DIANA Docker Image

Derek Merck
derek.merck@ufl.edu
University of Florida and Shands Hospital
Gainesville, FL

Build Status Coverage Status Doc Status

Source: https://www.github.com/derekmerck/diana2
Documentation: https://diana.readthedocs.io
Image: https://cloud.docker.com/repository/docker/derekmerck/diana2

Build multi-architecture Docker images for embedded systems.

Use It

These images are manifested per modern Docker.io guidelines so that an appropriately architected image can be will automatically selected for a given tag depending on the pulling architecture.

$ docker run derekmerck/diana2      # (latest-amd64, latest-arm32v7, latest-arm64v8)
$ docker run derekmerck/diana2-plus # (latest-amd64, latest-arm32v7)

Images for specific architectures images can be directly pulled from the same namespace using the format derekmerck/diana2:${TAG}-${ARCH}, where $ARCH is one of amd64, arm32v7, or arm64v8. Explicit architecture specification is sometimes helpful when an indirect build service shadows the production architecture.

Build It

These images are based on the cross-platform resin/${ARCH}-debian:buster image. Resin.io base images include the QEMU cross-compiler to facilitate building Docker images for low-power single-board computers while using more powerful Intel-architecture compute servers.

This supports builds for amd64, armhf/arm32v7, and aarch64/arm64v8 architectures. Most low-power single board computers such as the Raspberry Pi and Beagleboard are armhf/arm32v7 devices. The Pine64 and NVIDIA Jetson are aarch64/arm64v8 devices. Desktop computers/vms, UP boards, and the Intel NUC are amd64 devices.

docker-compose.yml contains build recipes for each architecture for a simple diana image with all requirements pre-installed.

To build all images:

  1. Register the Docker QEMU cross-compilers
  2. Get docker-manifest from Github
  3. Put Docker into “experimental mode” for manifest creation
  4. Call docker-compose to build the diana2-base images
  5. Call docker-manifest.py with an appropriate domain to retag and push the base images
  6. Call docker-compose to build the diana2 images
  7. Call docker-manifest.py with an appropriate domain to retag and push the completed images
$ docker run --rm --privileged multiarch/qemu-user-static:register --reset
$ pip install git+https://github.com/derekmerck/docker-manifest
$ mkdir -p $HOME/.docker && echo '{"experimental":"enabled"}' > "$HOME/.docker/config.json"
$ git clone git+https://github.com/derekmerck/diana2
$ cd diana2/platform/images/diana-docker
$ docker-compose build diana2-base-amd64 diana2-base-arm32v7 diana2-base-arm64v8
$ docker-manifest -s diana2-base $DOCKER_USERNAME
$ docker-compose build diana2-amd64 diana2-arm32v7 diana2-arm64v8
$ docker-manifest -s diana2 $DOCKER_USERNAME

Because the base image rarely changes, but the latest Diana build is still fluid, the Travis automation pipeline for git-push-triggered image creation only automates only steps 7 and 8.

$ docker run derekmerck/diana2:2.1.x python3 -c "import diana; print(diana.__version__)"
2.1.x

Build a DIANA-Plus Docker Image

DIANA-Plus includes scientfic and machine learning packages for advanced image processing on medical image data. It is currently only available for amd64 and arm32v7 because tensorflow is hard to compile for arm64v8. For amd64, DIANA-Plus uses the tf-nightly package (currently 1.13) and for arm32v7 we compile our own wheel (currently 1.12, see TF on arm32 note)

The DIANA-Plus Docker image is much heavier at 700MB, compared to 300MB for the vanilla image.

$ cp ${WHEEL_DIR}/tensorflowXX-py37-arm23.whl .
$ docker-compose build diana2-plus-base-amd64 diana2-plus-base-arm32v7
$ docker-manifest -s diana-plus-base derekmerck
$ docker-compose build diana2-plus-amd64 diana2-plus-arm32v7
$ docker-manifest -s diana-plus derekmerck

$ docker run derekmerck/diana2-plus:latest-arm32v7 python3 -c "import tensorflow; print(tensorflow.__version__)"
1.12.0

DIANA on ARM

If you need access to an ARM device for development, Packet.net rents bare-metal 96-core 128GB aarch64 Cavium ThunderX servers for $0.50/hour. Packet’s affiliated Works On Arm program provided compute time for developing and testing these cross-platform images.

An arm64v8 image can be built natively and pushed from Packet, using a brief tenancy on a bare-metal Cavium ThunderX ARMv8 server.

$ apt update && apt upgrade
$ curl -fsSL get.docker.com -o get-docker.sh
$ sh get-docker.sh
$ docker run hello-world
$ apt install git python-pip
$ pip install docker-compose
$ git clone http://github.com/derekmerck/diana2
... continue as above

Although Resin uses Packet ARM servers to compile arm32 images, the available ThunderX does not implement the arm32 instruction set, so it cannot compile natively for the Raspberry Pi.

Now pull the image without specifying the architecture tag. You can confirm that the appropriate image has been pulled by starting a container with the command arch.

$ docker run derekmerck/diana2 arch
aarch64

You can also confirm the image architecture without running a container by inspecting the value of .Config.Labels.architecture. (This is a creator-defined label that is different than the normal .Architecture key – which appears to always report as amd64.)

$ docker inspect derekmerck/diana2 --format "{{ .Config.Labels.architecture }}"
arm64v8

License

MIT