Notes on Tensorflow-GPU

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If you don’t need tensorflow-gpu but just tensorflow, you may safely ignore this post.

If you need tensorflow-gpu, according to its software requirement, you need the following:

  • NVIDIA proprietary driver, also includes CUPTI
  • CUDA Toolkit 10.0
  • cuDNN
  • (Optional) TensorRT 5.0

The last two are easy to install and this post focus on NVIDIA driver and CUDA toolkit.

First, CUDA 10.0 requires NVIDIA driver version >= 410.48, according to this.

Then, depending on whether you need CUDA simply as a runtime dependency for tensorflow and other softwares, or you need it as a build-time dependency for your own development, go to one of the two sections below.

CUDA for Runtime Dependency

  • Install the latest NVIDIA driver, without setting no-modprobe during installation. Otherwise tensorflow may fail to detect your CUDA device.
  • Download CUDA 10.0, which is a run file, containing an old version of NVIDIA driver, the CUDA toolkit, and other component. You can extract it and get the CUDA Toolkit itself. Then install it.

Note: During installation of CUDA, you need to tell it to skip building CUDA examples.

CUDA for Built-time Dependency

According to Installation guide of CUDA 10.0, you need kernel <=4 and gcc <=7. Fortunately this is outdated.

The guide of CUDA 10.1 shows that, it works on kernel =5 and gcc <=7. Previously I had verified that 10.0 also works with this configuration.

LTS kernel

If you are on the LTS kernel, your kernel version is 4 and you can install CUDA without any hassle.

Native kernel

If you are on native kernel, which has version 5.3.7 now, you have to install gcc7, which is available in c-extras-gcc7 bundle. Then follow the same steps mentioned above.

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