Install System Dependencies¶
The fastest way to set up dependencies for garage is via running the setup script.
Clone our repository (https://github.com/rlworkgroup/garage) and navigate to its directory.
A MuJoCo key is required for installation. You can get one here: https://www.roboti.us/license.html
Make sure you run these scripts from the root directory of the repo, not from the scripts directory.
- On Linux, run the following:
./scripts/setup_linux.sh --mjkey path-to-your-mjkey.txt --modify-bashrc
- On macOS, run the following:
./scripts/setup_macos.sh --mjkey path-to-your-mjkey.txt --modify-bashrc
Install Garage in a Python Environment¶
The script sets up pre-requisites for each platform, but does not install the Python package. We recommend you build your project using a Python environment manager which supports dependency resolution, such as pipenv, conda, or poetry. We test against pipenv and conda.
garage is also tested using virtualenv, but we recommend against building your project using virtualenv, because it has difficulty resolving dependency conflicts which may arise between garage and other packages in your project. You are of course free to install garage as a system-wide Python package using pip, but we don’t recommend this for the same reasons we recommend against using virtualenv.
NOTE: garage only supports Python 3.5+, so make sure you Python environment is using this or a later version.
pipenv --three # garage only supports Python 3.5+ pipenv install --pre garage # --pre required because garage has some dependencies with verion numbers <1.0
- conda (environment named “myenv”)
source activate myenv pip install garage
Alternatively, you can add garage in the pip section of your environment.yml
name: myenv channels: - conda-forge dependencies: - python>=3.5 - pip - pip - garage
- virtualenv (environment named “myenv”)
source myenv/bin/activate pip install garage
Extra Steps for Developers¶
If you plan on developing the garage repository, as opposed to simply using it as a library, you will probably prefer to install your copy of the garage repository as an editable library instead. After installing the pre-requisites using the instructions in Install System Dependencies, you should install garage in your environment as below.
cd path/to/garage/repo pipenv --three pipenv install --pre -e .[all,dev]
source activate myenv cd path/to/garage/repo pip install -e .[all,dev]
source myenv/bin/activate cd path/to/garage/repo pip install -e .[all,dev]
To enable GPU support, install the garage[gpu] extra package into your Python environment.
Before you run garage, you need to specify the directory for the CUDA library in environment variable
LD_LIBRARY_PATH. You may need to replace the directory conforming to your CUDA version accordingly. We recommend you add this to your shell profile (e.g. ~/.bashrc) for convenience.
You should now be able to use your GPU with TensorFlow and PyTorch.