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License: GNU Affero General Public License v3.0
We need to auto deploy naas-models version to pypi.
We need to create the Space model with it's validation logic. This also needs to build the proto files into the Python classes using the pydantic BaseModel (there is a protobuf to pydantic module out there).
You also need to setup Semantic release on this project, just for versioning, not pushing on pypi as it's a private repo.
You must create a Makefile and leverage docker to install protoc and needed software to build the proto
Now that the scheduler-v2-poc is done, we can proceed to the implementation of that in the api.
For that we need to create the Workflow.proto that will contain the resources needed to build a DAG Workflow that will run in Argo Workflow.
We should follow the following documentation from Argo: https://argoproj.github.io/argo-workflows/walk-through/the-structure-of-workflow-specs/
Resources that should be taken in account for creating the protos are:
We must also be able to specify metadata but this will mainly be for us in the backend to pinpoint to which users are the workflow tied to etc.
As part of this you should also create all the CRUD messages (DTO) like WorkflowCreationRequest
, WorkflowDeletionRequest
etc ...
@viniciusdc please let me know if I am forgetting anything.
After installing grpcio-tools, there are issues with the make
, generate
, and target
commands, as well as problems with the Dockerfile building. Although bumping everything seemed to resolve some issues, it led to the naas-api-ce
failing. This issue aims to fix the mentioned problems and update naas-api-ce
to resolve the failures.
When defining error codes in proto files, there is a risk of overlapping codes which can lead to confusion and misinterpretation of error responses. This can be a critical issue, especially when handling HTTP status codes.
To prevent this issue, we need to establish and maintain a system or protocol for managing and assigning unique error codes across all our proto files. This system should ensure that each error code is distinct and clearly documented to prevent any ambiguity.
By addressing this issue proactively, we can enhance the clarity and reliability of our error handling mechanisms within the project.
We need to have models being created for chats, models and messages
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