message: Add bot comment for cuda and windows related build and install issues.
diff: @@ -24,6 +24,64 @@ assignees:
  - amahendrakar
  - ravikyram
  - Saduf2019
  -# A list of assignees for
  +# A list of assignees for compiler folder
  compiler_assignees:
  - joker-eph
  +# Cuda Comment
  +cuda_comment: >
  + From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:
  + * For TF-GPU - See point 1
  + * For TF-CPU - See point 2
  + -----------------------------------------------------------------------------------------------
  +
  + **1. Installing **TensorFlow-GPU** (TF) prebuilt binaries**
  +
  +
  + Make sure you are using compatible TF and CUDA versions.
  + Please refer following TF version and CUDA version compatibility table.
  +
  + | TF | CUDA |
  +
  + | :-------------: | :-------------: |
  +
  + | 2.1.0 - 2.2.0 | 10.1 |
  +
  + | 1.13.1 - 2.0 | 10.0 |
  +
  + | 1.5.0 - 1.12.0 | 9.0 |
  +
  + * If you have above configuration and using _**Windows**_ platform -
  + * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.
  + * Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup).
  + * If you have above configuration and using _**Ubuntu/Linux**_ platform -
  + * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.
  + * Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).
  + * If error still persists then, apparently your CPU model does not support AVX instruction sets.
  + * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements).
  +
  + -----------------------------------------------------------------------------------------------
  +
  + **2. Installing **TensorFlow** (TF) CPU prebuilt binaries**
  +
  +
  + *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
  +
  +
  + Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
  +
  + Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
  +
  + * Try Google Colab to use TensorFlow.
  + * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install``` to install any other preferred TF version.
  + * It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
  + * All you need is a good internet connection and you are all set.
  + * Try to build TF from sources by changing CPU optimization flags.
  +
  + *Please let us know if this helps.*
  +
  +windows_comment: >
  + From the stack trace it looks like you are hitting windows path length limit.
  + * Try to disable path length limit on Windows 10.
  + * Refer [disable path length limit instructions guide.](https://mspoweruser.com/ntfs-260-character-windows-10/)
  +
  + Please let us know if this helps.
  