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ofplang schedule

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A scheduler for Object-flow Programming Language v0 — a YAML-based dataflow workflow IR with linear Object tracking. The language is defined in the ofplang/spec repository.

The scheduler takes a portable v0 workflow plus an execution environment definition and plans when its work runs; it also replans from an execution status. The design is documented in docs/SPECIFICATIONS.md.

Status: the schema validators (environment definition and execution document, spec §9) and the scheduler are implemented: it produces an optimal plan for a single workflow with mode selection, spot/device occupancy, and transport, pins a workflow's boundary material to spots via an interface (spec §6.8), respects device-local consumable resources — what a mode draws and what a replenishment puts back (spec §4.7) — and replans from an execution document (--document) by fixing completed/running activities and re-optimising the rest at or after now. A visualize command renders a plan as a self-contained SVG/HTML Gantt chart. The model is documented in docs/FORMULATION.md.

This is a fresh implementation that targets the spec directly. The prototype ofp-scheduler (OR-Tools CP-SAT) is a reference for ideas but not a dependency.

Install

pip install ofplang-schedule

Requires Python 3.10+. Runtime dependencies are PyYAML, OR-Tools (the CP-SAT solver used by the scheduler), and the sibling ofplang-validate (pulled in automatically), which the CLI's front-door check uses. The scheduler library never imports validate, so embedders that only call ofplang.schedule take no validation overhead.

For development, install editable with the test extra from a clone:

pip install -e ".[test]"

Command line

ofp-schedule validate <file>...                 # validate an environment or a plan/status
ofp-schedule schedule <workflow> --env <env> [--document doc.yaml] [--running-margin N] [--seed N] [--no-validate] [-o plan.yaml] [--format yaml|json]
ofp-schedule visualize <plan|status> [--view device|workflow|lane] [--theme light|dark|auto] [--format svg|html] [-o FILE]

validate auto-detects whether the file is an environment definition or an execution document (pass --kind to force it); diagnostics are reported as file:line:col: <severity> <code>. schedule produces an execution plan (§6), minimising the objective the document declares (§4.8; makespan, then the number of refills). A --document (execution document, §6) supplies the interface boundary constraint (§6.8, where a workflow's entry inputs / final outputs sit), the inventories a run starts with (§6.10) where devices hold consumables, the objective (§6.1, now its only declaration site) and, when it sets now, the prior status to replan from (§7) — emitting the full timeline (fixed history + re-optimised future) that round-trips as the next status input. By default the solve is non-deterministic (a multi-worker search that may return a different equally-optimal schedule each run); --seed N makes it reproducible by fixing the CP-SAT seed and using a single worker. --ignore-resources switches consumables off (§4.7.3): the declarations are still checked for shape but nothing is applied, and the plan is shaped as it would be from an environment that never declared one — a relaxation, so it never turns a solvable instance unsolvable. --no-validate skips the one-shot ofplang-validate front-door check of the workflow — use it when the workflow was already validated upstream (e.g. by the ofp umbrella CLI); $import is still resolved, since that is structural rather than a validation check. visualize renders any §6 execution document — a plan, or the status a finished run produced — as a self-contained Gantt chart, either SVG (fixed colours, transparent background, PowerPoint-safe) or HTML. --format chooses; without it the output is SVG, except that an -o path ending in .html or .htm is taken as asking for HTML, and an explicit --format always wins — --format svg -o chart.html writes SVG. Exit codes: 0 success, 1 validation errors or no feasible schedule, 2 usage/input error.

This tool is also the schedule subcommand of the umbrella ofp CLI (ofplang), which forwards to it in-process with this CLI's own subcommands intact: ofp schedule schedule …, ofp schedule visualize …, each with the same options and exit codes as above.

Feature support

v0 defines seven optional features (spec §4.2), and a document requiring one an implementation does not have "is valid v0 but unsupported by that implementation" (§4.1). So ofp-validate accepting a workflow does not mean this scheduler can plan it:

v0 feature ofplang-schedule
python_script_processes Supported. A script process is scheduled like any atomic one; its mode duration is the estimate of the compute cost. Running the script is the runner's job.
scheduling_policies Accepted, then ignored: §23 makes these best-effort preferences, and a composite's scheduling section is dropped when the composite is flattened. The report's diagnostics carry a scheduling_policies_ignored warning.
node_map, node_fold, node_do_while, node_branch Not supported. A structured node reshapes dataflow in ways the flat scheduler graph cannot represent, so it is refused with unsupported_feature.
generic_processes Not supported. Refused with unsupported_feature.

Library

from ofplang.schedule import schedule

report = schedule(workflow, environment, document_path=status)  # -> ScheduleReport

Each input is either a path or an already-loaded document (a mapping), so an embedder that holds them in memory — a rolling-horizon runner rendering a fresh status every replan — passes them straight in, with no temporary files and nothing re-parsed. An in-memory document is read, never written to, and the plan it produces shares no structure with it. Because such a document has no file to point at, its diagnostics carry no file:line:col and locate by their path instead, and the plan's meta provenance reads <in-memory> unless the caller names the original file (workflow_source / environment_source / document_source).

The package lives under the ofplang PEP 420 namespace (ofplang.schedule), shared across the organization's tools.

Examples

examples/ holds complete workflow + environment pairs used to drive and eyeball the scheduler: a minimal source → target, a workflow with boundary material pinned by an interface, two jobs on a two-transporter fleet, a plate-reformatting DAG, and a parametric generator that scales the instance up. Each comes with its solved plan and a rendered chart under examples/outputs/.

Tests

pytest

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