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Make the benchmark runnable on CPU-only instance; speed up CPU RF #256

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merged 2 commits into from
Aug 2, 2023
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Make the benchmark runnable on CPU-only instance; speed up CPU RF #256

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3583f48
Make the benchmark runnable on CPU-only instance; speed up CPU RF
hcho3 Jul 31, 2023
7dbd95d
Fix formatting
hcho3 Aug 2, 2023
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20 changes: 14 additions & 6 deletions source/examples/xgboost-rf-gpu-cpu-benchmark/hpo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,12 +4,8 @@
import time

import dask
import dask_cudf
import optuna
import xgboost as xgb
from cuml.dask.common.utils import persist_across_workers
from cuml.dask.ensemble import RandomForestClassifier as RF_gpu
from cuml.metrics import accuracy_score as accuracy_score_gpu
from dask.distributed import Client, LocalCluster, wait
from dask_cuda import LocalCUDACluster
from dask_ml.model_selection import train_test_split
Expand Down Expand Up @@ -40,6 +36,8 @@

def ingest_data(mode):
if mode == "gpu":
import dask_cudf

dataset = dask_cudf.read_parquet(
glob.glob("./data/*.parquet"),
columns=feature_columns,
Expand All @@ -61,6 +59,8 @@ def preprocess_data(dataset, *, client, i_fold, mode):
X_test, y_test = X_test.astype("float32"), y_test.astype("int32")

if mode == "gpu":
from cuml.dask.common.utils import persist_across_workers

X_train, y_train, X_test, y_test = persist_across_workers(
client, [X_train, y_train, X_test, y_test], workers=client.has_what().keys()
)
Expand Down Expand Up @@ -94,6 +94,8 @@ def train_xgboost(trial, *, dataset, client, mode):
)

if mode == "gpu":
from cuml.metrics import accuracy_score as accuracy_score_gpu

params["tree_method"] = "gpu_hist"
dtrain = xgb.dask.DaskDeviceQuantileDMatrix(client, X_train, y_train)
dtest = xgb.dask.DaskDeviceQuantileDMatrix(client, X_test)
Expand Down Expand Up @@ -125,7 +127,6 @@ def train_randomforest(trial, *, dataset, client, mode):
"n_estimators": trial.suggest_int("n_estimators", 100, 500, step=10),
"criterion": trial.suggest_categorical("criterion", ["gini", "entropy"]),
"min_samples_split": trial.suggest_int("min_samples_split", 2, 1000, log=True),
"n_bins": 256,
}

cv_fold_scores = []
Expand All @@ -135,14 +136,21 @@ def train_randomforest(trial, *, dataset, client, mode):
)

if mode == "gpu":
from cuml.dask.ensemble import RandomForestClassifier as RF_gpu
from cuml.metrics import accuracy_score as accuracy_score_gpu

params["n_bins"] = 256
trained_model = RF_gpu(client=client, **params)
accuracy_score_func = accuracy_score_gpu
else:
params["n_jobs"] = -1
trained_model = RF_cpu(**params)
accuracy_score_func = accuracy_score_cpu

trained_model.fit(X_train, y_train)
pred = trained_model.predict(X_test).compute()
pred = trained_model.predict(X_test)
if mode == "gpu":
pred = pred.compute()
y_test = y_test.compute()
score = accuracy_score_func(y_test, pred)
cv_fold_scores.append(score)
Expand Down
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