How can I retrive the model.pkl in the experiment in Databricks

user_9553352 0
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I want to retrieve the pickle off my trained model, which I know is in the run file inside my experiments in Databricks.

It seems that the mlflow.pyfunc.load_model can only do the predict method.

There is an option to directly access the pickle?

I also tried to use the path in the run using the pickle.load(path) (example of path: dbfs:/databricks/mlflow-tracking/20526156406/92f3ec23bf614c9d934dd0195/artifacts/model/model.pkl).

mlflow datastore databricks azure python
2 Answers

Use the frmwk's native load_model() method (e.g. sklearn.load_model()) or download_artifacts()

user_139801770

I recently found the solution which can be done by the following two approaches:

  1. Use the customized predict function at the moment of saving the model (check databricks documentation for more details).

example give by Databricks

class AddN(mlflow.pyfunc.PythonModel):

    def __init__(self, n):
        self.n = n

    def predict(self, context, model_input):
        return model_input.apply(lambda column: column + self.n)
# Construct and save the model
model_path = "add_n_model"
add5_model = AddN(n=5)
mlflow.pyfunc.save_model(path=model_path, python_model=add5_model)

# Load the model in `python_function` format
loaded_model = mlflow.pyfunc.load_model(model_path)
  1. Load the model artefacts as we are downloading the artefact:
from mlflow.tracking import MlflowClient

client = MlflowClient()

tmp_path = client.download_artifacts(run_id="0c7946c81fb64952bc8ccb3c7c66bca3", path='model/model.pkl')

f = open(tmp_path,'rb')

model = pickle.load(f)

f.close()

 

client.list_artifacts(run_id="0c7946c81fb64952bc8ccb3c7c66bca3", path="")

client.list_artifacts(run_id="0c7946c81fb64952bc8ccb3c7c66bca3", path="model")
user_95533520
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