This documents provides more details on the installation process of EasyVolcap.
For basic installation, you should follow the guide in the main readme first since this doc is an extension to that.
Optionally if you only want to use EasyVolcap in other projects by directly importing its components, you can install it from GitHub with:
# Install core dependencies
pip install -v "git+https://github.com/zju3dv/EasyVolcap"
# Maybe also install development required dependencies (might require CUDA compilation)
pip install -v "git+https://github.com/zju3dv/EasyVolcap#egg=easyvolcap[devel]"Alternatively, if your pip install command fails due to one or two packages, try installing the dependencies one by one in this way:
# Install PyTorch
pip install torch torchvision torchaudio -f https://download.pytorch.org/whl/torch_stable.html
# Install pip dependencies
cat requirements.txt | sed -e '/^\s*-.*$/d' -e '/^\s*#.*$/d' -e '/^\s*$/d' | awk '{split($0, a, "#"); if (length(a) > 1) print a[1]; else print $0;}' | awk '{split($0, a, "@"); if (length(a) > 1) print a[2]; else print $0;}' | xargs -n 1 pip install
# Install development pip dependencies
cat requirements-devel.txt | sed -e '/^\s*-.*$/d' -e '/^\s*#.*$/d' -e '/^\s*$/d' | awk '{split($0, a, "#"); if (length(a) > 1) print a[1]; else print $0;}' | awk '{split($0, a, "@"); if (length(a) > 1) print a[2]; else print $0;}' | xargs -n 1 pip install # use this for full dependencies
# Register EasyVolcp for imports
pip install -e . --no-build-isolation --no-depsNote that the --no-build-isolation gives faster install by not creating a virtual environment for building dependencies.
But it does require the latest setuptools and pip to work correctly.
So if the result of running the pip install -e . --no-build-isolation --no-deps command contains a package name of UNKNOWN,
try updating setuptools and pip with:
python -m pip install -U pip setuptoolsCopy-and-paste version of the installation process listed below. For a more thorough explanation, read on.
# Prepare conda environment
conda install -n base mamba -y -c conda-forge
mamba create -n easyvolcap "python>=3.11,<3.12" -y
conda activate easyvolcap
# Install conda dependencies
mamba env update
# Install pip dependencies
cat requirements.txt | sed -e '/^\s*-.*$/d' -e '/^\s*#.*$/d' -e '/^\s*$/d' | awk '{split($0, a, "#"); if (length(a) > 1) print a[1]; else print $0;}' | awk '{split($0, a, "@"); if (length(a) > 1) print a[2]; else print $0;}' | xargs -n 1 pip install
cat requirements-devel.txt | sed -e '/^\s*-.*$/d' -e '/^\s*#.*$/d' -e '/^\s*$/d' | awk '{split($0, a, "#"); if (length(a) > 1) print a[1]; else print $0;}' | awk '{split($0, a, "@"); if (length(a) > 1) print a[2]; else print $0;}' | xargs -n 1 pip install # use this for full dependencies
# Register EasyVolcp for imports
pip install -e . --no-build-isolation --no-depsWe opted to use the latest pyproject.toml style packing system for exposing command line interfaces.
It creates a virtual environment for building dependencies by default, which could be quite slow. Disabled with --no-build-isolation.
You should create a conda or mamba (recommended) environment for development, and install the dependencies manually.
If the existing environment with PyTorch installed can be utilized, you can jump straight to installing the pip dependencies.
More details about installing on Windows or compiling CUDA modules can be found in install.md.
Note: pip dependencies can sometimes fail to install & build. However, not all of them are strictly required for EasyVolcap.
- The core ones include
tinycudannandpytorch3d. Make sure those are built correctly and you'll be able to use most of the functionality of EasyVolcap. - It's also OK to install missing packages manually when EasyVolcap reports that they are missing since we lazy load a lot of them (
tinycudann,diff_gauss,open3detc.). - Just be sure to check how we listed the missing package in
requirements.txtbefore performingpip installon them. Some packages require to be installed from GitHub. - If the
mamba env updatestep fails due to network issues, it is OK to proceed with pip installs sincePyTorchwill also be installed by pip.
If you encounter import errors, they can usually be safely ignored if the code runs since we lazy load a lot of the trikcy-to-install ones, especially when there's CUDA kernel compilation involved.
If the import error gets in the way of the actual code you want to use, you might want to search the reported package name in requirements-devel.txt and install them manually.
For example, for the missing diff_gauss package, you can find the line diff_gauss @ git+https://github.com/dendenxu/diff-gaussian-rasterization there and install it with:
pip install git+https://github.com/dendenxu/diff-gaussian-rasterization # will try to compile this package and install itHere, we provide a more thorough explanation of the installation process listed in the main readme.
Basically, what we does is create a separated conda environment, install PyTorch from conda and then install other dependencies one-by-one using pip.
The reason for the cat command is that pip install -r requirements.txt will fail even if only one of the dependencies failed to install and it quickly became infuriating to see an error message eating away 30 minutes of our lives.
