ray/python/ray/tune/progress_reporter.py

from __future__ import print_function

import collections
import time

from ray.tune.result import (CONFIG_PREFIX, EPISODE_REWARD_MEAN, MEAN_ACCURACY,
                             MEAN_LOSS, TRAINING_ITERATION, TIME_TOTAL_S,
                             TIMESTEPS_TOTAL)
from ray.tune.utils import flatten_dict

try:
    from tabulate import tabulate
except ImportError:
    raise ImportError("ray.tune in ray > 0.7.5 requires 'tabulate'. "
                      "Please re-run 'pip install ray[tune]' or "
                      "'pip install ray[rllib]'.")


class ProgressReporter:
    """Abstract class for experiment progress reporting.

    `should_report()` is called to determine whether or not `report()` should
    be called. Tune will call these functions after trial state transitions,
    receiving training results, and so on.
    """

    def should_report(self, trials, done=False):
        """Returns whether or not progress should be reported.

        Args:
            trials (list[Trial]): Trials to report on.
            done (bool): Whether this is the last progress report attempt.
        """
        raise NotImplementedError

    def report(self, trials, *sys_info):
        """Reports progress across trials.

        Args:
            trials (list[Trial]): Trials to report on.
            sys_info: System info.
        """
        raise NotImplementedError


class TuneReporterBase(ProgressReporter):
    """Abstract base class for the default Tune reporters."""

    # Truncated representations of column names (to accommodate small screens).
    DEFAULT_COLUMNS = {
        EPISODE_REWARD_MEAN: "reward",
        MEAN_ACCURACY: "acc",
        MEAN_LOSS: "loss",
        TIME_TOTAL_S: "total time (s)",
        TIMESTEPS_TOTAL: "ts",
        TRAINING_ITERATION: "iter",
    }

    def __init__(self,
                 metric_columns=None,
                 max_progress_rows=20,
                 max_error_rows=20,
                 max_report_frequency=5):
        """Initializes a new TuneReporterBase.

        Args:
            metric_columns (dict[str, str]|list[str]): Names of metrics to
                include in progress table. If this is a dict, the keys should
                be metric names and the values should be the displayed names.
                If this is a list, the metric name is used directly.
            max_progress_rows (int): Maximum number of rows to print
                in the progress table. The progress table describes the
                progress of each trial. Defaults to 20.
            max_error_rows (int): Maximum number of rows to print in the
                error table. The error table lists the error file, if any,
                corresponding to each trial. Defaults to 20.
            max_report_frequency (int): Maximum report frequency in seconds.
                Defaults to 5s.
        """
        self._metric_columns = metric_columns or self.DEFAULT_COLUMNS
        self._max_progress_rows = max_progress_rows
        self._max_error_rows = max_error_rows

        self._max_report_freqency = max_report_frequency
        self._last_report_time = 0

    def should_report(self, trials, done=False):
        if time.time() - self._last_report_time > self._max_report_freqency:
            self._last_report_time = time.time()
            return True
        return done

    def add_metric_column(self, metric, representation=None):
        """Adds a metric to the existing columns.

        Args:
            metric (str): Metric to add. This must be a metric being returned
                in training step results.
            representation (str): Representation to use in table. Defaults to
                `metric`.
        """
        if metric in self._metric_columns:
            raise ValueError("Column {} already exists.".format(metric))

        if isinstance(self._metric_columns, collections.Mapping):
            representation = representation or metric
            self._metric_columns[metric] = representation
        else:
            if representation is not None and representation != metric:
                raise ValueError(
                    "`representation` cannot differ from `metric` "
                    "if this reporter was initialized with a list "
                    "of metric columns.")
            self._metric_columns.append(metric)

    def _progress_str(self, trials, *sys_info, fmt="psql", delim="\n"):
        """Returns full progress string.

        This string contains a progress table and error table. The progress
        table describes the progress of each trial. The error table lists
        the error file, if any, corresponding to each trial. The latter only
        exists if errors have occurred.

        Args:
            trials (list[Trial]): Trials to report on.
            fmt (str): Table format. See `tablefmt` in tabulate API.
            delim (str): Delimiter between messages.
        """
        messages = ["== Status ==", memory_debug_str(), *sys_info]
        if self._max_progress_rows > 0:
            messages.append(
                trial_progress_str(
                    trials,
                    metric_columns=self._metric_columns,
                    fmt=fmt,
                    max_rows=self._max_progress_rows))
        if self._max_error_rows > 0:
            messages.append(
                trial_errors_str(
                    trials, fmt=fmt, max_rows=self._max_error_rows))
        return delim.join(messages) + delim


class JupyterNotebookReporter(TuneReporterBase):
    """Jupyter notebook-friendly Reporter that can update display in-place."""

    def __init__(self,
                 overwrite,
                 metric_columns=None,
                 max_progress_rows=20,
                 max_error_rows=20,
                 max_report_frequency=5):
        """Initializes a new JupyterNotebookReporter.

