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A novel, open-source platform enabling organizations to define per-job optimization problems for ML training (hardware allocation, data-loading strategies, batch scheduling, hyperparameter budgets, gradient compression patterns) as Category-Theory-in
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README.md

CatOpt-Flow: Category-Theoretic Compositional Optimizer

CatOpt-Flow is a production-grade, open-source platform for defining and solving per-job optimization problems in multi-tenant ML training pipelines across heterogeneous accelerators.

Key abstractions (category-theory inspired)

  • Objects: local training tasks representing per-job optimization problems.
  • Morphisms: data-exchange channels with versioned schemas (signals like resource usage, gradient statistics, throughput metrics).
  • Functors: adapters mapping device-specific problems to a vendor-agnostic representation.
  • Limits/Colimits: global constraints and governance that aggregate local problems into a coherent global plan.
  • Delta-sync: a lightweight delta-based synchronization protocol enabling asynchronous updates and partial failures.
  • Schema registry and contract marketplace: plug-and-play adapters for major ML frameworks and hardware backends.
  • Code generation: orchestration stubs (Rust/C++) and Python bindings for rapid deployment.

What you get

  • A pragmatic, test-driven architecture suitable for large-scale, multi-tenant ML workloads.
  • A ready-to-extend core, with simple yet expressive primitives and a working ADMM-like solver MVP.
  • A packaging-ready Python distribution with tests that exercise the core primitives.

Getting started

  • This is a Python project. You can run tests and build the package with the provided script:
    • bash test.sh
  • The test suite validates core functionality: object/morphism relations, local/global planning, and an ADMM-like convergence flow.
  • The packaging step exercises Python packaging metadata and wheel/sdist generation.

What’s inside

  • catopt_flow_category_theoretic_compositi/core.py: core primitives (Object, Morphism, LocalProblem, GlobalProblem, Functor, Planner, DeltaSyncRegistry, ADMMNode, run_admm).
  • tests/test_core.py: unit tests for core primitives.
  • A minimal, production-ready packaging layout with pyproject.toml and a README hook.

Development and contribution

  • See AGENTS.md for architectural guidelines and contribution rules.
  • All changes should be reflected in tests and documented in this README.

Roadmap (high level)

  • Phase 0: protocol skeleton with 2 starter adapters per platform and delta-sync.
  • Phase 1: governance ledger with DID-based identities.
  • Phase 2: cross-domain demo with a simulated satellite domain.
  • Phase 3: hardware-in-the-loop validation.
  • A small DSL sketch (LocalProblem/SharedVariables/PlanDelta) and a Graph-of-Contracts registry.

This project aims for clean, production-grade code with strong test coverage and clear extension points.