d_data_loader_step()

# Load the model to finetune# If no version is specified, the latest version of "my_model" is used

model = client.get_artifact_version(

name_id_or_prefix="my_model", version=model_version

# Finetune the model

# This automatically creates a new version of "my_model"

model_finetuner_step(model=model, dataset=dataset)

def main():

# Save an untrained model as first version of "my_model"

untrained_model = SVC(gamma=0.001)

save_artifact(

untrained_model, name="my_model", version="1", tags=["SVC", "untrained"]

# Create a first version of "my_dataset" and train the model on it

model_finetuning_pipeline()

# Finetune the latest model on an older version of the dataset

model_finetuning_pipeline(dataset_version="1")

# Run inference with the latest model on an older version of the dataset

latest_trained_model = load_artifact("my_model")

old_dataset = load_artifact("my_dataset", version="1")

latest_trained_model.predict(old_dataset[0])

if __name__ == "__main__":

main()

This would create the following pipeline run DAGs:

Run 1:

Run 2:

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Last updated 15 days ago