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
80 lines
2.2 KiB
Python
Executable file
80 lines
2.2 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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import tensorflow as tf
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--epochs", type=int, default=10)
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parser.add_argument("--batch_size", type=int, default=32)
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parser.add_argument("--learning_rate", type=float, default=0.001)
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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(f"Training TensorFlow model for {args.epochs} epochs...")
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# Generate synthetic data
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np.random.seed(42)
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tf.random.set_seed(42)
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X = np.random.randn(1000, 20)
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y = np.random.randint(0, 2, (1000,))
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# Create TensorFlow dataset
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dataset = tf.data.Dataset.from_tensor_slices((X, y))
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dataset = dataset.shuffle(buffer_size=1000).batch(args.batch_size)
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# Build model
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model = tf.keras.Sequential(
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[
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tf.keras.layers.Dense(64, activation="relu", input_shape=(20,)),
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tf.keras.layers.Dense(32, activation="relu"),
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tf.keras.layers.Dense(2, activation="softmax"),
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]
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)
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model.compile(
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optimizer=tf.keras.optimizers.Adam(learning_rate=args.learning_rate),
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loss="sparse_categorical_crossentropy",
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metrics=["accuracy"],
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)
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# Training
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history = model.fit(dataset, epochs=args.epochs, verbose=1)
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final_accuracy = history.history["accuracy"][-1]
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logger.info(f"Training completed. Final accuracy: {final_accuracy:.4f}")
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# Save results
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results = {
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"model_type": "TensorFlow",
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"epochs": args.epochs,
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"batch_size": args.batch_size,
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"learning_rate": args.learning_rate,
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"final_accuracy": float(final_accuracy),
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"n_samples": len(X),
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"input_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(output_dir / "tensorflow_model")
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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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