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Add daal inference benchmarks and configs
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@@ -17,4 +17,5 @@ data | |
| results*.json | ||
| *.xlsx | ||
| vtune* | ||
| xgb_bench_log.txt | ||
| xgb_bench_log.txt | ||
| xgb_daal_bench_log.txt | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,33 @@ | ||
| { | ||
| "common": { | ||
| "lib": "daal_xgboost", | ||
| "data-format": "pandas", | ||
| "data-order": "F", | ||
| "dtype": "float32", | ||
| "algorithm": "daal_xgb", | ||
| "tree-method": "hist", | ||
| "count-dmatrix": "" | ||
| }, | ||
| "cases": [ | ||
| { | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "abalone", | ||
| "training": { | ||
| "x": "data/abalone_x_train.npy", | ||
| "y": "data/abalone_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/abalone_x_test.npy", | ||
| "y": "data/abalone_y_test.npy" | ||
| } | ||
| } | ||
| ], | ||
| "learning-rate": 0.03, | ||
| "max-depth": 6, | ||
| "n-estimators": 1000, | ||
| "objective": "reg:squarederror" | ||
| } | ||
| ] | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,141 @@ | ||
| { | ||
| "common": { | ||
| "lib": "xgboost", | ||
| "data-format": "pandas", | ||
| "data-order": "F", | ||
| "dtype": "float32", | ||
| "algorithm": "gbt", | ||
| "tree-method": "hist", | ||
| "count-dmatrix": "", | ||
| "max-depth": 8, | ||
| "learning-rate": 0.1, | ||
| "reg-lambda": 1, | ||
| "max-leaves": 256 | ||
| }, | ||
| "cases": [ | ||
| { | ||
| "objective": "binary:logistic", | ||
| "scale-pos-weight": 2.1067817411664587, | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "airline", | ||
| "training": { | ||
| "x": "data/airline_x_train.npy", | ||
| "y": "data/airline_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/airline_x_test.npy", | ||
| "y": "data/airline_y_test.npy" | ||
| } | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "objective": "binary:logistic", | ||
| "scale-pos-weight": 173.63348001466812, | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "bosch", | ||
| "training": { | ||
| "x": "data/bosch_x_train.npy", | ||
| "y": "data/bosch_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/bosch_x_test.npy", | ||
| "y": "data/bosch_y_test.npy" | ||
| } | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "objective": "multi:softmax", | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "covtype", | ||
| "training": { | ||
| "x": "data/covtype_x_train.npy", | ||
| "y": "data/covtype_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/covtype_x_test.npy", | ||
| "y": "data/covtype_y_test.npy" | ||
| } | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "objective": "binary:logistic", | ||
| "scale-pos-weight": 2.0017715678375363, | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "epsilon", | ||
| "training": { | ||
| "x": "data/epsilon_x_train.npy", | ||
| "y": "data/epsilon_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/epsilon_x_test.npy", | ||
| "y": "data/epsilon_y_test.npy" | ||
| } | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "objective": "binary:logistic", | ||
| "scale-pos-weight": 578.2868020304569, | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "fraud", | ||
| "training": { | ||
| "x": "data/fraud_x_train.npy", | ||
| "y": "data/fraud_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/fraud_x_test.npy", | ||
| "y": "data/fraud_y_test.npy" | ||
| } | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "objective": "binary:logistic", | ||
| "scale-pos-weight": 1.8872389605086624, | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "higgs", | ||
| "training": { | ||
| "x": "data/higgs_x_train.npy", | ||
| "y": "data/higgs_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/higgs_x_test.npy", | ||
| "y": "data/higgs_y_test.npy" | ||
| } | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "objective": "reg:squarederror", | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "year_prediction_msd", | ||
| "training": { | ||
| "x": "data/year_prediction_msd_x_train.npy", | ||
| "y": "data/year_prediction_msd_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/year_prediction_msd_x_test.npy", | ||
| "y": "data/year_prediction_msd_y_test.npy" | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ] | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,35 @@ | ||
