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Simple, powerful API
From training to production in minutes
1import michelangelo.uniflow.core as uniflow2from michelangelo.uniflow.plugins.ray import RayTask3
[email protected](config=RayTask(head_cpu=2, worker_instances=2))5def train(train_data, validation_data, params: dict):6 """Distributed training with Ray."""7 trainer = XGBoostTrainer(8 params=params,9 datasets={"train": train_data, "validation": validation_data},10 )11 return trainer.fit()12
[email protected]()14def train_workflow(dataset: str):15 """End-to-end ML training pipeline."""16 train_data, val_data = load_and_split(dataset)17 result = train(train_data, val_data, params={"max_depth": 5})18 return result19
20if __name__ == "__main__":21 ctx = uniflow.create_context()22 ctx.run(train_workflow, dataset="s3://data/training.parquet")