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CUDA

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110
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0
TensorFlow

2.4K
2.6K
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75
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CUDA vs TensorFlow: What are the differences?

What is CUDA? It provides everything you need to develop GPU-accelerated applications. A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.

What is TensorFlow? Open Source Software Library for Machine Intelligence. TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

CUDA and TensorFlow belong to "Machine Learning Tools" category of the tech stack.

According to the StackShare community, TensorFlow has a broader approval, being mentioned in 259 company stacks & 742 developers stacks; compared to CUDA, which is listed in 13 company stacks and 13 developer stacks.

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Pros of CUDA
Pros of TensorFlow
    Be the first to leave a pro
    • 23
      High Performance
    • 16
      Connect Research and Production
    • 13
      Deep Flexibility
    • 9
      Auto-Differentiation
    • 9
      True Portability
    • 2
      Easy to use
    • 2
      High level abstraction
    • 1
      Powerful

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    Cons of CUDA
    Cons of TensorFlow
      Be the first to leave a con
      • 8
        Hard
      • 5
        Hard to debug
      • 1
        Documentation not very helpful

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      What is CUDA?

      A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.

      What is TensorFlow?

      TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use CUDA?
      What companies use TensorFlow?
      See which teams inside your own company are using CUDA or TensorFlow.
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      What tools integrate with CUDA?
      What tools integrate with TensorFlow?

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      What are some alternatives to CUDA and TensorFlow?
      OpenCL
      It is the open, royalty-free standard for cross-platform, parallel programming of diverse processors found in personal computers, servers, mobile devices and embedded platforms. It greatly improves the speed and responsiveness of a wide spectrum of applications in numerous market categories including gaming and entertainment titles, scientific and medical software, professional creative tools, vision processing, and neural network training and inferencing.
      OpenGL
      It is a cross-language, cross-platform application programming interface for rendering 2D and 3D vector graphics. The API is typically used to interact with a graphics processing unit, to achieve hardware-accelerated rendering.
      Keras
      Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
      PyTorch
      PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
      scikit-learn
      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
      See all alternatives
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