rhoai-s3-object-storage-data

name	rhoai-s3-object-storage-data
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 authoring Red Hat OpenShift AI workbench workflows for S3-compatible object storage: creating a Boto3 S3 client from project-scoped connection environment variables, listing buckets, creating buckets, listing objects, downloading files, uploading files, copying objects between buckets, deleting objects, deleting empty buckets, formatting MinIO, Amazon S3, and other S3-compatible endpoints, troubleshooting endpoint, authentication, and network access, and handling self-signed certificate trust for in-cluster object stores. Do NOT use for connection type template administration (use rhoai-connection-types), storage class administration (use rhoai-storage-classes), broad certificate management (use rhoai-certificate-management), AI pipeline server artifact storage and pipeline object-store configuration (use rhoai-ai-pipelines), project/workbench/connection lifecycle (use rhoai-project-workflows), model registry/model serving storage, or live cluster changes without the OpenShift safety guard.

RHOAI S3 Object Storage Data

Use this skill for OpenShift AI user workflows that access S3-compatible object storage from workbenches 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 S3-compatible object storage guide to this repo's demo workflow and GitOps review model.

Scope

This skill covers:

• workbench prerequisites for using S3-compatible object storage
• project-scoped S3 credentials and connection handling
• Boto3 client setup from workbench environment variables
• bucket operations: list, create, and delete empty buckets
• object operations: list, download, upload, copy, and delete objects
• endpoint formatting for on-cluster MinIO, Amazon S3, and other S3-compatible stores
• connection verification and troubleshooting for endpoint reachability, authentication, and endpoint formatting
• self-signed certificate trust for in-cluster object stores and databases

Use other skills for adjacent work:

• rhoai-connection-types for administrator connection type templates and dashboard form design
• rhoai-project-workflows for creating projects, workbenches, project connections, and attaching connections to workbench workflows
• rhoai-storage-classes for OpenShift persistent storage classes and access modes
• rhoai-certificate-management for broader DSCInitialization trusted CA bundle policy and GitOps review
• rhoai-workbenches-custom-images and rhoai-workbench-image-import for workbench image behavior and package availability
• rhoai-ai-pipelines for AI Pipelines artifact storage, pipeline server object storage, workspaces, and pipeline object-store configuration
• rhoai-automl for AutoML training-data CSV requirements and AutoML S3-backed optimization run handoff
• rhoai-autorag for AutoRAG document source rules, JSON evaluation data, and generated notebook storage handoff
• rhoai-mlflow for MLflow artifact storage, project-specific S3 artifact overrides, and MLflowConfig review
• rhoai-evaluation for EvalHub custom data, LM-Eval S3 support, risk assessment artifacts, and disconnected translation-model cache handoff
• rhoai-kfp-pipeline-authoring for repo-specific KFP pipeline code that consumes object storage
• rhoai-distributed-workload-workflows for S3-compatible checkpoint storage in distributed training
• rhoai-model-registry and rhoai-model-serving-platform for model artifact storage consumption by registry or serving workflows

Demo Policy

For this repo:

• Treat S3-compatible object storage credentials as project-scoped secrets. Never share credentials across projects in examples or demo manifests.
• Do not commit access keys, secret keys, session tokens, endpoint secrets, or bucket-specific credentials.
• Prefer IAM or bucket policies that grant only the minimum permissions needed for the specific project bucket.
• Use environment variables injected by OpenShift AI connections when showing Boto3 client setup from a workbench.
• Keep Boto3 package installation as an explicit notebook/workbench step unless the selected workbench image already includes it.
• Verify endpoint formatting before blaming application code.
• Use HTTPS endpoints where the storage provider supports it.
• For on-cluster object stores or databases with self-signed certificates, route CA trust decisions through rhoai-certificate-management.
• Treat destructive operations, especially object and bucket deletion, as explicit demo steps with verification before and after.

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:
   • connection and credential review
   • Boto3 client setup
   • bucket listing or creation
   • object list/download/upload/copy/delete
   • bucket deletion
   • endpoint formatting
   • self-signed CA trust
   • troubleshooting
4. Use examples/s3-object-storage-patterns.md for focused review patterns.
5. Validate with references/validation-checklist.md.

References

• references/source-capture.md
• references/official-doc-extraction.md
• references/validation-checklist.md
• examples/s3-object-storage-patterns.md