搭建GPU深度学习环境：TensorFlow 1.15 GPU兼容CUDA/cuDNN版本咨询

Hey there! I get it—tracking down compatibility for older TensorFlow versions can be a pain since they’re no longer front-and-center on the official docs. Let’s break down exactly what you need for a stable TF 1.15 GPU setup:

Official Recommended (Stable) Versions #

These are the versions TensorFlow 1.15 was explicitly tested against, so they’re your safest bet:

• CUDA Toolkit: 10.0 (Do not use 10.1, 10.2, or newer—TF 1.15’s GPU backend isn’t built to support these)
• cuDNN: 7.4.1 (This is the exact cuDNN version paired with CUDA 10.0 in TF 1.15’s official release notes)

Compatible Latest Versions #

If you want a slightly newer cuDNN with minor optimizations (still fully compatible), you can go with:

• cuDNN: 7.6.5 (This is the final release in the cuDNN 7.x series that works with CUDA 10.0. It maintains full compatibility with TF 1.15 while fixing some small bugs from earlier 7.x releases)

Quick Installation Tips #

• When installing CUDA 10.0, uncheck the "Visual Studio Integration" option unless you specifically need it—this avoids conflicts with other CUDA versions you might have installed later.
• After extracting cuDNN, copy its bin, include, and lib folders into your CUDA 10.0 installation directory (usually C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0 on Windows, or /usr/local/cuda-10.0 on Linux).
• To verify your setup works, run this command in your Python environment:

  import tensorflow as tf
  print(tf.test.is_gpu_available())
  

  If it returns True, your GPU environment is correctly configured!

内容的提问来源于stack exchange，提问作者Anjana Ouseph