feat: get trainer from model_name in weight

data/model_weight/acpi_AbsPowerModel.json

@@ -1 +1 @@
-{"platform": {"All_Weights": {"Bias_Weight": 220.9079278650894, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 29.028228361462897}}}}}
+{"model_name": "SGDRegressorTrainer_0", "platform": {"All_Weights": {"Bias_Weight": 220.9079278650894, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 29.028228361462897}}}}}

data/model_weight/acpi_DynPowerModel.json

@@ -1 +1 @@
-{"platform": {"All_Weights": {"Bias_Weight": 49.56491877218095, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 28.501356366108837}}}}}
+{"model_name": "SGDRegressorTrainer_0", "platform": {"All_Weights": {"Bias_Weight": 49.56491877218095, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 28.501356366108837}}}}}

data/model_weight/intel_rapl_AbsPowerModel.json

@@ -1 +1 @@
-{"package": {"All_Weights": {"Bias_Weight": 69.91739430907396, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 22.16772409328642}}}}, "core": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "uncore": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "dram": {"All_Weights": {"Bias_Weight": 47.142633336743344, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 3.57348245077466}}}}}
+{"model_name": "SGDRegressorTrainer_0", "package": {"All_Weights": {"Bias_Weight": 69.91739430907396, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 22.16772409328642}}}}, "core": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "uncore": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "dram": {"All_Weights": {"Bias_Weight": 47.142633336743344, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 3.57348245077466}}}}}

data/model_weight/intel_rapl_DynPowerModel.json

@@ -1 +1 @@
-{"package": {"All_Weights": {"Bias_Weight": 38.856412561925055, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 22.258830113477515}}}}, "core": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "uncore": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "dram": {"All_Weights": {"Bias_Weight": 9.080889901856153, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 3.0358946796490924}}}}}
+{"model_name": "SGDRegressorTrainer_0", "package": {"All_Weights": {"Bias_Weight": 38.856412561925055, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 22.258830113477515}}}}, "core": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "uncore": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "dram": {"All_Weights": {"Bias_Weight": 9.080889901856153, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 3.0358946796490924}}}}}

pkg/config/config.go

@@ -25,6 +25,7 @@ import (
 	"strconv"
 	"strings"
 
+	"github.com/sustainable-computing-io/kepler/pkg/model/types"
 	"golang.org/x/sys/unix"
 	"k8s.io/klog/v2"
 )
@@ -129,7 +130,7 @@ var (
 	FixedTrainerNameKey      = "TRAINER"
 	FixedNodeTypeKey         = "NODE_TYPE"
 	ModelFiltersKey          = "FILTERS"
-	DefaultTrainerName       = "SGDRegressorTrainer"
+	DefaultTrainerName       = types.LinearRegressionTrainer
 	////////////////////////////////////
 
 	// KubeConfig is used to start k8s client with the pod running outside the cluster

pkg/model/estimator/local/regressor/exponential_test.go

@@ -25,7 +25,7 @@ import (
 
 var (
 	exponentialCurveFits          = []float64{1, 1, 1}
-	dummyExponentialWeightHandler = genHandlerFunc(exponentialCurveFits)
+	dummyExponentialWeightHandler = genHandlerFunc(exponentialCurveFits, types.ExponentialTrainer)
 )
 
 var _ = Describe("Test Exponential Predictor Unit", func() {

pkg/model/estimator/local/regressor/logarithm_test.go

@@ -25,7 +25,7 @@ import (
 
 var (
 	logarithmicCurveFits          = []float64{1, 1, 1}
-	dummyLogarithmicWeightHandler = genHandlerFunc(logarithmicCurveFits)
+	dummyLogarithmicWeightHandler = genHandlerFunc(logarithmicCurveFits, types.LogarithmicTrainer)
 )
 
 var _ = Describe("Test Logarithmic Predictor Unit", func() {

pkg/model/estimator/local/regressor/logistic_test.go

@@ -25,7 +25,7 @@ import (
 
 var (
 	logisticCurveFits          = []float64{1, 1, 1, 1}
-	dummyLogisticWeightHandler = genHandlerFunc(logisticCurveFits)
+	dummyLogisticWeightHandler = genHandlerFunc(logisticCurveFits, types.LogisticTrainer)
 )
 
 var _ = Describe("Test Logistic Predictor Unit", func() {

pkg/model/estimator/local/regressor/model_weights.go

@@ -19,6 +19,9 @@ package regressor
 import (
 	"errors"
 	"fmt"
+	"strings"
+
+	"github.com/sustainable-computing-io/kepler/pkg/model/types"
 
