Best Config after Hyperparameter Search with Ray Tune

I just run my first Ray Tune. I got nice terminal output an all that but now I'm wondering: Which configuration gave me the best score?

I see that there are a ton of results files but is there an easy way to get the best config?

You can use the ExperimentAnalysis object returned by tune.run() to get the best config, like this:

analysis = tune.run(trainable, search_alg=algo, stop={"training_iteration": 20})

best_trial = analysis.best_trial  # Get best trial
best_config = analysis.best_config  # Get best trial's hyperparameters
best_logdir = analysis.best_logdir  # Get best trial's logdir
best_checkpoint = analysis.best_checkpoint  # Get best trial's best checkpoint
best_result = analysis.best_result  # Get best trial's last results
best_result_df = analysis.best_result_df  # Get best result as pandas dataframe

See the documentation: https://docs.ray.io/en/latest/tune/key-concepts.html#analysis