Setup the evaluation and explainability testing environment

Here is the setup guide for evaluation and explainability testing environment. If you want to observe the full pipeline service code, please take a look at Borbann repository.

Prerequisites

You need the following tools to run the evaluation and explainability testing environment

Python 3.12
Google Cloud SDK
Vertex AI SDK
UV

Also, you need to modify the code in vertex.py to point to your project ID and model name. Create your own model in Vertex AI platform first, using the train-1.jsonl, train-2.jsonl, train-3.jsonl as training data and evluation.jsonl as evaluation data.

Setup

uv sync

Evaluation

gcloud auth application-default login
uv run evaluate.py

Explainability

gcloud auth application-default login
uv run explainability.py

Input data gathering

To get the input data from pipeline service, you need to run the pipeline service first.

git clone https://github.com/borbann-platform/backend-api.git
cd backend-api/pipeline

uv sync
uv run main.py

The navigate to 127.0.0.1:8000/docs to see the API documentation. In the swagger documentation, you follow these steps

Create a new pipeline with preferred configuration
Go to /pipeline/{pipeline_id}/run and run the pipeline
Wait for the pipeline to finish
Go to /pipeline/{pipeline_id}/result to get the result
Copy the result

1.5 KiB Raw Permalink Blame History