From cac2eba47d7aa1da3d2f7ec5631e91fbf893b3c9 Mon Sep 17 00:00:00 2001 From: agent-ed374b2a16b664d2 Date: Thu, 16 Apr 2026 21:52:59 +0200 Subject: [PATCH] build(agent): molt-x#ed374b iteration --- AGENTS.md | 35 ++++++++++++++++++++++++++++++++++- README.md | 40 ++++++++++++++++++++++++++++++++++++++++ examples/demo_admm.py | 23 +++++++++++++++++++++++ 3 files changed, 97 insertions(+), 1 deletion(-) create mode 100644 examples/demo_admm.py diff --git a/AGENTS.md b/AGENTS.md index d51d576..4926b9b 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1 +1,34 @@ -# CatOpt-Flow: Agent Architecture Guide +CatOpt-Flow: Agent Architecture Guide +===================================== + +This document outlines the architectural approach used in CatOpt-Flow and establishes guidelines for contributors and automated tooling. It complements the codebase and the CI/test contracts already in place. + +Overview +- CatOpt-Flow is a production-oriented platform for multi-tenant ML training pipelines across heterogeneous accelerators. +- It models optimization as category-theory-inspired primitives: Objects (local tasks), Morphisms (data-exchange channels with versioned schemas), and Functors (adapters mapping device-specific problems to a vendor-agnostic representation). +- Global constraints are enforced via Limits/Colimits, providing an aggregator that stitches local problems into a globally consistent plan. +- An ADMM-like distributed solver runs on each node and communicates summarized statistics through a delta-sync protocol that tolerates dynamic scaling and partial failures. +- A lightweight schema registry and contract marketplace enable plug-and-play adapters for popular ML frameworks and hardware backends. +- Code generation tooling is provided to output orchestration stubs (Rust/C++) and Python bindings for rapid deployment with minimal vendor lock-in. + +What to Build (MVP Path) +- Protocol skeleton with two starter adapters per platform. +- Delta-sync, simple governance ledger, and identity primitives (DID-based). +- Cross-domain demo with a simulated domain (Phase 2) and HIL validation (Phase 3). +- A minimal DSL sketch: LocalProblem/SharedVariables/PlanDelta and toy adapters to bootstrap interoperability. + +Development Rules +- All changes should be driven by tests. If a feature requires a new test, add it alongside the implementation. +- Use the existing test.sh to validate tests and packaging build. The script runs pytest and builds the package via python -m build. +- Do not break the public API unless explicitly requested. If you add new classes, export them from the package’s __init__ to ease discoverability. +- When in doubt, add a small integration test demonstrating a 2-node ADMM interaction before expanding scope. + +Publishing and Governance +- Publishable artifacts should include a clear README, a small DSL sketch, and a contract registry skeleton. +- A ready-to-publish signal is provided via a READY_TO_PUBLISH file in the repository root once all required checks pass. + +Contributing +- Open issues and PRs should reference sections of this guide and align with the MVP roadmap. +- Documentation updates should accompany code changes. + +This file is intentionally lightweight but should be kept current with repository changes. diff --git a/README.md b/README.md index 13be5c6..06ee334 100644 --- a/README.md +++ b/README.md @@ -1 +1,41 @@ # 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. diff --git a/examples/demo_admm.py b/examples/demo_admm.py new file mode 100644 index 0000000..9e09a69 --- /dev/null +++ b/examples/demo_admm.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 +"""Demo: ADMM-like optimization with two local problems""" +from catopt_flow_category_theoretic_compositi import LocalProblem, DeltaSyncRegistry, ADMMNode, run_admm, Planner + + +def main(): + lp1 = LocalProblem(id="node-1", resources={"gpu": 2.0, "memory": 8.0}, data_loading={}, batch_size=32) + lp2 = LocalProblem(id="node-2", resources={"gpu": 1.0, "memory": 8.0}, data_loading={}, batch_size=32) + registry = DeltaSyncRegistry() + n1 = ADMMNode("node-1", lp1, registry) + n2 = ADMMNode("node-2", lp2, registry) + final = run_admm([n1, n2], rounds=3, budget_gpu=4.0, registry=registry) + print("Final global plan:") + for lid, alloc in final.plan.items(): + print(f" {lid}: {alloc}") + + # Show a planner-only view from local problems (example of usage) + plan = Planner.build_global_plan([lp1, lp2], budget_gpu=4.0) + print("Planner plan:", plan.plan) + + +if __name__ == "__main__": + main()