docs: add ADR-0054 Kubeflow Pipeline CI/CD and ADR-0055 Internal Python Package Publishing
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decisions/0054-kubeflow-pipeline-cicd.md
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decisions/0054-kubeflow-pipeline-cicd.md
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# Kubeflow Pipeline CI/CD
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* Status: accepted
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* Date: 2026-02-13
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* Deciders: Billy
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* Technical Story: Automate compilation and upload of Kubeflow Pipelines on git push
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## Context and Problem Statement
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Kubeflow Pipelines are defined as Python scripts (`*_pipeline.py`) that compile to YAML IR documents. These must be compiled with `kfp` and then uploaded to the Kubeflow Pipelines API. Doing this manually is error-prone and easy to forget — a push to `main` should automatically make pipelines available in the Kubeflow UI.
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How do we automate the compile-and-upload lifecycle for Kubeflow Pipelines using the existing Gitea Actions CI infrastructure?
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## Decision Drivers
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* Pipeline definitions change frequently as new ML workflows are added
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* Manual `kfp pipeline upload` is tedious and easy to forget
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* Kubeflow Pipelines API is accessible within the cluster
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* Gitea Actions runners already exist (ADR-0031)
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* Notifications via ntfy are established (ADR-0015)
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## Considered Options
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1. **Gitea Actions workflow with in-cluster KFP API access**
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2. **Argo Events watching git repo, triggering Argo Workflow to upload**
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3. **CronJob polling for changes**
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4. **Manual upload via CLI**
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## Decision Outcome
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Chosen option: **Option 1 — Gitea Actions workflow**, because the runners are already in-cluster, the pattern is consistent with other CI workflows (ADR-0031), and it provides immediate feedback via ntfy.
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### Positive Consequences
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* Zero-touch pipeline deployment — push to main and pipelines appear in Kubeflow
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* Consistent CI pattern across all repositories
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* Version tracking with timestamped tags (`v20260213-143022`)
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* Existing pipelines get new versions; new pipelines are auto-created
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* ntfy notifications on success/failure
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### Negative Consequences
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* Requires NetworkPolicy to allow cross-namespace traffic (gitea → kubeflow)
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* Pipeline compilation happens in CI, not locally — compilation errors only surface in CI
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* KFP SDK version must be pinned in CI to match the cluster
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## Implementation
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### Workflow Structure
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The workflow (`.gitea/workflows/compile-upload.yaml`) has two jobs:
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| Job | Purpose |
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|-----|---------|
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| `compile-and-upload` | Find `*_pipeline.py`, compile each with KFP, upload YAML to Kubeflow |
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| `notify` | Send ntfy notification with compile/upload summary |
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### Pipeline Discovery
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```yaml
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on:
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push:
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branches: [main]
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paths:
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- "**/*_pipeline.py"
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- "**/*pipeline*.py"
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workflow_dispatch:
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```
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Pipelines are discovered at runtime with `find . -maxdepth 1 -name '*_pipeline.py'`, avoiding shell issues with glob expansion in CI variables. The `workflow_dispatch` trigger allows manual re-runs.
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### Upload Strategy
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The upload step uses an inline Python script with the KFP client:
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1. Connect to `ml-pipeline.kubeflow.svc.cluster.local:8888`
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2. For each compiled YAML:
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- Check if a pipeline with that name already exists
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- **Exists** → upload as a new version with timestamp tag
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- **New** → create the pipeline
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3. Report uploaded/failed counts as job outputs
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### NetworkPolicy Requirement
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Gitea Actions runners run in the `gitea` namespace. Kubeflow's NetworkPolicies default-deny cross-namespace ingress. A dedicated policy was added:
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```yaml
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apiVersion: networking.k8s.io/v1
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kind: NetworkPolicy
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metadata:
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name: allow-gitea-ingress
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namespace: kubeflow
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spec:
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podSelector: {}
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policyTypes:
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- Ingress
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ingress:
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- from:
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- namespaceSelector:
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matchLabels:
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kubernetes.io/metadata.name: gitea
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```
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This joins existing policies for envoy (external access) and ai-ml namespace (pipeline-bridge, kfp-sync-job).
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### Notification
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The `notify` job sends a summary to `ntfy.observability.svc.cluster.local:80/gitea-ci` including:
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- Compile count and upload count
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- Version tag
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- Failed pipeline names (on failure)
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- Clickable link to the CI run in Gitea
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## Current Pipelines
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| Pipeline | Purpose |
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|----------|---------|
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| `document_ingestion_pipeline` | RAG document processing with MLflow |
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| `evaluation_pipeline` | Model evaluation |
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| `dvd_transcription_pipeline` | DVD audio → transcript via Whisper |
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| `qlora_pdf_pipeline` | QLoRA fine-tune on PDFs from S3 |
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| `voice_cloning_pipeline` | Speaker extraction + VITS voice training |
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| `vllm_tuning_pipeline` | vLLM inference parameter tuning |
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## Links
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* Related to [ADR-0009](0009-dual-workflow-engines.md) (Kubeflow Pipelines)
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* Related to [ADR-0013](0013-gitea-actions-for-ci.md) (Gitea Actions)
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* Related to [ADR-0015](0015-ci-notifications-and-semantic-versioning.md) (ntfy notifications)
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* Related to [ADR-0031](0031-gitea-cicd-strategy.md) (Gitea CI/CD patterns)
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* Related to [ADR-0043](0043-cilium-cni-network-fabric.md) (NetworkPolicy)
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decisions/0055-internal-python-package-publishing.md
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decisions/0055-internal-python-package-publishing.md
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# Internal Python Package Publishing
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* Status: accepted
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* Date: 2026-02-13
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* Deciders: Billy
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* Technical Story: Publish reusable Python packages to Gitea's built-in PyPI registry with automated CI
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## Context and Problem Statement
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Shared Python libraries like `mlflow_utils` are used across multiple projects (handler-base, Kubeflow pipelines, Argo workflows). Currently these are consumed via git dependencies or copy-paste. This is fragile — there's no versioning, no quality gate, and no single source of truth for installed versions.
