draft: add resource limits

gantry/routes/prediction/prediction.py

@@ -42,6 +42,7 @@ async def predict(db: aiosqlite.Connection, spec: dict, strategy: str = None) ->
     predictions = {}
     if not sample:
         predictions = {
+            # TODO: do we want to avoid limits by default?
             "cpu_request": DEFAULT_CPU_REQUEST,
             "mem_request": DEFAULT_MEM_REQUEST,
         }
@@ -51,6 +52,8 @@ async def predict(db: aiosqlite.Connection, spec: dict, strategy: str = None) ->
             # averages the respective metric in the sample
             "cpu_request": sum([build[0] for build in sample]) / len(sample),
             "mem_request": sum([build[2] for build in sample]) / len(sample),
+            "cpu_limit": sum([build[1] for build in sample]) / len(sample),
+            "mem_limit": sum([build[3] for build in sample]) / len(sample),
         }
 
     if strategy == "ensure_higher":
@@ -63,6 +66,7 @@ async def predict(db: aiosqlite.Connection, spec: dict, strategy: str = None) ->
     if predictions["mem_request"] < 10_000_000:
         logger.warning(f"Warning: Memory request for {spec} is below 10MB")
         predictions["mem_request"] = DEFAULT_MEM_REQUEST
+    # TODO add similar checks for limits
 
     # convert predictions to k8s friendly format
     for k, v in predictions.items():
@@ -78,6 +82,8 @@ async def predict(db: aiosqlite.Connection, spec: dict, strategy: str = None) ->
             # and clogging up the code
             "KUBERNETES_CPU_REQUEST": predictions["cpu_request"],
             "KUBERNETES_MEMORY_REQUEST": predictions["mem_request"],
+            "KUBERNETES_CPU_LIMIT": predictions["cpu_limit"],
+            "KUBERNETES_MEMORY_LIMIT": predictions["mem_limit"],
         },
     }