build(agent): new-agents-3#dd492b iteration

README.md

@@ -39,3 +39,13 @@ Roadmap (high level)
 - 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)

catopt_flow_category_theoretic_compositi/adapters/__init__.py

@@ -0,0 +1,4 @@
+from .pytorch_adapter import PyTorchAdapter
+from .tensorflow_adapter import TensorFlowAdapter
+
+__all__ = ["PyTorchAdapter", "TensorFlowAdapter"]

catopt_flow_category_theoretic_compositi/adapters/pytorch_adapter.py

@@ -0,0 +1,19 @@
+from typing import List
+
+from ..core import LocalProblem, GlobalProblem, Planner
+
+
+class PyTorchAdapter:
+    """Starter PyTorch adapter that maps a collection of LocalProblems to a GlobalProblem
+    using the planner with a given GPU budget.
+    """
+
+    name = "pytorch"
+
+    def __init__(self, budget_gpu: float = 1.0) -> None:
+        self.budget_gpu = budget_gpu
+
+    def adapt(self, local_problems: List[LocalProblem]) -> GlobalProblem:
+        # Simple, vendor-agnostic mapping via Planner. Could be extended with PyTorch-specific
+        # resource modeling, hooks, and data-plane metadata in the future.
+        return Planner.build_global_plan(local_problems, self.budget_gpu)

catopt_flow_category_theoretic_compositi/adapters/tensorflow_adapter.py

@@ -0,0 +1,17 @@
+from typing import List
+
+from ..core import LocalProblem, GlobalProblem, Planner
+
+
+class TensorFlowAdapter:
+    """Starter TensorFlow adapter that maps a collection of LocalProblems to a GlobalProblem
+    using the planner with a given GPU budget.
+    """
+
+    name = "tensorflow"
+
+    def __init__(self, budget_gpu: float = 1.0) -> None:
+        self.budget_gpu = budget_gpu
+
+    def adapt(self, local_problems: List[LocalProblem]) -> GlobalProblem:
+        return Planner.build_global_plan(local_problems, self.budget_gpu)