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How do we publish internal Python packages so they can be installed with `pip install` / `uv add` from a private registry, with automated quality checks and versioning?
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## Decision Drivers
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* Shared libraries are consumed by multiple services and pipelines
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* Need version pinning for reproducible builds
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* Quality gates (lint, format, test) should run before publishing
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* Must work with `uv`, `pip`, and KFP container images
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* Self-hosted — no PyPI.org or external registries
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* Consistent with existing CI patterns (ADR-0031, ADR-0015)
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## Considered Options
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1. **Gitea's built-in PyPI registry** with CI-driven publish
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2. **Private PyPI server** (pypiserver or devpi)
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3. **Git-based dependencies** (`pip install git+https://...`)
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4. **Vendored copies** in each consuming repository
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## Decision Outcome
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Chosen option: **Option 1 — Gitea's built-in PyPI registry**, because Gitea already provides a packages API with PyPI compatibility, eliminating the need for another service. Combined with `uv build` and `twine upload`, the publish workflow is minimal.
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### Positive Consequences
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* Standard `pip install mlflow-utils --index-url ...` works everywhere
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* Semantic versioning with git tags provides clear release history
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* Lint + format + test gates prevent broken packages from publishing
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* No additional infrastructure — Gitea handles package storage
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* Consuming projects can pin exact versions
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### Negative Consequences
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* Registry credentials must be configured as CI secrets per repo
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* Gitea's PyPI registry is basic (no yanking, no project pages)
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* Version conflicts possible if consumers don't pin
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## Implementation
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### Package Structure
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```
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mlflow/
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├── pyproject.toml # hatchling build, ruff+pytest dev deps
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├── uv.lock # Locked dependencies
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├── mlflow_utils/
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│ ├── __init__.py
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│ ├── client.py
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│ ├── tracker.py
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│ ├── inference_tracker.py
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│ ├── model_registry.py
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│ ├── kfp_components.py
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│ ├── experiment_comparison.py
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│ └── cli.py # CLI entrypoint: mlflow-utils
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└── tests/
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└── test_smoke.py # Import validation for all modules
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```
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### CI Workflow
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Four jobs in `.gitea/workflows/ci.yaml`:
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| Job | Purpose | Gate |
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|-----|---------|------|
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| `lint` | `ruff check` + `ruff format --check` | Must pass |
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| `test` | `pytest -v` | Must pass |
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| `publish` | Build + upload to Gitea PyPI + tag | After lint+test, main only |
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| `notify` | ntfy success/failure notification | Always |
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### Key Design Decisions
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**uv over pip for CI**: All jobs use `uv` installed via `curl -LsSf https://astral.sh/uv/install.sh | sh` rather than the `astral-sh/setup-uv` GitHub Action, which is unavailable in Gitea's act runner. `uv sync --frozen --extra dev` ensures reproducible installs from the lockfile.
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**uvx twine for publishing**: Rather than `uv pip install twine --system` (blocked by Debian's externally-managed environment), `uvx twine upload` runs twine in an ephemeral virtual environment.
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**Semantic versioning from commit messages**: Same pattern as ADR-0015 — commit prefixes (`major:`, `feat:`, `fix:`) determine version bumps. The publish step patches `pyproject.toml` at build time via `sed`, builds with `uv build`, uploads with `twine`, then tags.
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### Registry Configuration
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| Setting | Value |
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|---------|-------|
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| **Registry URL** | `http://gitea-http.gitea.svc.cluster.local:3000/api/packages/daviestechlabs/pypi` |
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| **Auth** | `REGISTRY_USER` + `REGISTRY_TOKEN` repo secrets (Gitea admin credentials) |
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| **External URL** | `https://git.daviestechlabs.io/api/packages/daviestechlabs/pypi/simple/` |
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### Consuming Packages
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From any project or Dockerfile:
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```bash
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# uv
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uv add mlflow-utils --index-url https://git.daviestechlabs.io/api/packages/daviestechlabs/pypi/simple/
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# pip
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pip install mlflow-utils --index-url https://git.daviestechlabs.io/api/packages/daviestechlabs/pypi/simple/
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```
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### Quality Gates
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| Tool | Check | Config |
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|------|-------|--------|
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| ruff check | Lint rules (F, E, W, I) | `line-length = 120` in pyproject.toml |
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| ruff format | Code formatting | Consistent with check config |
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| pytest | Import smoke tests, unit tests | `kfp` auto-skipped if not installed |
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## Future Packages
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This pattern applies to any shared Python library:
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| Candidate | Repository | Status |
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|-----------|-----------|--------|
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| `mlflow-utils` | `mlflow` | Published |
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| `handler-base` | `handler-base` | Candidate |
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| `ray-serve-apps` | `ray-serve` | Candidate |
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## Links
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* Related to [ADR-0012](0012-use-uv-for-python-development.md) (uv for Python)
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* Related to [ADR-0015](0015-ci-notifications-and-semantic-versioning.md) (semantic versioning)
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* Related to [ADR-0031](0031-gitea-cicd-strategy.md) (Gitea CI/CD patterns)
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* Related to [ADR-0047](0047-mlflow-experiment-tracking.md) (mlflow_utils library)
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* Updates [ADR-0020](0020-internal-registry-for-cicd.md) (internal registry — now includes PyPI)
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