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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++11, and you will need a sufficiently modern compiler. GCC 4.8 should suffice.

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

  1. Install Bazel (see above).

  2. Install DeepMind Lab's dependencies:

    • On Debian or Ubuntu:

      Tested on Debian 8.6 (Jessie) and Ubuntu 14.04 (Trusty) and newer.

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

    • On Red Hat Enterprise Linux Server:

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

      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 \
    SDL2 SDL2-devel mesa-libOSMesa-devel zip numpy

    • 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

For Python3, replace the various python-* packages with their corresponding python3-* versions.

  1. Clone or download DeepMind Lab.

  2. If necessary, edit python.BUILD according to the Python instructions below.

  3. 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 must already be installed on your system. This means that depending on the details of where that library is installed, you may need to adjust the Bazel build rules in python.BUILD to locate it correctly.

The default build rules should work for Debian and Ubuntu. Note that paths in the build rules are relative to the root path specified in the WORKSPACE file (which is "/usr" by default).

Python requires two separate dependencies: The CPython extension API, and NumPy. If, say, NumPy is installed in a custom location, like it is on SUSE Linux, you need to add the files from that location and set an include search path accordingly. For example:

cc_library(
    name = "python",
    hdrs = glob([
        "include/python2.7/*.h",
        "lib64/python2.7/site-packages/numpy/core/include/**/*.h",
    ]),
    includes = [
        "include/python2.7",
        "lib64/python2.7/site-packages/numpy/core/include",
    ],
    visibility = ["//visibility:public"],
)

The outputs of rpm -ql python and rpm -ql python-numpy-devel might be helpful to find the right include directories on Red-Hat-like systems.

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())

You will also need to change the Python paths if you want to use Python 3. For example:

cc_library(
    name = "python",
    hdrs = glob([
        "include/python3.5/*.h",
        "lib/python3/dist-packages/numpy/core/include/numpy/*.h",
    ]),
    includes = [
        "include/python3.5",
        "lib/python3/dist-packages/numpy/core/include",
    ],
    visibility = ["//visibility:public"],
)

(In that case you may also want to pass --python_path=/usr/bin/python3 to Bazel. 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.)