 	"github.com/sustainable-computing-io/kepler/pkg/config"
 )
@@ -113,3 +116,24 @@ func (w ComponentModelWeights) String() string {
 	}
 	return fmt.Sprintf("%s (package: %v (core: %v, uncore: %v), dram: %v)", w.ModelName, w.Package, w.Core, w.Uncore, w.DRAM)
 }
+
+func (w ComponentModelWeights) Trainer() string {
+	if w.ModelName == "" {
+		return ""
+	}
+	modelNameSplits := strings.Split(w.ModelName, "_")
+	splitTrainer := strings.Join(modelNameSplits[0:len(modelNameSplits)-1], "_")
+	if isSupportedTrainer(splitTrainer) {
+		return splitTrainer
+	}
+	return ""
+}
+
+func isSupportedTrainer(trainer string) bool {
+	for _, supportedTrainer := range types.WeightSupportedTrainers {
+		if trainer == supportedTrainer {
+			return true
+		}
+	}
+	return false
+}

pkg/model/estimator/local/regressor/regressor.go

@@ -186,7 +186,8 @@ func (r *Regressor) getWeightFromServer() (*ComponentModelWeights, error) {
 		return nil, fmt.Errorf("model unmarshal error: %v (%s)", err, string(body))
 	}
 	if weightResponse.ModelName != "" {
-		klog.V(3).Infof("Using weights trained by %s", weightResponse.ModelName)
+		r.TrainerName = weightResponse.Trainer()
+		klog.V(3).Infof("Using weights from model %s trained by %s for %s", weightResponse.ModelName, r.TrainerName, r.EnergySource)
 	}
 	r.updateCoreRatio(weightResponse.ModelMachineSpec)
 	return &weightResponse, nil
@@ -195,6 +196,7 @@ func (r *Regressor) getWeightFromServer() (*ComponentModelWeights, error) {
 // loadWeightFromURLorLocal get weight from either local or URL
 // if string start with '/', we take it as local file
 func (r *Regressor) loadWeightFromURLorLocal() (*ComponentModelWeights, error) {
+	var modelName string // to be set by ModelWeightsURL
 	var body []byte
 	var err error
 
@@ -204,12 +206,21 @@ func (r *Regressor) loadWeightFromURLorLocal() (*ComponentModelWeights, error) {
 		if err != nil {
 			return nil, err
 		}
+	} else {
+		modelName = utils.GetModelNameFromURL(r.ModelWeightsURL)
 	}
 	var content ComponentModelWeights
 	err = json.Unmarshal(body, &content)
 	if err != nil {
 		return nil, fmt.Errorf("model unmarshal error: %v (%s)", err, string(body))
 	}
+	if content.ModelName == "" {
+		// Expect for the case loadWeightFromURL
+		// ModelWeightsFilepath should contain model_name field
+		content.ModelName = modelName
+	}
+	r.TrainerName = content.Trainer()
+	klog.V(3).Infof("Using weights from model %s trained by %s for %s", content.ModelName, r.TrainerName, r.EnergySource)
 	r.updateCoreRatio(content.ModelMachineSpec)
 	return &content, nil
 }
@@ -250,13 +261,13 @@ func (r *Regressor) loadWeightFromURL() ([]byte, error) {
 // Create Predictor based on trainer name
 func (r *Regressor) createPredictor(weight ModelWeights) (predictor Predictor, err error) {
 	switch r.TrainerName {
-	case "SGDRegressorTrainer":
+	case types.LinearRegressionTrainer:
 		predictor, err = NewLinearPredictor(weight)
-	case "LogarithmicRegressionTrainer":
+	case types.LogarithmicTrainer:
 		predictor, err = NewLogarithmicPredictor(weight)
-	case "LogisticRegressionTrainer":
+	case types.LogisticTrainer:
 		predictor, err = NewLogisticPredictor(weight)
-	case "ExponentialRegressionTrainer":
+	case types.ExponentialTrainer:
 		predictor, err = NewExponentialPredictor(weight)
 	default:
 		predictor, err = NewLinearPredictor(weight)

pkg/model/estimator/local/regressor/regressor_test.go

@@ -65,16 +65,15 @@ var (
 	SampleDramNumbericalVars = map[string]NormalizedNumericalFeature{
 		"cache_miss": {Weight: 1.0, Scale: 2},
 	}
-	DummyWeightHandler                      = http.HandlerFunc(genHandlerFunc([]float64{}))
-	DummyModelName                          = "dummy"
+	DummyWeightHandler                      = http.HandlerFunc(genHandlerFunc([]float64{}, types.LinearRegressionTrainer))
 	ModelCores                              = config.GenerateSpec().Cores
 	ExpectedAbsPowerFromDummyWeightHandler  = 2500
 	ExpectedIdlePowerFromDummyWeightHandler = 2000
 )
 
-func GenPlatformModelWeights(curveFitWeights []float64) ComponentModelWeights {
+func GenPlatformModelWeights(curveFitWeights []float64, trainerName string) ComponentModelWeights {
 	return ComponentModelWeights{
-		ModelName: DummyModelName,
+		ModelName: trainerName + "_0",
 		ModelMachineSpec: &config.MachineSpec{
 			Cores: ModelCores,
 		},
@@ -100,7 +99,7 @@ func genWeights(numericalVars map[string]NormalizedNumericalFeature, curveFitWei
 	}
 }
 
