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Process
In general, the process to build a multiplatform-cpu architecture agnostic container image follows the flow depicted on the following figure.
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Example using a Python Micro-Service
Workflow
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Following are the commands needed to create a multi-cpu architecture container image. Let's call the image onap/multi-cpu-app-py.
Note that this flow can be used during the ONAP development-test-debug process. For the release process, the flow is implemented using CI/CD pipelines as shown in the next section.
Source code
Code structure
Main steps
The following diagram captures the main steps we need to take to enable platform-agnostic containers:
(1) and (2) Build and push container images for each platform.
(3) Create and push a manifest list for the images above
(4) Pull and run the exact same image/tag on different platforms.
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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 platform-agnostic image (tag).
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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
| Code Block |
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.
├── app
│ ├── main.py
│ └── requirements.txt
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| Code Block |
.
├── app
│ ├── main.py
│ └── requirements.txt
└── Dockerfile
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Python App
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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)
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Requirements
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Flask==0.10.1 |
Dockerfile
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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.
...
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docker build -t onap/app-py-armapp-linuxarm64 . |
Push arm image to the registry
Once the image has been successfully built, push it to the repository.
...
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docker push onap/app-py-armapp-linuxarm64v8:latest |
Build Intel image (B)
Log into an intel server, setup the code structure as before.
...
| Code Block |
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docker build -t onap/app-py-amdapp-linuxamd64 . |
Push Intel image to the registry
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docker push onap/app-py-amdapp-linuxamd64: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
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docker manifest create onap/multi-cpupy-app-py \ onap/app-py-armapp-linuxarm64v8 \ onap/app-py-amdapp-linuxamd64 |
Verify that the manifest describes a
...
platform-
...
agnostic container image.
| Code Block |
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docker manifest inspect --verbose onap/multipy-cpu-app-py |
Verify that the manifest actually represents a multi-cpu architecture by looking at the different "platform" entries.
...
| Code Block |
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[
{
"Ref": "docker.io/onap/app-py-amdapp-linuxamd64: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/app-py-armapp-linuxarm64v8: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
| Code Block |
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docker manifest push onap/multi-cpu-app-py
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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.
]
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Push the manifest list to the registry
| Code Block |
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docker manifest push onap/py-app
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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.
| Gliffy Diagram | ||||||
|---|---|---|---|---|---|---|
|
The following view illustrates intermediate step of building and storing executable and sequence of image processing
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|---|---|---|---|---|
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Intermediate Image Naming Convention
As described above, platform-agnostic support requires platform specific images that are put together to expose a multi-platform image. Platform 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 (<onap-image-name>). This convention is aligned with existing industry standards and naming conventions (amd64, arm64v8).
| Architecture | OS | Variant | Image Name |
|---|---|---|---|
| amd64 | Linux | <onap-image-name>-amd64 | |
| arm64 | Linux | v8 | <onap-image-name>-arm64v8 |
| mips | Linux | <onap-image-name>-mips |
Note: This table does not contain a exhaustive list of options and must only be used as an image naming guide. Because ONAP is vendor-agnostic, the list is not a statement of what architectures or OSs ONAP must support.Gliffy Diagram