDA_VISIBLE_DEVICES"]="-1"    
import tensorflow as tf


Machine Learning Operations preferred on CPUs

Systems used for training and inference involve tremendous memory for embedding layers.
Machine learning algorithms that do not need parallel computing, i.e., support vector machine algorithms and time-series data.
An algorithm that utilizes sequential data, for example, recurrent neural networks.
Algorithms that apply intensive branching.
Most data science algorithms deployed on cloud or Backend-as-a-service (BAAS) architectures

Conclusion:

We can’t leave the CPU out of any machine learning configuration since it acts as a conduit for data from the source to the GPU cores. If the CPU is weak and the GPU is powerful, the user may experience a CPU bottleneck. Faster data transport and processing are promised with stronger CPUs.

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