deployments

name	deployments
description	Register, change, or debug Prefect deployments in this repo — schedules, triggers, parameters, work pools, job variables, secret injection, or dependency/python-version problems in flow runs. Use when editing prefect.yaml or when a flow run fails during pull or dependency install.

deployments

prefect.yaml is the source of truth for schedules, triggers, parameters, and per-deployment job variables. After any dependency or flow change:

just prefect deploy --all

flow code is never baked in

It is pulled at runtime. The pull: steps clone main fresh per run:

pull:
  - prefect.deployments.steps.run_shell_script:
      script: |
        bash -c "rm -rf my-prefect-server && (git clone --depth 1 --branch main https://tangled.sh/zzstoatzz.io/my-prefect-server.git my-prefect-server || git clone --depth 1 --branch main https://github.com/zzstoatzz/my-prefect-server.git my-prefect-server)"
  - prefect.deployments.steps.set_working_directory:
      directory: my-prefect-server

Two consequences that matter constantly:

• Flow code never goes in worker images or ConfigMaps. Don't put it there.
• Prod runs pushed main, not your working tree. An uncommitted change is not live, and a commit that isn't pushed is not live either. Conversely, a push changes prod behavior on the next run with no deploy step.

dependencies install per run

The command job variable builds an ephemeral venv before pull steps run:

command: >-
  uv run
  --refresh-package my-prefect-server
  --with 'my-prefect-server @ git+https://github.com/zzstoatzz/my-prefect-server.git'
  prefect flow-run execute

Per-deployment overrides go in work_pool.job_variables.command, not at the deployment root. Example: --python 3.13 for dbt compatibility. requires-python is >=3.13 (not 3.14) precisely so the transform flow can run dbt under 3.13.

Heavy extras are opt-in: the atlas extra (numpy/scipy/umap/hdbscan/numba, ~250MB) installs via my-prefect-server[atlas] @ git+... only for phi-atlas, so other flows don't pull ML libs they never use. numba>=0.60 has a floor for a real reason — without it uv backtracks numba to a py<3.10 sdist that crashed phi-atlas daily from 2026-06-22.

secrets

Flow runtime secrets (ANTHROPIC_API_KEY, TURBOPUFFER_API_KEY, CLOUDFLARE_API_TOKEN, TURSO_*) are not in .env. They are Prefect Secret blocks, injected into flow runs via job_variables.env in prefect.yaml, resolved at prefect deploy time. Flow code does not call the Secret API for these.

The exception is a block whose shape matters to the flow — e.g. hetzner-tokens is a dict of {label: token}, one per Hetzner Cloud project, because Hetzner has no account-wide token. That one is loaded in-flow via Secret.load(...), since an env var can't express a mapping.

work pools

Two pools are defined as YAML anchors, *home and *k8s.

home-pool (process, on heavypad) is where flow execution actually happens. Process flow runs execute in an ephemeral /tmp dir, so persistent analytics paths must be set explicitly via job_variables.env:

ANALYTICS_DB_PATH: /home/stoat/prefect-analytics/analytics.duckdb
LLM_SPEND_LOG_PATH: /home/stoat/prefect-analytics/llm-spend.jsonl
PREFECT_LOCAL_STORAGE_PATH: /home/stoat/prefect-analytics/storage

These are hostPaths on the home box, not a k8s PVC.

kubernetes-pool is a retained-but-unused fallback — no k8s worker runs in normal operation. If you ever revive it: the base job template defaults namespace to default and must be prefect; the worker image must be the -kubernetes tag (prefecthq/prefect:3-python3.14-kubernetes, which has uv pre-installed); and PREFECT_INTEGRATIONS_KUBERNETES_OBSERVER_NAMESPACES=prefect is what makes namespace-scoped RBAC work.

conventions

• Never pip or uv pip — uv add, uv sync, uv run.
• We maintain prefect-dbt; never replace PrefectDbtOrchestrator with subprocess calls.
• Prefect docs are on disk at ~/github.com/prefecthq/prefect/docs — read before guessing.

related

Where runs execute: [[home-pool-worker]]. Running one by hand: [[adhoc-runs]].