Merge pull request #400 from sunya-ch/server-api-rebase-patch-3

fix: remove remaining intel_rapl source in script

Author: Sunil Thaha

model_training/script.sh

@@ -16,7 +16,7 @@ declare KIND_CLUSTER_NAME=${KIND_CLUSTER_NAME:-"kind-for-training"}
 declare KIND_REGISTRY_NAME=${KIND_REGISTRY_NAME:-"kind-registry-for-training"}
 
 declare PROM_SERVER=${PROM_SERVER:-"http://localhost:9090"}
-declare ENERGY_SOURCE=${ENERGY_SOURCE:-"intel_rapl,acpi"}
+declare ENERGY_SOURCE=${ENERGY_SOURCE:-"rapl-sysfs,acpi"}
 
 declare VERSION=${VERSION:-"latest"}
 declare PIPELINE_PREFIX=${PIPELINE_PREFIX:-"std_"}

src/kepler_model/estimate/estimator.py

@@ -14,7 +14,7 @@
 from kepler_model.estimate.model_server_connector import is_model_server_enabled, make_request
 from kepler_model.util.config import SERVE_SOCKET, download_path, set_env_from_model_config
 from kepler_model.util.loader import get_download_output_path
-from kepler_model.util.train_types import ModelOutputType, is_output_type_supported
+from kepler_model.util.train_types import ModelOutputType, convert_enery_source, is_output_type_supported
 
 ###############################################
 # power request
@@ -57,9 +57,7 @@ def handle_request(data: str) -> dict:
         return {"powers": dict(), "msg": msg}
 
     output_type = ModelOutputType[power_request.output_type]
-    # TODO: need revisit if get more than one rapl energy source
-    if power_request.energy_source is None or "rapl" in power_request.energy_source:
-        power_request.energy_source = "rapl-sysfs"
+    power_request.energy_source = convert_enery_source(power_request.energy_source)
 
     if output_type.name not in loaded_model:
         loaded_model[output_type.name] = dict()

src/kepler_model/estimate/model_server_connector.py

@@ -5,6 +5,7 @@
 
 import requests
 
+from kepler_model.server.model_server import ModelListParam
 from kepler_model.util.config import (
     download_path,
     get_model_server_list_endpoint,
@@ -13,7 +14,7 @@
 )
 from kepler_model.util.loader import get_download_output_path
 from kepler_model.util.train_types import ModelOutputType
-from kepler_model.server.model_server import ModelListParam
+
 
 # discover_spec: determine node spec in json format (refer to NodeTypeSpec)
 def discover_spec():

src/kepler_model/server/model_server.py

@@ -1,9 +1,9 @@
 import codecs
+import enum
 import logging
 import os
 import shutil
 import sys
-import enum
 
 import click
 import requests
@@ -39,6 +39,7 @@
     FeatureGroups,
     ModelOutputType,
     PowerSourceMap,
+    convert_enery_source,
     get_valid_feature_groups,
     weight_support_trainers,
 )
@@ -52,7 +53,7 @@
 class ModelRequest:
     def __init__(self, metrics, output_type, source="rapl-sysfs", node_type=-1, weight=False, trainer_name="", filter="", pipeline_name="", spec=None):
         # target source of power metric to be predicted (e.g., rapl-sysfs, acpi)
-        self.source = source
+        self.source = convert_enery_source(source)
         # type of node to select a model learned from similar nodes (default: -1, applied universal model learned by all node_type (TODO))
         self.node_type = node_type
         # list of available resource usage metrics to find applicable models (using a valid feature group that can be obtained from the list)
@@ -197,6 +198,7 @@ def get_available_models():
     fg = request.args.get(ModelListParam.FeatureGroup.value)
     ot = request.args.get(ModelListParam.OutputType.value)
     energy_source = request.args.get(ModelListParam.EnergySource.value)
+    energy_source = convert_enery_source(energy_source)
     node_type = request.args.get(ModelListParam.NodeType.value)
     filter = request.args.get(ModelListParam.Filter.value)
 

src/kepler_model/util/train_types.py

@@ -234,6 +234,11 @@ def is_weight_output(output_type):
         return True
     return False
 
+def convert_enery_source(energy_source: str) -> str:
+    # TODO: need revisit if get more than one rapl energy source
+    if energy_source is None or "rapl" in energy_source:
+        return "rapl-sysfs"
+    return energy_source
 
 if __name__ == "__main__":
     for g, g_features in FeatureGroups.items():