mlflow.log_metric("foo", 4, step=2) mlflow.log_metric("foo", 6, step=3) # Log an artifact (output file) with open("output.txt", "w") as f: f.write("Hello world!") mlflow.log_artifact("output.txt") # COMMAND ---------- # MAGIC %md ## Switch to Azure Machine Learning backend # COMMAND ---------- workspace.get_mlflow_tracking_uri() # COMMAND ---------- mlflow.get_tracking_uri() # COMMAND ---------- # change to AML mlflow.set_tracking_uri(workspace.get_mlflow_tracking_uri()) mlflow.get_tracking_uri() # COMMAND ---------- # # revert changes back to databricks # mlflow.set_tracking_uri('databricks')
# COMMAND ---------- !pip install azureml-mlflow # COMMAND ---------- run.wait_for_completion() import mlflow # Get best model from automl run best_run, non_onnx_model = run.get_output() artifact_path = experiment_name + "_artifact" mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri()) mlflow.set_experiment(experiment_name) with mlflow.start_run() as run: # Save the model to the outputs directory for capture mlflow.sklearn.log_model(non_onnx_model, artifact_path) # Register the model to AML model registry mlflow.register_model("runs:/" + run.info.run_id + "/" + artifact_path, "satraining-nyc_taxi-20210525085738-Best") # COMMAND ---------- # MAGIC %md # MAGIC Model registry functionality is unavailable; got unsupported URI 'azureml://westeurope.experiments.azureml.net/mlflow/v1.0/subscriptions/f80606e5-788f-4dc3-a9ea-2eb9a7836082/resourceGroups/rg-synapse-training/providers/Microsoft.MachineLearningServices/workspaces/mlworkspace-training?' for model registry data storage. Supported URI schemes are: ['', 'file', 'databricks', 'http', 'https', 'postgresql', 'mysql', 'sqlite', 'mssql']. See https://www.mlflow.org/docs/latest/tracking.html#storage for how to run an MLflow server against one of the supported backend storage locations. # COMMAND ----------