KFP | KungFury inspired game coded in React Native | Frontend Framework library

In this case is the best way to extract the information. But, I recommend using the yarl library for complex uri to parse.

You can see this example:

After some time researching the problem I've stumbled upon this Github issue. The problem was originated by a mismatch between google_cloud_pipeline_components and kubernetes_api docs. In this case, serving_container_environment_variables is typed as an Optional[dict[str, str]] whereas it should have been typed as a Optional[list[dict[str, str]]]. A similar mismatch can be found for serving_container_ports argument as well. Passing arguments following kubernetes documentation did the trick:

UPDATE:
I tested the Output[HTML] from kfp sdk v2 and it works but I came across other issues.
First of, Kubeflow html viewer creates an iframe with blank src and srcdoc="your static html". This made it impossible to use an iframe in your html as you'd have a nested iframe (the parent from the html viewer and the nested one from your actual html).

Solution :

I found a solution that works on KFP SDK v1 and v2 for all use cases, I used markdown visualization instead of HTML visualization. Since markdown supports inline HTML, I was able to directly paste my html to the markdown output. Compared to using HTML visualization, this supports iframe.

Here is some code to illustrate the solution :

I'm glad you solved most of your main issues and found a workaround for model declaration.

For your input.output observation on gcs_source_uris, the reason behind it is because the way the function/class returns the value. If you dig inside the class/methods of google_cloud_pipeline_components you will find that it implements a structure that will allow you to use .outputs from the returned value of the function called.

If you go to the implementation of one of the components of the pipeline you will find that it returns an output array from convert_method_to_component function. So, in order to have that implemented in your custom class/function your function should return a value which can be called as an attribute. Below is a basic implementation of it.

I didn't realized in the first place that ConcatPlaceholder accept both Artifact and string. This is exactly what I wanted to achieve:

You don’t have to rewrite the components, there is no mapping of components of tfx in kfp, as they are not competitive tools.

With tfx you create the components and then you use an orchestrator to run them. Kubeflow pipelines is one of the orchestrators.

The tfx.orchestration.pipeline will wrap your tfx components and create your pipeline.

We have two schedulers behind kubeflow pipelines: Argo (used by gcp) and Tekton (used by openshift). There are examples for tfx with kubeflow pipelines using tekton and tfx with kubeflow pipelines using argo in the respective repositories.

From the error you shared, apparently Cloud Function failed to create the job.

Currently, GCP don't support A2 Machine type for normal KF Components. A potential workaround right now is to use GCP custom job component that you can explicitly specify the machine type.

My original CustomPythonPackageTrainingJobRunOp wasn't defining worker_pool_spec which was the reason for the error. After I specified replica_count and machine_type the error resolved. Final training op is: