Installing TensorFlow with GPU Support on Windows

Installing TensorFlow with GPU Support on Windows

CPU’s can fetch data at a quicker rate but cannot deal with more data at a time as CPU has to make numerous iterations to primary memory to perform a basic task. Then again, GPU comes with its own devoted VRAM memory hence makes fewer calls to main memory subsequently is fast. Installing TensorFlow with GPU Support on Windows

CPU’s can fetch data at a quicker rate but cannot deal with more data at a time as CPU has to make numerous iterations to primary memory to perform a basic task. Then again, GPU comes with its own devoted VRAM memory hence makes fewer calls to main memory subsequently is fast.

You will require an NVIDIA graphics card that supports CUDA, as TensorFlow still just authoritatively supports CUDA (see here:  https://www.tensorflow.org/install/gpu).

In the event that you are on Linux or macOS, you can probably install a pre-made Docker image with GPU- supported TensorFlow. Be that as it may, for those of us on Windows, we have to do things the most difficult way possible.

Table of Contents

  • Prerequisites
  • Install Microsoft Visual C++ Compiler
  • Download and install the NVIDIA driver
  • Install CUDA Toolkit
  • Install cuDNN
  • Installing and setting up the GPU environment
  • Testing and verifying the installation of GPU

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