rhoai-ai-pipelines

name	rhoai-ai-pipelines
metadata	{"author":"rhoai3-demo","version":"1.0.0","platform-family":"rhoai","platform-baseline":"repo","ocp-baseline":"repo","skill-group":"RHOAI Platform"}
description	Use when documenting, reviewing, or operating Red Hat OpenShift AI AI Pipelines from the Working with AI pipelines guide: pipeline server configuration, S3-compatible artifact storage, default versus external MySQL/MariaDB metadata databases, Kubernetes API pipeline definition storage, KFP 2.0 SDK compilation, Pipeline/PipelineVersion custom resources, import/version lifecycle, caching, experiments, runs, workspaces, logs, Elyra, DSPA troubleshooting, and reusable component pipeline lifecycle. Do NOT use for repo KFP component code edits (use rhoai-kfp-pipeline-authoring), project/workbench lifecycle, IDE usage outside pipelines, S3 notebook operations, certificate trust outside pipeline context, Spark applications, or live cluster changes without the OpenShift safety guard.

RHOAI AI Pipelines

Use this skill for Red Hat OpenShift AI AI Pipelines product workflows on the active product baseline in docs/PLATFORM_BASELINE.md.

Source Grounding

Read references/source-capture.md before using product workflow details. Official Red Hat documentation is product authority. This skill adapts the official Working with AI pipelines guide to this repo's GitOps and demo workflow model.

Scope

This skill covers:

• pipeline concepts: pipeline server, pipeline, pipeline version, experiment, artifact, task execution, and run
• pipeline server configuration with S3-compatible object storage and database choices
• external MySQL/MariaDB and Amazon RDS certificate trust handoff
• KFP 2.0 SDK pipeline compilation to IR YAML
• Kubernetes API storage, Pipeline, PipelineVersion, and GitOps-aligned pipeline definition management
• KFP SDK authentication to an OpenShift AI pipeline server
• pipeline import, delete, version upload/delete/view/download lifecycle
• caching behavior and cache controls at task, run, compile, and server levels
• experiments, artifacts, task executions, comparisons, archive/restore/delete
• active, scheduled, archived, duplicated, stopped, restored, and deleted runs
• pipeline run workspace support and external artifact copy pattern
• pipeline step logs and download behavior
• Elyra JupyterLab pipeline editor runtime configuration, run, and export
• DSPA component error interpretation and troubleshooting handoff
• product-side lifecycle for modular pipelines assembled from reusable components, including import, versioning, experiments, runs, logs, and artifact review

Use other skills for adjacent work:

• rhoai-kfp-pipeline-authoring for editing repo KFP Python code, components, runner scripts, and demo-specific pipeline implementation standards
• rhoai-project-workflows for project, workbench, connection, and cluster storage lifecycle
• rhoai-data-science-ide-workflows for non-pipeline JupyterLab and code-server IDE workflows
• rhoai-s3-object-storage-data for notebook object-storage data operations
• rhoai-automl for AutoML optimization runs, leaderboard evaluation, AutoGluon pipeline naming, saved notebooks, and AutoGluon serving handoff
• rhoai-autorag for AutoRAG optimization runs, imported AutoRAG pipeline naming, leaderboard review, and generated indexing/inference notebooks
• rhoai-model-customization-training for Docling, SDG Hub, Training Hub, and end-to-end model customization pipeline patterns
• rhoai-enterprise-rag for deciding whether a RAG stage should use Docling and KFP automation for unstructured corpus ingestion
• rhoai-evaluation for EvalHub risk assessment KFP orchestration and official LM-Eval or EvalHub evaluation workflows
• rhoai-certificate-management for DSCI trusted CA bundle changes
• rhoai-storage-classes for OpenShift AI storage class administration
• rhoai-model-evaluation for evaluation-specific pipeline evidence and metrics patterns
• rhoai-kubeflow-spark-operator for Spark data processing applications that run through SparkApplication resources rather than AI Pipelines

Demo Policy

For this repo:

• Prefer Kubernetes API pipeline definition storage when the demo needs reviewed GitOps desired state for pipeline definitions and versions.
• Keep pipeline server configuration in GitOps only after fields are verified against official docs or active CRD schema.
• Treat the default on-cluster database as development/test only. For production-positioned pipeline workloads, document the external MySQL or MariaDB requirement and CA trust path.
• Use S3-compatible object storage for pipeline artifacts and keep credentials project-scoped and out of Git.
• Do not pass OpenShift access tokens as literal command arguments. Use environment variables or secure prompts.
• Use exact KFP SDK and Python prerequisites from the active official docs before compiling or authenticating pipelines.
• Use caching intentionally. Disable it only for tasks or runs that require deterministic re-execution, debugging, or frequently changing inputs.
• Treat archive as retention and delete as destructive. Require explicit confirmation for deleting pipeline servers, pipelines, versions, archived experiments, and archived runs.
• Use Elyra only with supported JupyterLab workbench images. Do not claim Elyra support for code-server, RStudio, Minimal Python, or CUDA-based workbenches.
• If an official example uses placeholder or mutable model artifacts, replace them with verified demo artifacts before committing demo content.
• For Docling data-processing automation, use the official model customization data-preparation chapter plus rhoai-model-customization-training for the example source. This skill governs the pipeline server, runs, artifacts, logs, versions, and dashboard lifecycle once the Docling pipeline exists.
• When a Red Hat Developer article or component catalog suggests reusable KFP components, treat it as implementation guidance. Verify the pipeline server, storage, run, experiment, artifact, and log behavior through this skill, then route component code selection or authoring to rhoai-kfp-pipeline-authoring.
• Prefer small validation runs before scaling pipeline workloads to full data sets or expensive GPU-backed steps.

Workflow

1. Confirm the active baseline in docs/PLATFORM_BASELINE.md.
2. Read references/source-capture.md and references/official-doc-extraction.md.
3. Decide whether the task is:
   • pipeline server setup or deletion
   • pipeline definition, import, version, or GitOps/Kubernetes API storage
   • KFP SDK compilation or authentication
   • caching configuration
   • experiment, artifact, or run management
   • scheduling and duplicate-run workflow
   • workspace or external artifact use
   • logs, Elyra, or DSPA troubleshooting
   • reusable component catalog adoption and pipeline run lifecycle
4. Use examples/ai-pipelines-patterns.md for focused review patterns.
5. For repo KFP code edits, pair with rhoai-kfp-pipeline-authoring.
6. For live cluster work, follow the OpenShift safety guard in AGENTS.md.
7. Validate with references/validation-checklist.md.

References

• references/source-capture.md
• references/official-doc-extraction.md
• references/validation-checklist.md
• examples/ai-pipelines-patterns.md