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How to build DeepMind Lab

DeepMind Lab uses Bazel as its build system. Its main BUILD file defines a number of build targets and their dependencies. The build rules should work out of the box on Debian (Jessie or newer) and Ubuntu (version 14.04 or newer), provided the required packages are installed. DeepMind Lab also builds on other Linux systems, but some changes to the build files might be required, see below.

DeepMind Lab is written in C99 and C++17, and you will need a sufficiently modern compiler.

Instructions for installing Bazel can be found in the Bazel install guide.

You may need to deal with some details concerning Python dependencies. Those are documented in a separate section below.

Step-by-step instructions for building and running

These instructions were checked for the initial release of DeepMind Lab, which only required C++11. Since 2022 it requires C++17, and more recent versions of the platforms shown below are needed. However, the general set of dependencies should continue to remain largely accurate.

  1. Install Bazel (see above).

  2. Install DeepMind Lab's dependencies:

    • On Debian or Ubuntu:

      Tested on Debian 9 (Strectch) and Ubuntu 16.04 (Xenial) and newer. Tested with Python 2 only on Debian 8.6 (Jesse) and Ubuntu 14.04 (Trusty).

      $ sudo apt-get install libffi-dev gettext freeglut3-dev libsdl2-dev \
      zip libosmesa6-dev python-dev python-numpy python-pil python-enum34 \
     python3-dev python3-numpy python3-pil

      To build a PIP package, also install python3-setuptools python-setuptools python3-wheel python-wheel. To use it, install python3-pip python-pip, and also python3-virtualenv python-virtualenv to use virtualenv.

    • On Red Hat Enterprise Linux Server:

      Tested on release 7.6 (Maipo). This should also work on Centos 7, and with some modifications of the package installation commands on Centos 6. Tested with Python 2 only on release 7.2.

      sudo yum -y install unzip java-1.8.0-openjdk libffi-devel gcc gcc-c++ \
    java-1.8.0-openjdk-devel freeglut-devel python-devel python-imaging \
    numpy python36-numpy python36-pillow python36-devel SDL2 SDL2-devel \
    mesa-libOSMesa-devel zip

    • On SUSE Linux:

      Tested on SUSE Linux Enterprise Server 12.

      sudo zypper --non-interactive install gcc gcc-c++ java-1_8_0-openjdk \
    java-1_8_0-openjdk-devel libOSMesa-devel freeglut-devel libSDL-devel \
    python-devel python-numpy-devel python-imaging

  3. Clone or download DeepMind Lab.

  4. Build DeepMind Lab and run a random agent. (Use the -c opt flag to enable optimizations.)

    $ cd lab

# Build the Python interface to DeepMind Lab
lab$ bazel build -c opt //:deepmind_lab.so

# Build and run the tests for it
lab$ bazel test -c opt //python/tests:python_module_test

# Run a random agent
lab$ bazel run -c opt //:python_random_agent

The Bazel target :deepmind_lab.so builds the Python module that interfaces with DeepMind Lab.

The random agent target :python_random_agent has a number of optional command line arguments. Run bazel run :random_agent -- --help to see those.

Python dependencies

DeepMind Lab does not include every dependency hermetically. In particular, Python is not included, but instead it must already be installed on your system. Our Bazel workspace includes a mechanism to discover the location of the system's Python paths automatically by running the python2 and python3 interpreters. Additionally, NumPy must be available on your system, too.

Bazel can build Python code using either Python 2 or Python 3. The default is Python 3, but each individual py_binary and py_test target can specify the desired version using the python_version argument. The build rules need to make the local installation path of correct version of Python available.

The default build rules should work for Debian and Ubuntu. They use Bazel's configurable attributes to provide paths for Python 2 and Python 3, respectively, based on which version is required during a particular build.

If you have installed NumPy locally via PIP and would like to use the DeepMind Lab PIP module, then you should build the module against the version of NumPy that you will be using at runtime. You can discover the include path of that version by running the following code in your desired environment:

import numpy as np
print(np.get_include())

For building PIP packages, you may need to run the PIP packaging script with PYTHON_BIN_PATH="/usr/bin/python3" bazel-bin/python/pip_package/build_pip_package /your/outputdir and then use the pip3 command. As before, the Python binary needs to match the Python and NumPy libraries that you linked against, which may need some care when a user's local installation differs from the system-wide one.