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Installation of AllenAct#

Clone the repository to your local machine and move into the top-level directory

git clone
cd allenact

Note 1: This library has been tested only in python 3.6. The following assumes you have a working version of python 3.6 installed locally.

Note 2: If you are installing allenact intending to use a GPU for training/inference and your current machine uses an older version of CUDA you may need to manually install the version of PyTorch that supports your CUDA version. In such a case, after installing the below requirements, you should follow the directions for installing PyTorch with older versions of CUDA available on the PyTorch homepage.

In order to install requirements we recommend creating a new python virtual environment and installing all of the below requirements into this virtual environment. Several tools exist to help manage virtual environments, we have had success in using pipenv and so provide instructions for installing the requirements using pipenv but also include instructions if you would prefer to install everything directly using pip.

Installing requirements with pipenv#

If you have already installed pipenv, you may run the following to install all requirements.

pipenv install --skip-lock --dev

Please see the documentation of pipenv to understand how to use the newly created virtual environment.

Installing requirements with pip#

Note: do not run the following if you have already installed requirements with pipenv as above. If you prefer managing your environment manually, all requirements may be installed using pip by running the following command:

pip install -r requirements.txt

Depending on your machine configuration, you may need to use pip3 instead of pip in the above.

Installing requirements with conda (experimental)#

This below is currently experimental and is provided without a guarantee of continued support.

This folder contains YAML files specifying Conda environments compatible with AllenAct. These environment files include:

  • environment-base.yml - A base environment file to be used on machines where GPU support is not needed (everything will be run on the CPU).
  • environment-<CUDA_VERSION>.yml - where <CUDA_VERSION> is the CUDA version used on your machine (if you are using linux, you can generally find this version by running /usr/local/cuda/bin/nvcc --version).

Installing a Conda environment (experimental)#

If you are unfamiliar with Conda, please familiarize yourself with their introductory documentation. If you have not already, you will need to first install Conda (i.e. Anaconda or Miniconda) on your machine. We suggest installing Miniconda as it's relatively lightweight.

For the moment let's assume you're using environment-base.yml above. To install a conda environment with name allenact using this file you can simply run the following (this will take a few minutes):

conda env create --file ./conda/environment-base.yml --name allenact

The above is very simple but has the side effect of creating a new src directory where it will place some of AllenAct's dependencies. To get around this, instead of running the above you can instead run the commands:

export MY_ENV_NAME=allenact
export CONDA_BASE="$(dirname $(dirname "${CONDA_EXE}"))"
PIP_SRC="${CONDA_BASE}/envs/${MY_ENV_NAME}/pipsrc" conda env create --file ./conda/environment-base.yml --name $MY_ENV_NAME

These additional commands tell conda to place these dependencies under the ${CONDA_BASE}/envs/${MY_ENV_NAME}/pipsrc directory rather than under src, this is more in line with where we'd expect dependencies to be placed when running pip install ....

Using the Conda environment#

Now that you've installed the conda environment as above, you can activate it by running:

conda activate allenact

after which you can run everything as you would normally.

Installing supported environments#

We also provide installation instructions for the environments supported in AllenAct here.