Update README and Troubleshooting for GCC versions

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duskvirkus · Oct 16, 2021 · eb91512 · eb91512
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diff --git a/README.md b/README.md
@@ -64,6 +64,7 @@ While new generator approaches enable new media synthesis capabilities, they may
 * 1&ndash;8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using Tesla V100 and A100 GPUs.
 * 64-bit Python 3.8 and PyTorch 1.9.0 (or later). See https://pytorch.org for PyTorch install instructions.
 * CUDA toolkit 11.1 or later.  (Why is a separate CUDA toolkit installation required?  See [Troubleshooting](./docs/troubleshooting.md#why-is-cuda-toolkit-installation-necessary)).
+- GCC 7 or later (Linux) or Visual Studio (Windows) compilers.  Recommended GCC version depends on CUDA version, see for example [CUDA 11.4 system requirements](https://docs.nvidia.com/cuda/archive/11.4.1/cuda-installation-guide-linux/index.html#system-requirements).
 * Python libraries: see [environment.yml](./environment.yml) for exact library dependencies.  You can use the following commands with Miniconda3 to create and activate your StyleGAN3 Python environment:
   - `conda env create -f environment.yml`
   - `conda activate stylegan3`

diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md
@@ -18,6 +18,7 @@ Make sure you've installed everything listed on the requirements section in the
 - **ninja**
   - PyTorch uses [Ninja](https://ninja-build.org/) as its build system.
 - **GCC** (Linux) or **Visual Studio** (Windows)
+  - GCC 7.x or later is required.  Earlier versions such as GCC 6.3 [are known not to work](https://github.com/NVlabs/stylegan3/issues/2).
 
 #### Why is CUDA toolkit installation necessary?