Process
In general, the process to build a multi-cpu architecture container image follows the flow depicted on the following figure.
The commands needed to implement the flow are described, using an example, in the next section.
Example using a Python Micro-Service
Main steps
The following diagram captures the main steps we need to take to enable multi-cpu architecture containers:
(1) and (2) Build and push container images for each cpu-architecture
(3) Create and push a manifest list for the images above
(4) Pull and run the exact same image/tag on different cpu architectures.
Manifest List and Image Layers
Digging a little bit deeper into step (4), the following diagram shows the relationship between a manifest list and image manifests for our multi-cpu architecture image (tag).
The following sections describe the commands needed to create a multi-cpu architecture container image. Let's call the image onap/py-app.
Note that this flow could be used by ONAP developers during the development-test-debug process.
For the release process, the flow will implemented using CI/CD pipelines as shown in the next section.
Source code
Code structure
. ├── app │ ├── main.py │ └── requirements.txt └── Dockerfile
Python App
from flask import Flask import platform app = Flask(__name__) @app.route("/") def hello(): return "Hello ONAP. I am a Python service running on " + platform.machine() if __name__ == "__main__": app.run(host='0.0.0.0', debug=True, port=5000)
Requirements
Flask==0.10.1
Dockerfile
FROM python:2.7-alpine LABEL maintainer="adolfo@orangemonk.net" # Keep it simple COPY ./app /app WORKDIR /app RUN pip install -r requirements.txt ENTRYPOINT ["python"] CMD ["main.py"]
Build arm image (A)
Log into an arm server, copy the code into the structure depicted above.
cd to the root of the code tree above, then execute
docker build -t onap/py-app-arm64 .
Push arm image to the registry
Once the image has been successfully built, push it to the repository.
Note that if you are using a private repository, you might need to "docker tag" the image before executing the next command.
docker push onap/py-app-arm64:latest
Build Intel image (B)
Log into an intel server, setup the code structure as before.
Let's now repeat the process for the intel layers of the multi-cpu container image.
docker build -t onap/py-app-amd64 .
Push Intel image to the registry
docker push onap/py-app-amd64:latest
Create a manifest list for image A and image B
Now that we have built and pushed layers for each cpu architecture, we can create the manifest list to put the final container image together with
docker manifest create onap/py-app \ onap/py-app-arm64 \ onap/py-app-amd64
Verify that the manifest describes a multi-cpu architecture container image.
docker manifest inspect --verbose onap/py-app
Verify that the manifest actually represents a multi-cpu architecture by looking at the different "platform" entries.
Notice how, in this case, the manifest shows layers for both arm and Intel cpu architectures.
[ { "Ref": "docker.io/onap/py-app-amd64:latest", "Descriptor": { "mediaType": "application/vnd.docker.distribution.manifest.v2+json", "digest": "sha256:4b1b8361d47770668ff4c728dc388f376158d9e8d82c0b4cdb22147cc8ca1be6", "size": 1579, "platform": { "architecture": "amd64", "os": "linux" } }, "SchemaV2Manifest": { "schemaVersion": 2, "mediaType": "application/vnd.docker.distribution.manifest.v2+json", "config": { "mediaType": "application/vnd.docker.container.image.v1+json", "size": 6616, "digest": "sha256:f2947f4a0e9d5d0a4ccf74a7b3ad94611a8921ca022b154cabcad9e453ea6ec5" }, "layers": [ { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 2206931, "digest": "sha256:4fe2ade4980c2dda4fc95858ebb981489baec8c1e4bd282ab1c3560be8ff9bde" }, { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 308972, "digest": "sha256:1b23fa3ccba56eced7bd53d424b29fd05cd66ca0203d90165e988fdd8e71fed7" }, { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 17711780, "digest": "sha256:b714494d7662fbb89174690cefce1051117ed524ec7995477b222b8d96fb8f0c" }, { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 1776866, "digest": "sha256:1098418f3d2f83bcccdbb3af549d75d9a5e9c37420e5c0d474fd84b022f6c995" }, { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 360, "digest": "sha256:ca727cee7c2469ab6edb7ca86378985b3747938a325ddb7d90f3b85a3d14b34f" }, { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 4315553, "digest": "sha256:767bbe8ba063767093b0350e8d1b86b438c677e3884c2a21851c00970d88317c" } ] } }, { "Ref": "docker.io/onap/py-app-arm64:latest", "Descriptor": { "mediaType": "application/vnd.docker.distribution.manifest.v2+json", "digest": "sha256:8a06f997353177dae82d7e01bc3893b2f05c6ac27b317655df3ca2287f9b83a9", "size": 1786, "platform": { "architecture": "arm64", "os": "linux" } }, "SchemaV2Manifest": { "schemaVersion": 2, "mediaType": "application/vnd.docker.distribution.manifest.v2+json", "config": { "mediaType": "application/vnd.docker.container.image.v1+json", "size": 6864, "digest": "sha256:b097a21c92a9a0dde06f9b36bf10def56a028b3b9b1617d6d6c6a8559d14d9d7" }, "layers": [ { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 2099514, "digest": "sha256:47e04371c99027fae42871b720fdc6cdddcb65062bfa05f0c3bb0a594cb5bbbd" }, { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 176, "digest": "sha256:b4103359e1ecd9a7253d8b8a041d4e81db1ff4a5e1950bc0e02305d221c9e6c2" }, { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 308518, "digest": "sha256:92079a442932f09a622f4a0f863e5cc2f6e0471a98e5121fa719d2a276440386" }, { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 18605961, "digest": "sha256:f1fc35806f46347993a2cd1eb7f7dd7837b0bef0392c8e2c973b24c02ad874a9" }, { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 1786389, "digest": "sha256:c2983ee3d71107a9a0bc1996fc3a58e050026995fad4aee9d72539153db1df3d" }, { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 391, "digest": "sha256:44c3eae5ed66bb040727a64fd78573fe6cc4a94a9317d5cd6f39e53332c2ae21" }, { "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", "size": 4305558, "digest": "sha256:8daa79c3024a565c320ff69990ad48273937cc3f6f0cdb324e086c268cf6245e" } ] } } ]
Push the manifest list to the registry
docker manifest push onap/py-app
Building Multi-CPU container images using CI/CD pipelines
The following diagram depicts a CI/CD flow that implements the production of multi-cpu architecture container images.
Although the flow shows a pipeline for two branches: arm-linux and intel-linux, the model can be extended --given the hardware and software resources-- to any number of platforms.
The following view illustrates intermediate step of building and storing executable and sequence of image processing
Intermediate Image Naming Convention
As described above, multi-cpu architecture support requires platform specific images that are put together to expose a multi-platform image. Architectures include, among others, arm,intel
, mips
, ppc64le
, and s390x
These platform-specific image names are typically used only by developers that build the images. ONAP end users should only use the aggregate tag but can still inspect the image using docker manifest.
The following is the recommended naming convention for ONAP platform-specific images that will be produced by the different pipelines (<base-image-name>). This convention is aligned with existing industry standards and naming conventions (amd64, arm64v8).
Architecture | OS | Variant | Image Name |
---|---|---|---|
amd64 | Linux | <base-image-name>-amd64 | |
arm64 | Linux | v8 | <base-image-name>-arm64v8 |
mips | Linux | <base-image-name>-mips |