Integrate ZenML with Docker to execute your ML pipelines in isolated environments locally. This integration simplifies debugging and ensures consistent execution across different systems.
from zenml import step, pipeline
from zenml.orchestrators.local_docker.local_docker_orchestrator import (
LocalDockerOrchestratorSettings,
)
@step
def preprocess_data():
# Preprocessing logic here
pass
@step
def train_model():
# Model training logic here
pass
settings = {
"orchestrator.local_docker": LocalDockerOrchestratorSettings(
run_args={"cpu_count": 2}
)
}
@pipeline(settings=settings)
def ml_pipeline():
data = preprocess_data()
train_model(data)
if __name__ == "__main__":
ml_pipeline()
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