Adam Carter

Adam Carter

1614111720

Tensorflow-GPU Installation with CUDA & CuDNN

What is a Tensorflow ?

Tensorflow is a software library or framework, designed by the Google team to implement machine learning and deep learning concepts in the easiest manner. It combines the computational algebra of optimization techniques for easy calculation of many mathematical expressions.

This is a official website of TensorFlow : www.tensorflow.org

Let us now consider the following important features of TensorFlow:

  • It includes a feature of that defines, optimizes and calculates mathematical expressions easily with the help of multi-dimensional arrays called tensors.
  • It includes a programming support of deep neural networks and machine learning techniques.
  • It includes a high scalable feature of computation with various data sets.
  • TensorFlow uses GPU computing, automating management. It also includes a unique feature of optimization of same memory and the data used.

Why is TensorFlow So Popular?

TensorFlow is well-documented and includes plenty of machine learning libraries. It offers a few important functionalities and methods for the same.

TensorFlow is also called a “Google” product. It includes a variety of machine learning and deep learning algorithms. TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embedding and creation of various sequence models.

#cudnn #cuda #tensorflow #deep-learning

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Tensorflow-GPU Installation with CUDA & CuDNN

Installing Tensorflow GPU on Anaconda

So, I was trying to use my GPU for the running a tensorflow code, but everytime it would run on CPU. I updated my Nvidia drivers, CUDA Toolkit and my tensorflow without any luck.

After a lot web search and trials i was finally able to get codes to run on GPU instead of CPU. I had to try many different codes and tricks from various sources. It took me more than a month to get the right codes and process. So i thought of putting it all in a single page so that someone new doesn’t has to struggle.

I have a Nvidia GeForce 940M graphics card, and I have Python 3.7 version and Anaconda 4.8.3. Assuming that you already have or know how to install Anaconda and Python, lets get started with installing Tensorflow-GPU. First you need to check if your graphics card supports CUDA. This can be checked on the Nvidia website link: https://developer.nvidia.com/cuda-gpus

#tensorflow #installation #cudnn #gpu #cuda

The Easy-Peasy Tensorflow-GPU Installation(Tensorflow 2.1) on Windows 10

Installing Tensorflow for GPU is an immensely complicated task that will drive you crazy. There are n-number of tutorials online that claims their way of doing things is the most efficient one. Despite their presence, I had a hard time getting stuff done as installing Tensorflow 2.1.0 is a bit different than its predecessor(Tensorflow 1). A minor difference in code will trigger AttributeError. So once I have succeeded, the very thought was to share my experience as a blog elaborating on the process.

The Easy Ways of Installation

The below-mentioned steps will definitely make your life easy:

  • To start with, it is advisable to verify your GPU as a CUDA compatible one. You can verify it here.
  • If Python is not yet installed, you may download it here.
  • Once GPU is found to be compatible, you are required to download the CUDA toolkit from the NVIDIA website. It’s mandatory to restart the OS(Windows 10) after installing the toolkit.
  • Open the Environment Variables by typing the term ‘environment variables’ in Windows 10 search bar in the taskbar, and select ‘Edit the system environment variables’. After installing the CUDA toolkit, I have to manually enter the CUDA_HOME variable. The other two variables-CUDA_PATH and CUDA_PATH_V11_0 were already present in the System variables list. Note that the three variables viz. CUDA_HOME, CUDA_PATH, and CUDA_PATH_V11_0 have the same variable value(C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0). If properly installed the System variables will have the three variables(paths) as highlighted by the red stroke as shown below

#gpu #python3 #tensorflow #cudnn #cuda #python

Installing TensorFlow GPU (Updated)

Good News!!..…TensorFlow providing in-built GPU.

Great, but how to install it ?? Don’t worry we will crack this in 2 Steps.

Note: This could be done only if the system has Nvidia Graphics Card (Must and should)

Come on let’s jump into it.

Why GPU’s ?

Using GPU’s we could run our neural network problems so comfortably not wasting time on unusual things (I mean sitting all the day to train them by watching epoch by epoch).

So, in this blog we would like to know the installation process very easily (Trust me) in 2 steps.

Steps :

  1. CUDA installation
  2. TensorFlow installation

#data-science #tensorflow #gpu-computing #ai #tensorflow-gpu #cuda

Adam Carter

Adam Carter

1614111720

Tensorflow-GPU Installation with CUDA & CuDNN

What is a Tensorflow ?

Tensorflow is a software library or framework, designed by the Google team to implement machine learning and deep learning concepts in the easiest manner. It combines the computational algebra of optimization techniques for easy calculation of many mathematical expressions.

This is a official website of TensorFlow : www.tensorflow.org

Let us now consider the following important features of TensorFlow:

  • It includes a feature of that defines, optimizes and calculates mathematical expressions easily with the help of multi-dimensional arrays called tensors.
  • It includes a programming support of deep neural networks and machine learning techniques.
  • It includes a high scalable feature of computation with various data sets.
  • TensorFlow uses GPU computing, automating management. It also includes a unique feature of optimization of same memory and the data used.

Why is TensorFlow So Popular?

TensorFlow is well-documented and includes plenty of machine learning libraries. It offers a few important functionalities and methods for the same.

TensorFlow is also called a “Google” product. It includes a variety of machine learning and deep learning algorithms. TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embedding and creation of various sequence models.

#cudnn #cuda #tensorflow #deep-learning

Installing CUDA and cuDNN on Windows

Libraries like Tensorflow and OpenCV are optimized for working with GPU. For these libraries to communicate with GPU we install CUDA and cuDNN, provided GPU is CUDA compatible. There are different versions of CUDA depending upon the architecture and model of GPU.

So, during the installation of CUDA, we need to first find its suitable version which is compatible with our machine’s GPU.

Finding suitable versions

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#opencv #cudnn #gpu #cuda #tensorflow