4aecd469a1
- Add Prometheus, Grafana, and Loki monitoring stack - Include pre-configured dashboards for ML metrics and logs - Add Podman container support with security policies - Implement ML runtime environments for multiple frameworks - Add containerized ML project templates (PyTorch, TensorFlow, etc.) - Include secure runner with isolation and resource limits - Add comprehensive log aggregation and alerting
84 lines
2.3 KiB
Python
Executable file
84 lines
2.3 KiB
Python
Executable file
#!/usr/bin/env python3
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import argparse
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import json
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import logging
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from pathlib import Path
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import time
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import numpy as np
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from sklearn.datasets import make_classification
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from sklearn.metrics import accuracy_score
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from sklearn.model_selection import train_test_split
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import xgboost as xgb
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--n_estimators", type=int, default=100)
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parser.add_argument("--max_depth", type=int, default=6)
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parser.add_argument("--learning_rate", type=float, default=0.1)
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parser.add_argument("--output_dir", type=str, required=True)
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args = parser.parse_args()
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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logger.info(
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f"Training XGBoost with {args.n_estimators} estimators, depth {args.max_depth}..."
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)
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# Generate synthetic data
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X, y = make_classification(
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n_samples=1000, n_features=20, n_classes=2, random_state=42
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)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42
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)
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# Convert to DMatrix (XGBoost format)
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dtrain = xgb.DMatrix(X_train, label=y_train)
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dtest = xgb.DMatrix(X_test, label=y_test)
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# Train model
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params = {
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"max_depth": args.max_depth,
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"eta": args.learning_rate,
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"objective": "binary:logistic",
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"eval_metric": "logloss",
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"seed": 42,
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}
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model = xgb.train(params, dtrain, args.n_estimators)
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# Evaluate
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y_pred_prob = model.predict(dtest)
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y_pred = (y_pred_prob > 0.5).astype(int)
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accuracy = accuracy_score(y_test, y_pred)
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logger.info(f"Training completed. Accuracy: {accuracy:.4f}")
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# Save results
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results = {
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"model_type": "XGBoost",
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"n_estimators": args.n_estimators,
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"max_depth": args.max_depth,
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"learning_rate": args.learning_rate,
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"accuracy": accuracy,
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"n_samples": len(X),
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"n_features": X.shape[1],
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}
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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with open(output_dir / "results.json", "w") as f:
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json.dump(results, f, indent=2)
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# Save model
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model.save_model(str(output_dir / "xgboost_model.json"))
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logger.info("Results and model saved successfully!")
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if __name__ == "__main__":
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main()
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