Module: meridian.mlflow.autolog

  import 
  
 mlflow 
 from 
  
 meridian.data 
  
 import 
 load 
 from 
  
 meridian.mlflow 
  
 import 
 autolog 
 from 
  
 meridian.model 
  
 import 
 model 
 # Enable autologging (call this once per session) 
 autolog 
 . 
 autolog 
 ( 
 log_metrics 
 = 
 True 
 ) 
 # Start an MLflow run (optionally name it for better grouping) 
 with 
 mlflow 
 . 
 start_run 
 ( 
 run_name 
 = 
 "my_run" 
 ): 
 # Load data 
 data 
 = 
 load 
 . 
 CsvDataLoader 
 ( 
 ... 
 ) 
 . 
 load 
 () 
 # Initialize Meridian model 
 mmm 
 = 
 model 
 . 
 Meridian 
 ( 
 input_data 
 = 
 data 
 ) 
 # Run Meridian sampling processes 
 mmm 
 . 
 sample_prior 
 ( 
 n_draws 
 = 
 100 
 , 
 seed 
 = 
 123 
 ) 
 mmm 
 . 
 sample_posterior 
 ( 
 n_chains 
 = 
 7 
 , 
 n_adapt 
 = 
 500 
 , 
 n_burnin 
 = 
 500 
 , 
 n_keep 
 = 
 1000 
 , 
 seed 
 = 
 1 
 ) 
 # After the run completes, you can retrieve run results using the MLflow client. 
 client 
 = 
 mlflow 
 . 
 tracking 
 . 
 MlflowClient 
 () 
 # Get the experiment ID for the run you just launched 
 experiment_id 
 = 
 "0" 
 # Search for runs matching the run name 
 runs 
 = 
 client 
 . 
 search_runs 
 ( 
 experiment_id 
 , 
 max_results 
 = 
 1000 
 , 
 filter_string 
 = 
 f 
 "attributes.run_name = 'my_run'" 
 ) 
 # Print details of the run 
 if 
 runs 
 : 
 print 
 ( 
 runs 
 [ 
 0 
 ]) 
 else 
 : 
 print 
 ( 
 "No runs found." 
 )