First, install dependencies. On Ubuntu 18.04, open terminal:
sudo apt install libxi-dev libxmu-dev liblapack-dev libadolc2 coinor-libipopt1v5
Install OpenSim using conda:
conda create -n osim -c vbotics opensim=4.3 python=3.7 numpy
conda activate osim
git clone https://github.com/vbotics/rug-opensim-rl.git
cd rug-opensim-rl
git checkout tags/v3.0
pip install -e .
# For the visualizer, you will also need this.
conda install -c conda-forge libstdcxx-ng
For non-Ubuntu distributions, you will have to find a way to build OpenSim-core. See this or below for more information.
After activating the virtual environment, install additional dependencies. Run the following in a local library folder:
# Gym 0.24.0/1 worked well for me.
pip install python-dateutil pytz wandb gym
git clone https://github.com/rug-my-leg/opensim-env.git
cd opensim-env/
pip install -e .
Before running, update your LD_LIBRARY_PATH. This needs to be done only when you open a new terminal:
# For Conda environments
export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$CONDA_PREFIX/adol-c/lib64/:$CONDA_PREFIX/ipopt/lib/:$LD_LIBRARY_PATH
# For environments built from source. Make sure you replace /home/$USER with the correct path to the libraries.
export LD_LIBRARY_PATH=/home/$USER/.libs/opensim_dependencies/ipopt/lib:/home/$USER/.libs/opensim_dependencies/adol-c/lib64:$LD_LIBRARY_PATH
To use MPI, run the following:
# Add the argument '-n' to use a specific amount of workers. If running with SLURM, disregard this argument.
mpirun python -m src.train_mpi -c configs/healthy.yml
If you encounter issues when running the MPI version, try changing start_method="fork" from src/ppg/logging.py to start_method="thread".
To find a good set of hyperparameters for the model, src/sweep_mpi.py makes use of the wandb sweep function. Currently this is only implemented for the MPI version.
To begin a hyperparameter sweep, first initialize the configurations using the command below. This will create a number of new folders under output/sweep that contain a config file, as well as a sweeps.info file that contains the path of these configs. Before running it, make sure you create a sweep in your project on wandb and provide the given ID to the src/utils/init_sweep.py script. You can also adjust the number of generated configurations by providing the runs parameter.
wandb init
python -m src.utils.init_sweep --id <SWEEP_ID> --runs 25
Finally, to train all the models, one can use the script mentioned below. If you're running the project on Peregrine, simply submit run_sweep.sh to the job handler.
#!/bin/bash
while read -r line; do
if ! [[ -f "$(dirname "$line")/agent.pth" ]]; then
echo $line
mpirun --mca opal_warn_on_missing_libcuda 0 python -m src.sweep_mpi -c $line < /dev/null
else
echo "Config $line was already used for a sweep. Continuing..."
fi
done < output/sweep/sweeps.info
If you are running on your own machine and have a graphical user interface available, you can use the command below to visualize your trained model. This will instantiate the Python OpenSim visualizer.
python -m src.utils.visualize_model output/example_run_name
However, if you are running the code on the cluster or you do not have a graphical user interface, you can run the command below to generate a motion file. This file can then be imported in OpenSim, along with the model you used. Please take a look first at the command help to see what it can do.
python -m src.utils.generate_motion output/example_run_name
If you do not have access to the OpenSim visualizer, you can also generate a .csv file and visualize it using the playback script.
python -m src.utils.generate_motion output/example_run_name --csv
python -m src.utils.playback output/example_run_name/episode.csv --speed 0.2
If you already have access to the Peregrine cluster, you can run the commands below on one of the nodes to have access to the latest OpenSim version. Try to perform them one by one, rather than running the following as a script.
module load PyTorch/1.10.0-fosscuda-2020b CMake/3.20.1-GCCcore-10.2.0 Eigen/3.3.8-GCCcore-10.2.0 SWIG/4.0.2-GCCcore-10.2.0 OpenBLAS/0.3.12-GCC-10.2.0
mkdir /home/$USER/.libs
mkdir /home/$USER/.envs
python -m venv /home/$USER/.envs/osim
source /home/$USER/.envs/osim/bin/activate
pip install --upgrade pip
pip install --upgrade wheel
mkdir software
mkdir software/opensim
cd software/opensim
git clone https://github.com/opensim-org/opensim-core.git
mkdir build_deps/
cd build_deps/
cmake ../opensim-core/dependencies/ -LAH \
-DCMAKE_INSTALL_PREFIX=/home/$USER/.libs/opensim_dependencies \
-DCMAKE_BUILD_TYPE=Release \
-DSUPERBUILD_ezc3d=ON \
-DOPENSIM_WITH_TROPTER=ON \
-DOPENSIM_WITH_CASADI=ON
make -j8
cd ..
mkdir build/
cd build/
export docopt_DIR=/home/$USER/.libs/opensim_dependencies/docopt/lib64/cmake
export ezc3d_DIR=/home/$USER/.libs/opensim_dependencies/ezc3d/lib64/cmake
cmake ../opensim-core -LAH \
-DCMAKE_INSTALL_PREFIX=/home/$USER/.libs/opensim-core \
-DCMAKE_BUILD_TYPE=Release \
-DOPENSIM_DEPENDENCIES_DIR=/home/$USER/.libs/opensim_dependencies \
-DOPENSIM_C3D_PARSER=ezc3d \
-DBUILD_PYTHON_WRAPPING=ON \
-DSWIG_DIR=/software/software/SWIG/4.0.2-GCCcore-10.2.0/share/swig \
-DSWIG_EXECUTABLE=/software/software/SWIG/4.0.2-GCCcore-10.2.0/bin/swig \
-DOPENSIM_INSTALL_UNIX_FHS=OFF \
-DOPENSIM_DOXYGEN_USE_MATHJAX=OFF \
-DOPENSIM_SIMBODY_DOXYGEN_LOCATION="https://simbody.github.io/simtk.org/api_docs/simbody/latest/" \
-DCMAKE_CXX_FLAGS="-Wno-error"
make -j8
# Ignore the python_example failure
# If other errors occur, run ctest using the --rerun-failed flag
# If the errors persist after rerunning the tests, try making the build again.
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/$USER/.libs/opensim_dependencies/simbody/lib
ctest --parallel 8 --output-on-failure
# Ignore issues with doxygen
make -j8 install
export LD_LIBRARY_PATH=/home/$USER/.libs/opensim_dependencies/ipopt/lib:/home/$USER/.libs/opensim_dependencies/adol-c/lib64:$LD_LIBRARY_PATH
cd /home/$USER/.libs/opensim-core/sdk/Python/
pip install .
# We do not need the build files anymore.
rm -rvf /home/$USER/software
You can now continue installing the other dependencies mentioned above.
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