catopt-flow-category-theoretic-compositi/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.

Adapters (Starter)

• This repo now includes two starter adapters to bootstrap cross-framework optimization flows:
• PyTorchAdapter and TensorFlowAdapter
• Path: catopt_flow_category_theoretic_compositi/adapters
• Each adapter implements a minimal adapt(local_problems) -> GlobalProblem using the core Planner.
• Example usage:
• from catopt_flow_category_theoretic_compositi.adapters import PyTorchAdapter
• adapter = PyTorchAdapter(budget_gpu=2.0)
• global_plan = adapter.adapt(local_problems)