This repository includes DTDL models that are made publicly available on https://devicemodels.azure.com. These models can be used to create Azure IoT Plug and Play solutions.
Related tools, samples, and specs can be found in the Azure/iot-plugandplay-models-tools repo. The current repo only stores DTDL models.
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Create a GitHub account if you do not have one yet: Join GitHub.
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Fork the public GitHub repo: https://github.com/Azure/iot-plugandplay-models.
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Clone the forked repo. Optionally create a new branch to keep your changes isolated from the
main
branch.By forking and cloning the public GitHub repo, a copy of repo will be created in your GitHub account and a local copy is created in your dev machine. Please use this local copy to make modifications.
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Author a new device model with an unique ID using Digital Twin Model Identifier.
Review the PR requirements for naming conventions.
[!TIP]
DTDL Editor for Visual Studio Code could help you with the language syntax (including auto-completion) and also validate the syntax with DTDL v2. -
Save the device model JSON file to a local folder.
E.g.
C:\iot-plugandplay-models\MyThermostat.json
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Validate the models locally using the
dmr-client
tool to validate. -
Add the new interfaces to the
dtmi
folder using the folder/filename convention. See the import command below. -
Review and cross check with the PR requirements and ensure all elements are conform to DTDL v2 specification.
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Commit the changes locally and push to your fork.
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From your fork, create a PR that targets the
main
branch.
The PR triggers a series of GitHub actions that will validate the new submitted interfaces, and make sure your PR satisfies all the checks.
Microsoft will respond to a PR with all checks in 3 business days.
GitHub Operation Workflow
+-------------------------------------------------+
| iot-plugandplay-models repo (Microsoft) |
+-------------------------------------------------+
| ⭡
| Fork | Pull Request (PR)
🡓 |
+-------------------------------------------------+
| iot-plugandplay-models repo (your Github account) |
+-------------------------------------------------+
| ⭡
| Clone | Commit/Push
🡓 |
+-------------------------------------------------+
| Your development PC |
| - Author device model |
| - Import your model to the DTMI folder |
+-------------------------------------------------+
The tools used to validate the models during the PR checks can also be used to add and validate the DTDL interfaces locally.
Note: This tool requires the .NET SDK (3.1 or greater)
curl -L https://aka.ms/install-dmr-client-linux | bash
iwr https://aka.ms/install-dmr-client-windows -UseBasicParsing | iex
To update the client you must uninstall the current version first:
dotnet tool uninstall -g microsoft.iot.modelsrepository.commandline
[Note] Previous versions must be uninstalled with
dotnet tool uninstall -g dmr-client
. Use thedotnet tool list -g
command to check the id of the tool you want to uninstall
If you have your model already stored in json files, you can use the dmr-client import
command to add those to the dtmi/
folder with the right file name.
# from the local repo root folder
> dmr-client import --model-file "MyThermostat.json"
This command will rename and locate the file in the appropriate folder
You can validate your models with the dmr-client validate
command.
# Validate a model file using the DTDL parser
> dmr-client validate --model-file ./my/model/file.json
Note: The validation uses the latest DTDL parser version to ensure all the interfaces are compatible with the DTDL language spec
To validate external dependencies, those must exist in the local repo. To validate those you can specify a local
or remote
folder to validate against.
# Validate a model file using the DTDL parser checking dependencies with the current folder as a local repo
> dmr-client validate --model-file ./my/model/file.json --repo .
The Device Model Repo includes additional requirements, these can be validated with the strict
flag.
# Validate a model file using the DTDL parser checking dependencies with the current folder as a local repo in strict mode
> dmr-client validate --model-file ./my/model/file.json --repo . --strict
Models can be exported from a given repo (local or remote) to a single file using a JSON Array.
# Retrieves an interface from a custom repo by DTMI
> dmr-client export --dtmi "dtmi:com:example:Thermostat;1" --repo https://raw.githubusercontent.com/Azure/iot-plugandplay-models/main
The model repo can host a index.json
file with all the ids
avaialble in the repository. Read the Index Spec
# Builds a model index for the repository. If models exceed the page limit new page files will be created relative to the root index.
> dmr-client index --local-repo .
# Build a model index with a custom page limit indicating max models per page.
> dmr-client index --local-repo . --page-limit 100
# Expand all models from the root directory of a local models repository following Azure IoT conventions.
# Expanded models are inserted in-place.
> dmr-client expand --local-repo .
# The default --local-repo value is the current directory. Be sure to specifiy the root for --local-repo.
> dmr-client expand
There are Azure SDKs available for the models repository in the following languages:
Platform | Package | Source | Samples |
---|---|---|---|
.NET | Azure.IoT.ModelsRepository | Source | Samples |
Java | com.azure/azure-iot-modelsrepository | Source | Samples |
Node | coming soon | ||
Python | coming soon |
Any HTTP client can consume the models by just applying the convention to translate DTMI ids to relative paths:
Eg, the interface:
dtmi:azure:DeviceManagement:DeviceInformation;1
can be retrieved from here:
https://devicemodels.azure.com/dtmi/azure/devicemanagement/deviceinformation-1.json
There are samples for .NET and Node in the Azure/iot-plugandplay-models-tools with code you can use to acquire models from your custom IoT solution.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.