refactor(benchmarks): extract library API from CLI (#21)

* refactor(benchmarks): extract library API from CLI for programmatic usage

Extract core VM management and pool lifecycle logic from cli.py into
importable modules (azure_vm.py, pool.py) with clean Python APIs.

- Add AzureVMManager class with Azure SDK primary path + az CLI fallback
- Add PoolManager class for pool create/wait/run/cleanup lifecycle
- Add configurable resource_group via Settings, env var, or --resource-group flag
- Support DefaultAzureCredential for enterprise SSO/service principals
- CLI handlers become thin wrappers delegating to library classes
- Add agent_factory parameter stub on PoolManager.run() for pluggable agents

All 327 tests pass, CLI surface unchanged.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* style: fix pre-existing ruff lint errors in pool_viewer and resource_tracker

Remove unused import `json` and unused variable `worker_re` in
pool_viewer.py, and unused import `Optional` in resource_tracker.py.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* style: run ruff formatter on benchmarks modules

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(azure_vm): add SDK path for set_auto_shutdown via generic resource API

Auto-shutdown schedules are Microsoft.DevTestLab/schedules resources.
Use azure-mgmt-resource (already a dependency) to create them via the
generic resource client, with az CLI fallback if SDK fails.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

Author: abrichr

openadapt_ml/benchmarks/__init__.py

@@ -1,7 +1,10 @@
 """Benchmark integration for openadapt-ml.
 
-This module provides ML-specific agents for benchmark evaluation.
-These agents wrap openadapt-ml internals (trained policies, API adapters).
+This module provides:
+
+1. ML-specific agents for benchmark evaluation (PolicyAgent, APIBenchmarkAgent, etc.)
+2. Azure VM management with clean Python API (AzureVMManager)
+3. Pool management for parallel WAA evaluation (PoolManager)
 
 For benchmark infrastructure (adapters, runners, viewers), use openadapt-evals:
     ```python
@@ -12,20 +15,32 @@
     )
     ```
 
-ML-specific agents (only available in openadapt-ml):
-    - PolicyAgent: Wraps openadapt_ml.runtime.policy.AgentPolicy
-    - APIBenchmarkAgent: Uses openadapt_ml.models.api_adapter.ApiVLMAdapter
-    - UnifiedBaselineAgent: Uses openadapt_ml.baselines adapters
+Library usage (programmatic, no CLI):
+    ```python
+    from openadapt_ml.benchmarks import PoolManager, AzureVMManager
+
+    vm = AzureVMManager(resource_group="my-rg")
+    manager = PoolManager(vm_manager=vm)
+    pool = manager.create(workers=4)
+    manager.wait()
+    result = manager.run(tasks=10)
+    manager.cleanup(confirm=False)
+    ```
 """
 
 from openadapt_ml.benchmarks.agent import (
     APIBenchmarkAgent,
     PolicyAgent,
     UnifiedBaselineAgent,
 )
+from openadapt_ml.benchmarks.azure_vm import AzureVMManager
+from openadapt_ml.benchmarks.pool import PoolManager, PoolRunResult
 
 __all__ = [
     "PolicyAgent",
     "APIBenchmarkAgent",
     "UnifiedBaselineAgent",
+    "AzureVMManager",
+    "PoolManager",
+    "PoolRunResult",
 ]