-func genHandlerFunc(curvefit []float64) (handlerFunc func(w http.ResponseWriter, r *http.Request)) {
+func genHandlerFunc(curvefit []float64, trainerName string) (handlerFunc func(w http.ResponseWriter, r *http.Request)) {
 	return func(w http.ResponseWriter, r *http.Request) {
 		reqBody, err := io.ReadAll(r.Body)
 		if err != nil {
@@ -114,7 +113,7 @@ func genHandlerFunc(curvefit []float64) (handlerFunc func(w http.ResponseWriter,
 		if req.EnergySource == types.ComponentEnergySource {
 			err = json.NewEncoder(w).Encode(GenComponentModelWeights(curvefit))
 		} else {
-			err = json.NewEncoder(w).Encode(GenPlatformModelWeights(curvefit))
+			err = json.NewEncoder(w).Encode(GenPlatformModelWeights(curvefit, trainerName))
 		}
 		if err != nil {
 			panic(err)
@@ -177,7 +176,7 @@ var _ = Describe("Test Regressor Weight Unit (default trainer)", func() {
 		})
 
 		It("Get Node Components Power By Default Regression Estimator with ModelServerEndpoint", func() {
-			compPowers := GetNodeComponentsPowerFromDummyServer(genHandlerFunc([]float64{}), "")
+			compPowers := GetNodeComponentsPowerFromDummyServer(genHandlerFunc([]float64{}, types.LinearRegressionTrainer), "")
 			// TODO: verify if the power makes sense
 			Expect(compPowers[0].Core).Should(BeEquivalentTo(3000))
 		})
@@ -317,4 +316,20 @@ var _ = Describe("Test Regressor Weight Unit (default trainer)", func() {
 			Entry("invalid model core", 16, 0, 1.0),
 		)
 	})
+
+	DescribeTable("Test ComponentModelWeights.Trainer", func(modelName, expectedTrainer string) {
+		w := ComponentModelWeights{
+			ModelName: modelName,
+		}
+		Expect(w.Trainer()).To(Equal(expectedTrainer))
+	},
+		Entry("empty model name", "", ""),
+		Entry("invalid model name", "some", ""),
+		Entry("invalid model name with _", "some_invalid", ""),
+		Entry("valid SGDRegressorTrainer", "SGDRegressorTrainer_0", "SGDRegressorTrainer"),
+		Entry("valid LogarithmicRegressionTrainer", "LogarithmicRegressionTrainer_0", "LogarithmicRegressionTrainer"),
+		Entry("valid LogisticRegressionTrainer", "LogisticRegressionTrainer_0", "LogisticRegressionTrainer"),
+		Entry("valid ExponentialRegressionTrainer", "ExponentialRegressionTrainer_0", "ExponentialRegressionTrainer"),
+		Entry("invalid GradientBoostingRegressorTrainer", "GradientBoostingRegressorTrainer_0", ""),
+	)
 })

pkg/model/model_test.go

@@ -67,6 +67,15 @@ var _ = Describe("Test Model Unit", func() {
 			Entry("IdlePower with invalid coreRatio = -1", true, -1.0, 1.0),
 			Entry("IdlePower with invalid coreRatio > 1", true, 1.2, 1.0),
 		)
+
+		DescribeTable("Test GetModelNameFromURL()", func(url, expectedModelName string) {
+			Expect(utils.GetModelNameFromURL(url)).To(Equal(expectedModelName))
+		},
+			Entry("empty", "", ""),
+			Entry("some model with multiple subfolders", "http://some/path/to/some_model.json", "some_model"),
+			Entry("some model with direct path", "http://some_model.json", "some_model"),
+			Entry("some model without file extension", "http://some_model", "some_model"),
+		)
 	})
 
 })

pkg/model/types/types.go

@@ -46,14 +46,23 @@
 	ExponentialTrainer      = "ExponentialRegressionTrainer"
 )
 
+var (
+	WeightSupportedTrainers = []string{
+		LinearRegressionTrainer,
+		LogarithmicTrainer,
+		LogisticTrainer,
+		ExponentialTrainer,
+	}
+)
+
 func getModelOutputTypeConverter() []string {
 	return []string{
 		"AbsPower", "DynPower",
 	}
 }
 
 func getModelTypeConverter() []string {
 	return []string{
 		"Ratio", "Regressor", "EstimatorSidecar",
 	}
 }

pkg/model/utils/utils.go

@@ -18,6 +18,7 @@ package utils
 
 import (
 	"math"
+	"strings"
 
 	"github.com/sustainable-computing-io/kepler/pkg/sensors/components/source"
 )
@@ -69,3 +70,12 @@ func GetCoreRatio(isIdlePower bool, inCoreRatio float64) float64 {
 	}
 	return coreRatio
 }
+
+func GetModelNameFromURL(url string) string {
+	urlSplits := strings.Split(url, "/")
+	if len(urlSplits) > 0 {
+		lastItem := urlSplits[len(urlSplits)-1]
+		return strings.Split(lastItem, ".")[0]
+	}
+	return ""
+}