Container images for running packaged UE4 projects
The various tags of the adamrehn/ue4-runtime image provide minimal, pre-configured environments for running packaged Unreal Engine projects with full GPU acceleration via NVIDIA Docker. (For more details on NVIDIA Docker, see the NVIDIA Docker primer from the ue4-docker documentation.) Note that these images will work with packaged Linux builds from any source, not just builds packaged using the container images from the ue4-docker project.
Both OpenGL and OpenGL+CUDA variants are provided, along with preconfigured images for common GPU-accelerated frameworks such as TensorFlow. Each image variant is also available in a configuration with VirtualGL bundled for displaying the output of OpenGL applications using the host system's display. See the section Using the VirtualGL images for usage details.
The following variants are available:
adamrehn/ue4-runtime:base-opengl is a base image with OpenGL support
adamrehn/ue4-runtime:base-cudagl9.2 is a base image with OpenGL + CUDA 9.2 support
adamrehn/ue4-runtime:base-cudagl10.0 is a base image with OpenGL + CUDA 10.0 support
adamrehn/ue4-runtime:base-cudagl10.1 is a base image with OpenGL + CUDA 10.1 support
adamrehn/ue4-runtime:virtualgl-opengl extends the base OpenGL image with VirtualGL
adamrehn/ue4-runtime:virtualgl-cudagl9.2 extends the base CUDA 9.2 image with VirtualGL
adamrehn/ue4-runtime:virtualgl-cudagl10.0 extends the base CUDA 10.0 image with VirtualGL
adamrehn/ue4-runtime:virtualgl-cudagl10.1 extends the base CUDA 10.1 image with VirtualGL
adamrehn/ue4-runtime:tensorflow-1.13.1 extends the base CUDA 10.0 image with TensorFlow 1.13.1
adamrehn/ue4-runtime:tensorflow-1.13.1-virtualgl extends the TensorFlow 1.13.1 image with VirtualGL
The following tags are merely concise aliases for more specific tags:
adamrehn/ue4-runtime:latest is an alias for
adamrehn/ue4-runtime:virtualgl is an alias for
adamrehn/ue4-runtime:tensorflow is an alias for
adamrehn/ue4-runtime:tensorflow-virtualgl is an alias for
For details on using these images to perform cloud rendering via NVIDIA Docker, see the Cloud rendering guide from the ue4-docker documentation.
Using the VirtualGL images
virtualgl configuration of each image variant adds the following components:
- The X11 libraries needed for running applications that create X11 windows
- VirtualGL itself, which provides the
vglruncommand for interposing OpenGL function calls
To run a container using a VirtualGL-enabled image, the Docker host system will need to be running an X11 server and you will need to bind-mount the host's X11 socket inside the container like so:
# Replace "adamrehn/ue4-runtime:virtualgl" with your chosen image tag docker run --runtime=nvidia -v/tmp/.X11-unix:/tmp/.X11-unix:rw -e DISPLAY adamrehn/ue4-runtime:virtualgl bash
The manner in which you need to invoke UE4 projects inside the container depends on your use case:
If you are running the container locally on a machine with an OpenGL-enabled X11 configuration (e.g. a standard desktop installation of Ubuntu 18.04) then the GLVND dispatch library provided by the NVIDIA base images will handle the relevant OpenGL function calls without the need to use VirtualGL. Running UE4 projects via
vglrunin this scenario will actually reduce performance due to the additional interposition overheads, so be sure to run projects directly. (e.g.
If you are running the container on a remote host and are using X11 forwarding to display the window on your local machine then you will need to run UE4 projects via
vglrunin order to ensure OpenGL functionality will work from within an SSH session. (e.g.
Building the images from source
Building the container images from source requires Python 3.5 or newer and the dependency packages listed in requirements.txt.
To build the images, simply run
build.py. This will automatically query Docker Hub to retrieve the list of available nvidia/cudagl base images based on Ubuntu 18.04 and build all variants of the
adamrehn/ue4-runtime image accordingly.
Copyright © 2019, Adam Rehn. Licensed under the MIT License, see the file LICENSE for details.
Development of the TensorFlow image variant was funded by Deepdrive, Inc.