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CuPy

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Pros of CuPy
Pros of TensorFlow
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    • 29
      High Performance
    • 17
      Connect Research and Production
    • 14
      Deep Flexibility
    • 11
      Auto-Differentiation
    • 10
      True Portability
    • 4
      Powerful
    • 4
      High level abstraction
    • 4
      Easy to use

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    Cons of CuPy
    Cons of TensorFlow
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      • 9
        Hard
      • 6
        Hard to debug
      • 1
        Documentation not very helpful

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      What is CuPy?

      It is an open-source matrix library accelerated with NVIDIA CUDA. CuPy provides GPU accelerated computing with Python. It uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture.

      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.

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      Jobs that mention CuPy and TensorFlow as a desired skillset
      CBRE
      Philippines National Capital Region Makati City
      CBRE
      United Kingdom of Great Britain and Northern Ireland England London
      CBRE
      Philippines National Capital Region Makati City
      What companies use CuPy?
      What companies use TensorFlow?
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        What tools integrate with CuPy?
        What tools integrate with TensorFlow?

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        What are some alternatives to CuPy and TensorFlow?
        NumPy
        Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
        Numba
        It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.
        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.
        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.
        Pandas
        Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
        See all alternatives