Closed misohu closed 2 weeks ago
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The internal ticket has been created: https://warthogs.atlassian.net/browse/KF-5995.
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Closed with https://github.com/canonical/charmed-kubeflow-uats/pull/104
@component(
base_image="python:3.11", # Use Python 3.11 base image
packages_to_install=["requests==2.32.3", "pandas==2.2.2"]
)
def download_dataset(url: str, dataset_path: OutputPath('Dataset')) -> None:
# Import necessary packages within the function
import requests
import pandas as pd
# Download the dataset from the provided URL
response = requests.get(url)
response.raise_for_status()
packages_to_install
.InputPath
and OutputPath
annotations. Please use the correct annotation type within the brackets; otherwise, data may be incorrectly passed between steps. Some useful annotations include Dataset
for passing dataset files through S3 and String
for passing strings as arguments in the Argo workflow outside of S3.@pipeline(name='download-preprocess-train-deploy-pipeline')
def download_preprocess_train_deploy_pipeline(url: str):
# Step 1: Download the dataset from the URL
download_task = download_dataset(url=url)
# Step 2: Preprocess the downloaded dataset
preprocess_task = preprocess_dataset(
dataset=download_task.outputs['dataset_path']
)
OutputPath
typed arguments can be accessed through the outputs
attribute, as shown above.Note: Most online resources focus on KFP v1, so I highly recommend consulting the official documentation for KFP v2.5.0, which is compatible with our kserve 0.13.1 SDK due to the protobuf requirement conflict. You can find the relevant documentation here.
Context
For the Kubeflow 1.9 we must update
e2e-wine
UAT because it uses Seldon to deploy the trained model. With Kubeflow 1.9 seldon wont be part of the bundle.While we are working on this we should also use kfp v2 SDK in the test rather then the older v1.
What needs to get done
e2e-wine
UAT for Kubeflow 1.9e2e-wine
UAT for Kubeflow 1.9Definition of Done