Create tpu_manager.py

services/tpu_manager.py

@@ -0,0 +1,97 @@
+# ===== FILE: services/tpu_manager.py (Intelligent Orchestrator) =====
+import os
+import google.cloud.tpu_v2 as tpu
+
+class TPUComputeManager:
+    def __init__(self, master_framework_instance):
+        self.mf = master_framework_instance
+        # We will use the official Google Cloud TPU client library for management.
+        # self.tpu_client = tpu.TpuClient() # This will be added when we install the library
+        
+        # --- DEFINITIVE RESOURCE POOLS (Based on the TRC Grant) ---
+        
+        # The On-Demand Pool is our single, most reliable resource.
+        # It is reserved for the highest priority tasks (like the Oracle Core).
+        self.on_demand_pool = {
+            "zone": "us-central2-b",
+            "accelerator_type": "v4-32", # Assuming we use the 32 chips as a single pod slice
+            "name": "aetherius-oracle-core"
+        }
+
+        # The Spot Pools are our powerful, distributed, but ephemeral resources.
+        # They are the default for batch jobs like the Harvester Fleet and Architect Guild.
+        # The orchestrator will try these in order.
+        self.spot_pools = [
+            {
+                "zone": "us-east1-d",
+                "accelerator_type": "v6e-64", # Note: v6e (TPU v5p) might have different naming
+                "name_prefix": "aetherius-harvester-useast1"
+            },
+            {
+                "zone": "us-central2-b",
+                "accelerator_type": "v4-32",
+                "name_prefix": "aetherius-architect-uscentral2"
+            },
+            {
+                "zone": "europe-west4-a",
+                "accelerator_type": "v6e-64",
+                "name_prefix": "aetherius-harvester-euwest4a"
+            },
+            {
+                "zone": "europe-west4-b",
+                "accelerator_type": "v5e-64", # Note: v5e might have different naming
+                "name_prefix": "aetherius-harvester-euwest4b"
+            },
+            {
+                "zone": "us-central1-a",
+                "accelerator_type": "v5e-64",
+                "name_prefix": "aetherius-harvester-uscentral1"
+            },
+        ]
+        
+        print("TPU Compute Manager: Definitive Resource Pools Configured.", flush=True)
+
+    def launch_training_job(self, script_path: str, is_critical: bool = False):
+        """
+        Launches a training job (like the Architect Guild's learning cycle).
+        It will prioritize spot instances unless the job is marked critical.
+        """
+        self.mf.add_to_short_term_memory(f"Orchestrator: Received request to launch training job: {script_path}")
+        
+        # --- PRIORITY LOGIC ---
+        if not is_critical:
+            # 1. Attempt to acquire a Spot instance first
+            for pool in self.spot_pools:
+                try:
+                    # This is a conceptual representation of the API call.
+                    # It would use gcloud or the TPU client library to request a queued resource.
+                    print(f"Orchestrator: Attempting to acquire Spot TPU from pool: {pool['zone']}...")
+                    # ... code to request and run job on a spot instance ...
+                    self.mf.add_to_short_term_memory(f"Orchestrator: Job successfully launched on Spot TPU in {pool['zone']}.")
+                    return "Job launched successfully on a Spot instance."
+                except Exception as e:
+                    # This error would indicate the spot capacity is unavailable.
+                    print(f"Orchestrator: Could not acquire Spot TPU from {pool['zone']}. Reason: {e}. Trying next pool...")
+                    continue # Try the next spot pool
+        
+        # 2. If all spot pools fail, or if the job is critical, failover to On-Demand
+        print("Orchestrator: All Spot pools unavailable or job is critical. Failing over to On-Demand pool...")
+        try:
+            # This is our fallback. Use the precious on-demand resource.
+            # ... code to request and run job on the on-demand instance ...
+            self.mf.add_to_short_term_memory("Orchestrator: Job successfully launched on On-Demand TPU as a fallback.")
+            return "Job launched successfully on the On-Demand instance."
+        except Exception as e:
+            print(f"Orchestrator CRITICAL FAILURE: Could not acquire any TPU resource. Reason: {e}")
+            self.mf.add_to_short_term_memory("Orchestrator: CRITICAL FAILURE. All TPU resources are unavailable.")
+            return f"Error: All TPU resources (Spot and On-Demand) are currently unavailable. Reason: {e}"
+
+    def run_oracle_query(self, query_vector):
+        """
+        Runs a query on the Oracle Core. This is always a high-priority,
+        on-demand task.
+        """
+        # This function would be designed to communicate directly with the
+        # models running on the provisioned on-demand TPU in us-central2-b.
+        # Its logic assumes that resource is always available.
+        pass