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Installation of 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 allenact and/or its requirements we recommend creating a new python virtual environment and installing all of the below requirements into this virtual environment.

Alternatively, we also document how to install a conda environment with all the requirements, which is especially useful if you plan to train models in Habitat.

Different ways to use allenact#

There are three main installation paths depending on how you wish to use allenact.

  1. You want to use the allenact abstractions and training engine for your own task/environment and don't really care about using any of our plugins that offer additional support (in the form of models, sensors, task samplers, etc.) for select tasks/environments like AI2-THOR, Habitat, and MiniGrid.
  2. You want to use allenact as above but would also like to use some of our additional plugins.
  3. You want full access to everything in allenact (including all plugins and all of our projects and baselines) and want to have the option to edit the internal implementation of allenact to suit your desire.

Standalone framework#

You can install allenact easily using pip:

pip install allenact

If you'd like to install the latest development version of allenact (possibly unstable) directly from GitHub see the next section.

Bleeding edge pip install#

To install the latest allenact framework, you can use

pip install -e "git+"

and, similarly, you can also use

pip install -e "git+[all]&subdirectory=allenact_plugins"

to install all plugins.

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

Framework and plugins#

To install allenact and all available plugins, run

pip install allenact allenact_plugins[all]

which will install allenact and allenact_plugins packages along with the requirements for all of the plugins (when possible). If you only want to install the requirements for some subset of plugins, you can specify these plugins with the allenact_plugins[plugin1,plugin2] notation. For instance, to install requirements for the ithor_plugin and the minigrid_plugin, simply run:

pip install allenact allenact_plugins[ithor,minigrid]

A list of all available plugins can be found here.

Full library#

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

git clone
cd allenact

Below we describe two alternative ways to install all dependencies via pip or conda.

Installing requirements with pip#

All requirements for allenact (not including plugin requirements) may be installed by running the following command:

pip install -r requirements.txt; pip install -r dev_requirements.txt

To install plugin requirements, see below.

Plugins extra requirements#

To install the specific requirements of each plugin, we need to additionally call

pip install -r allenact_plugins/<PLUGIN_NAME>_plugin/extra_requirements.txt

from the top-level directory.

Installing a conda environment#

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.

The conda 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 all machines (it includes PyTorch with the latest cudatoolkit).
  • environment-dev.yml - Additional dev dependencies.
  • environment-<CUDA_VERSION>.yml - Additional dependencies, where <CUDA_VERSION> is the CUDA version used on your machine (if you are using linux, you might find this version by running /usr/local/cuda/bin/nvcc --version).
  • environment-cpu.yml - Additional dependencies to be used on machines where GPU support is not needed (everything will be run on the CPU).

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}"))"
export 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 ....

If needed, you can use one of the environment-<CUDA_VERSION>.yml environment files to install the proper version of the cudatoolkit by running:

conda env update --file ./conda/environment-<CUDA_VERSION>.yml --name allenact

or the CPU-only version:

conda env update --file ./conda/environment-cpu.yml --name allenact

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 with conda#

Each supported plugin contains a YAML environment file that can be applied upon the existing allenact environment. To install the specific requirements of each plugin, we need to additionally call

conda env update --file allenact_plugins/<PLUGIN_NAME>_plugin/extra_environment.yml --name $MY_ENV_NAME

from the top-level directory.

Habitat: Note that, for habitat, we provide two environment types, regarding whether our machine is connected to a display. More details can be found here.