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GNN CLI Module

Overview

Unified command-line interface for the GNN pipeline. Provides 12 subcommands for running, validating, rendering, and managing GNN models.

Entry point: gnn = "src.cli:main" (defined in pyproject.toml)

Subcommands

Command Description
gnn run Execute the full 25-step pipeline
gnn validate <file> Validate a GNN file (sections, state-space, connections, dimensions)
gnn parse <file> Parse a GNN file and output JSON/YAML/summary
gnn render <file> Render a GNN file to a specific framework (pymdp, rxinfer, jax, etc.)
gnn report Generate pipeline report from existing outputs
gnn reproduce <hash> Re-run from a previous run hash (content-addressable)
gnn preflight Run environment & config checks
gnn health Show renderer & dependency status (8/8 renderers)
gnn serve Start Pipeline-as-a-Service API (FastAPI)
gnn lsp Launch GNN Language Server (stdio)
gnn watch <dir> Monitor directory and live-reparse on file change
gnn graph <file> Generate dependency graph from multi-model files

Usage

# Full pipeline
gnn run --target-dir input/gnn_files --verbose

# Validate a model
gnn validate input/gnn_files/discrete/actinf_pomdp_agent.md --strict

# Parse to JSON
gnn parse input/gnn_files/discrete/actinf_pomdp_agent.md

# Check environment
gnn preflight
gnn health

Architecture

The CLI module is a thin dispatcher — each subcommand delegates to the corresponding module's public API:

  • run → main.main()
  • validate → gnn.schema.validate_required_sections() + parse_state_space() + parse_connections()
  • parse → gnn.schema.parse_state_space() + gnn.frontmatter.parse_frontmatter()
  • render → render.processor (planned full integration)
  • report → report.pipeline_report.generate_pipeline_report()
  • reproduce → pipeline.hasher.lookup_run() + main.main(override_args=...)
  • preflight → pipeline.preflight.run_preflight()
  • health → render.health.check_renderers() + pipeline.preflight.check_environment()
  • serve → api.app.start_server()
  • lsp → lsp.start_server()
  • watch → gnn.watcher.GNNWatcher()
  • graph → gnn.dep_graph.render_graph_from_file()

References