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ray-xgboost.py
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import os | |
import ray | |
from xgboost_ray import RayDMatrix, RayParams, train | |
from sklearn.datasets import load_breast_cancer | |
HEAD_SERVICE_IP_ENV = "EXAMPLE_CLUSTER_RAY_HEAD_SERVICE_HOST" | |
HEAD_SERVICE_CLIENT_PORT_ENV = "EXAMPLE_CLUSTER_RAY_HEAD_SERVICE_PORT_CLIENT" | |
head_service_ip = os.environ[HEAD_SERVICE_IP_ENV] | |
client_port = os.environ[HEAD_SERVICE_CLIENT_PORT_ENV] | |
ray.util.connect(f"{head_service_ip}:{client_port}") | |
train_x, train_y = load_breast_cancer(return_X_y=True) | |
train_set = RayDMatrix(train_x, train_y) | |
evals_result = {} | |
bst = train( | |
{ | |
"objective": "binary:logistic", | |
"eval_metric": ["logloss", "error"], | |
}, | |
train_set, | |
evals_result=evals_result, | |
evals=[(train_set, "train")], | |
verbose_eval=False, | |
ray_params=RayParams( | |
num_actors=2, | |
cpus_per_actor=1)) | |
bst.save_model("model.xgb") | |
print("Final training error: {:.4f}".format( | |
evals_result["train"]["error"][-1])) |
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