Why Was Installing Tiny-cuda-nn & Nerfstudio On My RTX 3060 Such A Nightmare? (And How I Finally Fixed It)

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Why Was Installing tiny-cuda-nn & Nerfstudio on My RTX 3060 Such a Nightmare? (And How I Finally Fixed It)

Installing the popular deep learning frameworks tiny-cuda-nn and Nerfstudio on a Windows system with an NVIDIA RTX 3060 GPU can be a daunting task, especially when faced with a multitude of errors and compatibility issues. In this article, we will delve into the various problems that I encountered during the installation process and provide step-by-step solutions to overcome these obstacles.

System Specs & Versions

Before we dive into the troubleshooting process, let's take a look at the system specifications and versions that I used:

  • GPU: NVIDIA RTX 3060
  • CUDA Version: 11.8
  • Visual Studio Version: VS 2019 v17.8 (Worked!)
  • Python Version: 3.8

Issues Faced & Fixes

1️⃣ TCNN_CUDA_ARCHITECTURES Error

The first issue that I encountered was a compilation failure due to missing CUDA architectures. To resolve this, I set the CUDA architecture to 86 using the following command:

set TCNN_CUDA_ARCHITECTURES=86

This simple fix resolved the compilation issue and allowed me to proceed with the installation.

2️⃣ VS 2022 v17.10+ Not Working

The next issue that I faced was with Visual Studio 2022 v17.10+, which was not compatible with CUDA 11.8. To resolve this, I removed VS 2022 v17.10+ and installed VS 2022 Fall 2023 LTSC (v17.8). This change allowed me to use the correct version of Visual Studio and continue with the installation.

3️⃣ CMake and cl Not Recognized

The third issue that I encountered was with CMake and cl not being recognized as commands. To resolve this, I manually added the following paths to the system PATH:

set PATH=C:\Program Files\Microsoft Visual Studio\2022\Enterprise\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin;%PATH%
set PATH=C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools\VC\Tools\MSVC\14.38.33130\bin\Hostx64\x86;%PATH%

I then activated the MSVC environment using the following command:

call "C:\Program Files\Microsoft Visual Studio\2022\BuildTools\VC\Auxiliary\Build\vcvars64.bat"

This change allowed me to use CMake and cl correctly and continue with the installation.

4️⃣ Conda Environment Variables Not Set

The fourth issue that I faced was with Conda environment variables not being set properly. To resolve this, I manually set the following environment variables before installation:

set CUDAVER=11.8
set CUDA_HOME=%CONDA_PREFIX%
set CUDA_ROOT=%CONDA_PREFIX%
set PATH=%CONDA_PREFIX%\Library\bin;%PATH%
set LD_LIBRARY_PATH=%CONDA_PREFIX%\Library\lib;%LD_LIBRARY_PATH%
set LD_LIBRARY_PATH=%CONDA_PREFIX%\Library\lib64;%LD_LIBRARY_PATH%
set CUDA_HOST_COMPILER=C:\Program Files\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.3X.X\bin\Hostx64\x64\cl.exe

To make these settings permanent, I added them to my System Environment Variables.

5️⃣ Created a Conda Environment for Nerfstudio

To create a Conda environment for Nerfstudio, I used the following command:

conda create --name nerfstudio -y python=3.8
conda activate nerfstudio
python -m pip install --upgrade pip

This created a new Conda environment for Nerfstudio and updated pip to the latest version.

6️⃣ Installed PyTorch with CUDA 11.8

To install PyTorch with CUDA 11.8, I used the following command:

pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html

This installed PyTorch with CUDA 11.8 and its dependencies.

7️⃣ Installed tiny-cuda-nn via GitHub

To install tiny-cuda-nn via GitHub, I used the following command:

pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

This installed tiny-cuda-nn from the GitHub repository.

8️⃣ Installed Nerfstudio

Finally, to install Nerfstudio, I used the following command:

pip install nerfstudio

This installed Nerfstudio and its dependencies.

🎉 Success!

After following these steps, I was able to successfully install tiny-cuda-nn and Nerfstudio on my Windows system with an NVIDIA RTX 3060 GPU. I hope that this article helps someone else facing similar issues and provides a step-by-step guide to overcome these obstacles.
Q&A: Installing tiny-cuda-nn & Nerfstudio on My RTX 3060

In our previous article, we discussed the various issues that I encountered while installing tiny-cuda-nn and Nerfstudio on a Windows system with an NVIDIA RTX 3060 GPU. We also provided step-by-step solutions to overcome these obstacles. In this article, we will answer some frequently asked questions (FAQs) related to the installation process.

Q: What are the system requirements for installing tiny-cuda-nn and Nerfstudio?

A: The system requirements for installing tiny-cuda-nn and Nerfstudio are:

  • GPU: NVIDIA RTX 3060 or higher
  • CUDA Version: 11.8 or higher
  • Visual Studio Version: VS 2019 v17.8 or higher
  • Python Version: 3.8 or higher

Q: Why do I need to set the CUDA architecture to 86?

A: You need to set the CUDA architecture to 86 because tiny-cuda-nn requires this specific architecture to compile correctly. If you don't set the architecture to 86, you may encounter compilation errors.

Q: Why can't I use VS 2022 v17.10+ with CUDA 11.8?

A: You can't use VS 2022 v17.10+ with CUDA 11.8 because VS 2022 v17.10+ sets _MSC_VER = 1940, which is not compatible with CUDA 11.8. To resolve this, you need to use VS 2022 Fall 2023 LTSC (v17.8) or lower.

Q: Why do I need to manually add the CMake and cl paths to the system PATH?

A: You need to manually add the CMake and cl paths to the system PATH because these tools are not automatically recognized by the system. By adding the paths manually, you can ensure that CMake and cl are recognized correctly.

Q: Why do I need to activate the MSVC environment?

A: You need to activate the MSVC environment because it provides the necessary tools and settings for compiling and running C++ code. By activating the MSVC environment, you can ensure that your code is compiled and run correctly.

Q: Why do I need to set the Conda environment variables manually?

A: You need to set the Conda environment variables manually because Conda doesn't automatically set these variables. By setting the variables manually, you can ensure that your Conda environment is configured correctly.

Q: Why do I need to create a Conda environment for Nerfstudio?

A: You need to create a Conda environment for Nerfstudio because it requires a specific Python version and dependencies to run correctly. By creating a Conda environment, you can ensure that Nerfstudio is installed and run correctly.

Q: Why do I need to install PyTorch with CUDA 11.8?

A: You need to install PyTorch with CUDA 11.8 because it provides the necessary tools and dependencies for running deep learning models on your GPU. By installing PyTorch with CUDA 11.8, you can ensure that your deep learning models are run correctly.

Q: Why do I need to install tiny-cuda-nn via GitHub?

A: You need to install tiny-cuda-nn via GitHub because it provides the latest version of the library. By installing tiny-cuda-nn via GitHub, you can ensure that you have the latest version of the library.

Q: Why do I need to install Nerfstudio?

A: You need to install Nerfstudio because it provides a powerful tool for rendering 3D scenes and models. By installing Nerfstudio, you can ensure that you have the necessary tools for creating and rendering 3D content.

Q: What if I encounter errors during the installation process?

A: If you encounter errors during the installation process, you can try the following:

  • Check the installation instructions carefully to ensure that you are following the correct steps.
  • Search online for solutions to the specific error message you are encountering.
  • Reach out to the community or support team for assistance.

By following these FAQs and troubleshooting tips, you should be able to successfully install tiny-cuda-nn and Nerfstudio on your Windows system with an NVIDIA RTX 3060 GPU.