# Clone this codebase with:
git clone https://github.com/zju3dv/EasyVolcap
cd EasyVolcap
# If you haven't installed conda,
# We recommend installing it from https://docs.conda.io/projects/miniconda/en/latest/
# On Ubuntu, these scripts can be used
# cd ~
# wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
# chmod +x Miniconda3-latest-Linux-x86_64.sh
# ./Miniconda3-latest-Linux-x86_64.sh # and finish the installation as the guide
# Maybe install mamba through conda
# The recommended way is to use mambaforge directly instead of installing miniconda3
conda install -n base mamba -c conda-forge -y
# Picks up on environment.yml and create a pytorch env
# mamba env create
# If this doesn't work, separate create and update as follows
# Note that as of 2023.04.21, wsl2 with python 3.10 could not correctly initialize or load opengl
# You need manually change the following command to accomodate for that (3.10 to 3.9)
# And update the environment.yml file to use python 3.9 instead of 3.10
# mamba create -n easyvolcap "python==3.10" -y
# mamba create -n easyvolcap "python>=3.10,<3.10" -y
mamba create -n easyvolcap "python>=3.11,<3.12" -y
conda activate easyvolcap
mamba env update # picks up environment.yml
# With the environment created, install other dependencies
# And possibly modify .zshrc to automatically activate this env
# echo conda activate easyvolcap >> ~/.zshrc
# source ~/.zshrc # actually not needed (already activated)
# Install all pip dependencies one by one in case some fails
# pip dependencies might fail to install, check the root cause and try this script again
# Possible caveats: cuda version? pytorch version? python version?
cat requirements-devel.txt | sed -e '/^\s*#.*$/d' -e '/^\s*$/d' | awk '{split($0, a, "@"); if (length(a) > 1) print a[2]; else print $0;}' | xargs -n 1 pip install # use this for full dependencies
# Registers command-line interfaces like `evc` `evc-train` `evc-test` etc.
pip install -e . --no-build-isolation --no-depsNote that the provided examples in the main readme uses an HDR video, thus you might also need to install a version of ffmpeg that supports the zscale filter.
sudo add-apt-repository ppa:savoury1/ffmpeg4
sudo apt update
sudo apt install ffmpegTo use any compiled CUDAExtension, you need to have the CUDA Toolkit and PyTorch installed and configured.
For PyTorch, running pip install -e . (the non-dev install of EasyVolcap) will have already taken care of that.
For the CUDA Toolkit, typically your system administration would have already done so if you're using a shared server for AI related research. Check under /usr/local to find anything related to CUDA.
Then, add these lines to your .zshrc or .bashrc to expose related paths for compilation:
# CUDA related configs
export PATH="/usr/local/cuda/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH"
export CUDA_HOME="/usr/local/cuda"
export CUDA_DEVICE_ORDER=PCI_BUS_ID # OPTIONAL: defaults to capability order, might be different for GL and CUDALet's go through the compilation process with the tinycudann package.
To retain the compiled objects without starting over in case anything fails, we recommend first cloning tinycudann then performing the compilation and installation manually:
cd ~
git clone https://github.com/NVlabs/tiny-cuda-nn --recursive
cd tiny-cuda-nn/bindings/torch
pip install .Or maybe try to install tiny-cuda-nn with:
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torchThis is automatically done when installing dependencies for EasyVolcap using this command above:
# Install all pip dependencies one by one in case some fails
cat requirements.txt | sed -e '/^\s*#.*$/d' -e '/^\s*$/d' | xargs -n 1 pip installIf you still encounter cannot find -lcuda: No such file or directory error after setting the above paths, try executing
# ln -s /usr/lib/x86_64-linux-gnu/libcuda.so ~/anaconda3/envs/easyvolcap/lib/libcuda.so
ln -s /usr/lib/x86_64-linux-gnu/libcuda.so ~/miniconda3/envs/easyvolcap/lib/libcuda.soand then install tiny-cuda-nn with: pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch.
Sometimes (diff-gaussian-rasterization), on some systems, the import order of CUDA-compiled extension and PyTorch matters.
On some of our systems, if you import PyTorch before the CUDA-compiled extension, it will fail to find some Python symbols.
Try changing the orders if that happens.
python -c "from diff_gaussian_rasterization import _C" # works
python -c "import torch; from diff_gaussian_rasterization import _C" # might fail
python -c "from diff_gaussian_rasterization import _C; import torch" # should workAs discussed in this issue, the installation commands are tailored for Linux systems but EasyVolcap's dependency requirements are very loose. Aside from PyTorch, which you can install in anyway you like following their official guide or just reuse your previous environment, also other packages are related to the specific functionality or algorithm you want to run.
During the initialization in the main.py script, we will try to recursively import user-defined components from the easyvolcap folder (also check config.py). Some warnings about missing packages might be raised, but as long as you don't use that functionality, it should be fine.
For example, the real-time viewer does not require packages like pytorch3d or open3d so you could ignore them during import.
We've also tested the viewer functionality (run evc-gui to test) on Windows, which requires the following OpenGL and ImGUI-related packages:
torch
pdbr
ujson
PyGLM
pyperclip
ruamel.yaml
imgui-bundle
opencv-python
These packages should be able to be installed directly from the command line using pip.
Sidenote: on macOS (Mac), there's no NVIDIA GPU. We use the CUDA-GL interface to transfer rendered images (a PyTorch tensor, which is a block of CUDA memory) onto the screen (a textured-based framebuffer). Thus the real-time viewer isn't fully supported on Mac yet.