Implementation of STAC using MJX. This is part of the VNL project.
stac-mjx relies on many prerequisites, therefore we suggest installing in a new conda environment, using the provided environment.yaml:
Create and activate the stac-mjx-env environment:
conda env create -f environment.yaml
conda activate stac-mjx-env
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Update the .yaml files in
config/with the proper information (details WIP). -
For new data, first run stac on just a small subset of the data with
python stac_mjx/main.py test.skip_transform=TrueNote: this currently will fail w/o supplying a data file.
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Render the resulting data using
mujoco_viz()from withinviz_usage.ipynb. Currently, this uses headless rendering on CPU viaosmesa, which requires its own setup. To set up (currently on supported on Linux), execute the following commands sequentially:sudo apt-get install libglfw3 libglew2.0 libgl1-mesa-glx libosmesa6 conda install -c conda-forge glew conda install -c conda-forge mesalib conda install -c anaconda mesa-libgl-cos6-x86_64 conda install -c menpo glfw3Finally, set the following environment variables, and reactivate the conda environment:
conda env config vars set MUJOCO_GL=osmesa PYOPENGL_PLATFORM=osmesa conda deactivate && conda activate baseTo ensure all of the above changes are encapsulated in your Jupyter kernel, a create a new kernel with:
conda install ipykernel python -m ipykernel install --user --name stac-mjx-env --display-name "Python (stac-mjx-env)" -
After tuning parameters and confirming the small clip is processed well, run through the whole thing with
python stac-mjx/main.py