.. _tune-reporter-doc: Tune Console Output (Reporters) =============================== By default, Tune reports experiment progress periodically to the command-line as follows. .. code-block:: bash == Status == Memory usage on this node: 11.4/16.0 GiB Using FIFO scheduling algorithm. Resources requested: 4/12 CPUs, 0/0 GPUs, 0.0/3.17 GiB heap, 0.0/1.07 GiB objects Result logdir: /Users/foo/ray_results/myexp Number of trials: 4 (4 RUNNING) +----------------------+----------+---------------------+-----------+--------+--------+--------+--------+------------------+-------+ | Trial name | status | loc | param1 | param2 | param3 | acc | loss | total time (s) | iter | |----------------------+----------+---------------------+-----------+--------+--------+--------+--------+------------------+-------| | MyTrainable_a826033a | RUNNING | 10.234.98.164:31115 | 0.303706 | 0.0761 | 0.4328 | 0.1289 | 1.8572 | 7.54952 | 15 | | MyTrainable_a8263fc6 | RUNNING | 10.234.98.164:31117 | 0.929276 | 0.158 | 0.3417 | 0.4865 | 1.6307 | 7.0501 | 14 | | MyTrainable_a8267914 | RUNNING | 10.234.98.164:31111 | 0.068426 | 0.0319 | 0.1147 | 0.9585 | 1.9603 | 7.0477 | 14 | | MyTrainable_a826b7bc | RUNNING | 10.234.98.164:31112 | 0.729127 | 0.0748 | 0.1784 | 0.1797 | 1.7161 | 7.05715 | 14 | +----------------------+----------+---------------------+-----------+--------+--------+--------+--------+------------------+-------+ Note that columns will be hidden if they are completely empty. The output can be configured in various ways by instantiating a ``CLIReporter`` instance (or ``JupyterNotebookReporter`` if you're using jupyter notebook). Here's an example: .. TODO: test these snippets .. code-block:: python from ray.train import RunConfig from ray.tune import CLIReporter # Limit the number of rows. reporter = CLIReporter(max_progress_rows=10) # Add a custom metric column, in addition to the default metrics. # Note that this must be a metric that is returned in your training results. reporter.add_metric_column("custom_metric") tuner = tune.Tuner(my_trainable, run_config=RunConfig(progress_reporter=reporter)) results = tuner.fit() Extending ``CLIReporter`` lets you control reporting frequency. For example: .. code-block:: python from ray.tune.experiment.trial import Trial class ExperimentTerminationReporter(CLIReporter): def should_report(self, trials, done=False): """Reports only on experiment termination.""" return done tuner = tune.Tuner(my_trainable, run_config=RunConfig(progress_reporter=ExperimentTerminationReporter())) results = tuner.fit() class TrialTerminationReporter(CLIReporter): def __init__(self): super(TrialTerminationReporter, self).__init__() self.num_terminated = 0 def should_report(self, trials, done=False): """Reports only on trial termination events.""" old_num_terminated = self.num_terminated self.num_terminated = len([t for t in trials if t.status == Trial.TERMINATED]) return self.num_terminated > old_num_terminated tuner = tune.Tuner(my_trainable, run_config=RunConfig(progress_reporter=TrialTerminationReporter())) results = tuner.fit() The default reporting style can also be overridden more broadly by extending the ``ProgressReporter`` interface directly. Note that you can print to any output stream, file etc. .. code-block:: python from ray.tune import ProgressReporter class CustomReporter(ProgressReporter): def should_report(self, trials, done=False): return True def report(self, trials, *sys_info): print(*sys_info) print("\n".join([str(trial) for trial in trials])) tuner = tune.Tuner(my_trainable, run_config=RunConfig(progress_reporter=CustomReporter())) results = tuner.fit() .. currentmodule:: ray.tune Reporter Interface (tune.ProgressReporter) ------------------------------------------ .. autosummary:: :nosignatures: :toctree: doc/ ProgressReporter .. autosummary:: :nosignatures: :toctree: doc/ ProgressReporter.report ProgressReporter.should_report Tune Built-in Reporters ----------------------- .. autosummary:: :nosignatures: :toctree: doc/ CLIReporter JupyterNotebookReporter