Malvina  O'Hara

Malvina O'Hara


TensorFlow and Keras GPU Support - CUDA GPU Setup

In this episode, we’ll discuss GPU support for TensorFlow and the integrated Keras API and how to get your code running with a GPU!


00:00 Welcome to DEEPLIZARD - Go to for learning resources
00:30 Help deeplizard add video timestamps - See example in the description
15:24 Collective Intelligence and the DEEPLIZARD HIVEMIND

What is GEEK

Buddha Community

TensorFlow and Keras GPU Support - CUDA GPU Setup
Hello Jay

Hello Jay


Keras vs. Tensorflow - Difference Between Tensorflow and Keras

Keras and Tensorflow are two very popular deep learning frameworks. Deep Learning practitioners most widely use Keras and Tensorflow. Both of these frameworks have large community support. Both of these frameworks capture a major fraction of deep learning production.

Which framework is better for us then?

This blog will be focusing on Keras Vs Tensorflow. There are some differences between Keras and Tensorflow, which will help you choose between the two. We will provide you better insights on both these frameworks.

What is Keras?

Keras is a high-level API built on the top of a backend engine. The backend engine may be either TensorFlow, theano, or CNTK. It provides the ease to build neural networks without worrying about the backend implementation of tensors and optimization methods.

Fast prototyping allows for more experiments. Using Keras developers can convert their algorithms into results in less time. It provides an abstraction overs lower level computations.

Major Applications of Keras

  • The performance of Keras is smooth on both CPU and GPU.
  • Keras provides modularity, flexibility to code, extensibility, and has an adaptation for innovation and research.
  • The pythonic nature of Keras makes it easy to explore and debug the code.

What is Tensorflow?

Tensorflow is a tool designed by Google for the deep learning developer community. The aim of TensorFlow was to make deep learning applications accessible to the people. It is an open-source library available on Github. It is one of the most famous libraries to experiment with deep learning. The popularity of TensorFlow is because of the ease of building and deployment of neural net models.

Major area of focus here is numerical computation. It was built keeping the processing computation power in mind. Therefore we can run TensorFlow applications on almost kind of computer.

Major applications of Tensorflow

  • From mobiles to embedded devices and distributed servers Tensorflow runs on all the platforms.
  • Tensorflow is the enterprise of solving real-world and real-time problems like image analysis, robotics, generating data, and NLP.
  • Developers are implementing tools for translation languages and the detection of skin cancers using Tensorflow.
  • Major projects using TensorFlow are Google translate, video detection, image recognition.

#keras tutorials #keras vs tensorflow #keras #tensorflow

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

Keras Tutorial - Ultimate Guide to Deep Learning - DataFlair

Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.

Keras Tutorial

Introduction to Keras

Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.

Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.

Characteristics of Keras

Keras has the following characteristics:

  • It is simple to use and consistent. Since we describe models in python, it is easy to code, compact, and easy to debug.
  • Keras is based on minimal substructure, it tries to minimize the user actions for common use cases.
  • Keras allows us to use multiple backends, provides GPU support on CUDA, and allows us to train models on multiple GPUs.
  • It offers a consistent API that provides necessary feedback when an error occurs.
  • Using Keras, you can customize the functionalities of your code up to a great extent. Even small customization makes a big change because these functionalities are deeply integrated with the low-level backend.

Benefits of using Keras

The following major benefits of using Keras over other deep learning frameworks are:

  • The simple API structure of Keras is designed for both new developers and experts.
  • The Keras interface is very user friendly and is pretty optimized for general use cases.
  • In Keras, you can write custom blocks to extend it.
  • Keras is the second most popular deep learning framework after TensorFlow.
  • Tensorflow also provides Keras implementation using its tf.keras module. You can access all the functionalities of Keras in TensorFlow using tf.keras.

Keras Installation

Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.

Starting with Keras

The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.

Keras Sequential model

Keras Functional API

It allows you to define more complex models.

#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras

Uriah  Dietrich

Uriah Dietrich


Step-By-Step Guide to Setup GPU with TensorFlow on Windows Laptop.

The very first and important step is to check which GPU card your laptop is using, based on the GPU card you need to select the correct version of CUDA, cuDNN, MSVC, Tensorflow etc. To check the GPU card on your windows 10 laptop follow below simple steps:

  • Right Click on your Desktop
  • Open Nvidia Control Panel
  • Goto Help -> System Information

#cudnn #cuda #tensorflow #gpu #geforce-gtx-1650

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