| { | ||
| "common": { | ||
| "lib": "daal_xgboost", | ||
| "data-format": "pandas", | ||
| "data-order": "F", | ||
| "dtype": "float32", | ||
| "algorithm": "daal_xgb", | ||
| "tree-method": "hist", | ||
| "count-dmatrix": "", | ||
| "max-depth": 8, | ||
| "learning-rate": 0.1, | ||
| "reg-lambda": 1, | ||
| "max-leaves": 256 | ||
| }, | ||
| "cases": [ | ||
| { | ||
| "objective": "binary:logistic", | ||
| "scale-pos-weight": 2.1067817411664587, | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "airline", | ||
| "training": { | ||
| "x": "data/airline_x_train.npy", | ||
| "y": "data/airline_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/airline_x_test.npy", | ||
| "y": "data/airline_y_test.npy" | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ] | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,40 @@ | ||
| { | ||
| "common": { | ||
| "lib": "daal_xgboost", | ||
| "data-format": "pandas", | ||
| "data-order": "F", | ||
| "dtype": "float32", | ||
| "algorithm": "daal_xgb", | ||
| "tree-method": "hist", | ||
| "count-dmatrix": "" | ||
| }, | ||
| "cases": [ | ||
| { | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "airline-ohe", | ||
| "training": { | ||
| "x": "data/airline-ohe_x_train.npy", | ||
| "y": "data/airline-ohe_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/airline-ohe_x_test.npy", | ||
| "y": "data/airline-ohe_y_test.npy" | ||
| } | ||
| } | ||
| ], | ||
| "reg-alpha": 0.9, | ||
| "max-bin": 256, | ||
| "scale-pos-weight": 2, | ||
| "learning-rate": 0.1, | ||
| "subsample": 1, | ||
| "reg-lambda": 1, | ||
| "min-child-weight": 0, | ||
| "max-depth": 8, | ||
| "max-leaves": 256, | ||
| "n-estimators": 1000, | ||
| "objective": "binary:logistic" | ||
| } | ||
| ] | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,35 @@ | ||
| { | ||
| "common": { | ||
| "lib": "daal_xgboost", | ||
| "data-format": "pandas", | ||
| "data-order": "F", | ||
| "dtype": "float32", | ||
| "algorithm": "daal_xgb", | ||
| "tree-method": "hist", | ||
| "count-dmatrix": "", | ||
| "max-depth": 8, | ||
| "learning-rate": 0.1, | ||
| "reg-lambda": 1, | ||
| "max-leaves": 256 | ||
| }, | ||
| "cases": [ | ||
| { | ||
| "objective": "binary:logistic", | ||
| "scale-pos-weight": 173.63348001466812, | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "bosch", | ||
| "training": { | ||
| "x": "data/bosch_x_train.npy", | ||
| "y": "data/bosch_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/bosch_x_test.npy", | ||
| "y": "data/bosch_y_test.npy" | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ] | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,34 @@ | ||
| { | ||
| "common": { | ||
| "lib": "daal_xgboost", | ||
| "data-format": "pandas", | ||
| "data-order": "F", | ||
| "dtype": "float32", | ||
| "algorithm": "daal_xgb", | ||
| "tree-method": "hist", | ||
| "count-dmatrix": "", | ||
| "max-depth": 8, | ||
| "learning-rate": 0.1, | ||
| "reg-lambda": 1, | ||
| "max-leaves": 256 | ||
| }, | ||
| "cases": [ | ||
| { | ||
| "objective": "multi:softmax", | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "covtype", | ||
| "training": { | ||
| "x": "data/covtype_x_train.npy", | ||
| "y": "data/covtype_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/covtype_x_test.npy", | ||
| "y": "data/covtype_y_test.npy" | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ] | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,35 @@ | ||
| { | ||
| "common": { | ||
| "lib": "daal_xgboost", | ||
| "data-format": "pandas", | ||
| "data-order": "F", | ||
| "dtype": "float32", | ||
| "algorithm": "daal_xgb", | ||
| "tree-method": "hist", | ||
| "count-dmatrix": "", | ||
| "max-depth": 8, | ||
| "learning-rate": 0.1, | ||
| "reg-lambda": 1, | ||
| "max-leaves": 256 | ||
| }, | ||
| "cases": [ | ||
| { | ||
| "objective": "binary:logistic", | ||
| "scale-pos-weight": 2.0017715678375363, | ||
| "dataset": [ | ||
| { | ||
| "source": "npy", | ||
| "name": "epsilon", | ||
| "training": { | ||
| "x": "data/epsilon_x_train.npy", | ||
| "y": "data/epsilon_y_train.npy" | ||
| }, | ||
| "testing": { | ||
| "x": "data/epsilon_x_test.npy", | ||
| "y": "data/epsilon_y_test.npy" | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ] | ||
| } |
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