        Args:
            overwrite (bool): Flag for overwriting the last reported progress.
            metric_columns (dict[str, str]|list[str]): Names of metrics to
                include in progress table. If this is a dict, the keys should
                be metric names and the values should be the displayed names.
                If this is a list, the metric name is used directly.
            max_progress_rows (int): Maximum number of rows to print
                in the progress table. The progress table describes the
                progress of each trial. Defaults to 20.
            max_error_rows (int): Maximum number of rows to print in the
                error table. The error table lists the error file, if any,
                corresponding to each trial. Defaults to 20.
            max_report_frequency (int): Maximum report frequency in seconds.
                Defaults to 5s.
        """
        super(JupyterNotebookReporter,
              self).__init__(metric_columns, max_progress_rows, max_error_rows,
                             max_report_frequency)
        self._overwrite = overwrite

    def report(self, trials, *sys_info):
        from IPython.display import clear_output
        from IPython.core.display import display, HTML
        if self._overwrite:
            clear_output(wait=True)
        progress_str = self._progress_str(
            trials, *sys_info, fmt="html", delim="<br>")
        display(HTML(progress_str))


class CLIReporter(TuneReporterBase):
    """Command-line reporter"""

    def __init__(self,
                 metric_columns=None,
                 max_progress_rows=20,
                 max_error_rows=20,
                 max_report_frequency=5):
        """Initializes a CLIReporter.

        Args:
            metric_columns (dict[str, str]|list[str]): Names of metrics to
                include in progress table. If this is a dict, the keys should
                be metric names and the values should be the displayed names.
                If this is a list, the metric name is used directly.
            max_progress_rows (int): Maximum number of rows to print
                in the progress table. The progress table describes the
                progress of each trial. Defaults to 20.
            max_error_rows (int): Maximum number of rows to print in the
                error table. The error table lists the error file, if any,
                corresponding to each trial. Defaults to 20.
            max_report_frequency (int): Maximum report frequency in seconds.
                Defaults to 5s.
        """
        super(CLIReporter, self).__init__(metric_columns, max_progress_rows,
                                          max_error_rows, max_report_frequency)

    def report(self, trials, *sys_info):
        print(self._progress_str(trials, *sys_info))


def memory_debug_str():
    try:
        import ray  # noqa F401
        import psutil
        total_gb = psutil.virtual_memory().total / (1024**3)
        used_gb = total_gb - psutil.virtual_memory().available / (1024**3)
        if used_gb > total_gb * 0.9:
            warn = (": ***LOW MEMORY*** less than 10% of the memory on "
                    "this node is available for use. This can cause "
                    "unexpected crashes. Consider "
                    "reducing the memory used by your application "
                    "or reducing the Ray object store size by setting "
                    "`object_store_memory` when calling `ray.init`.")
        else:
            warn = ""
        return "Memory usage on this node: {}/{} GiB{}".format(
            round(used_gb, 1), round(total_gb, 1), warn)
    except ImportError:
        return ("Unknown memory usage. Please run `pip install psutil` "
                "(or ray[debug]) to resolve)")


def trial_progress_str(trials, metric_columns, fmt="psql", max_rows=None):
    """Returns a human readable message for printing to the console.

    This contains a table where each row represents a trial, its parameters
    and the current values of its metrics.

    Args:
        trials (list[Trial]): List of trials to get progress string for.
        metric_columns (dict[str, str]|list[str]): Names of metrics to include.
            If this is a dict, the keys are metric names and the values are
            the names to use in the message. If this is a list, the metric
            name is used in the message directly.
        fmt (str): Output format (see tablefmt in tabulate API).
        max_rows (int): Maximum number of rows in the trial table. Defaults to
            unlimited.
    """
    messages = []
    delim = "<br>" if fmt == "html" else "\n"
    if len(trials) < 1:
        return delim.join(messages)

    num_trials = len(trials)
    trials_by_state = collections.defaultdict(list)
    for t in trials:
        trials_by_state[t.status].append(t)

    for local_dir in sorted({t.local_dir for t in trials}):
        messages.append("Result logdir: {}".format(local_dir))

    num_trials_strs = [
        "{} {}".format(len(trials_by_state[state]), state)
        for state in trials_by_state
    ]
    messages.append("Number of trials: {} ({})".format(
        num_trials, ", ".join(num_trials_strs)))

    max_rows = max_rows or float("inf")
    if num_trials > max_rows:
        # TODO(ujvl): suggestion for users to view more rows.
        trials_by_state_trunc = _fair_filter_trials(trials_by_state, max_rows)
        trials = []
        overflow_strs = []
        for state in trials_by_state:
            trials += trials_by_state_trunc[state]
            overflow = len(trials_by_state[state]) - len(
                trials_by_state_trunc[state])
            overflow_strs.append("{} {}".format(overflow, state))
        # Build overflow string.
        overflow = num_trials - max_rows
        overflow_str = ", ".join(overflow_strs)
        messages.append("Table truncated to {} rows. {} trials ({}) not "
                        "shown.".format(max_rows, overflow, overflow_str))

    # Pre-process trials to figure out what columns to show.
    if isinstance(metric_columns, collections.Mapping):
        keys = list(metric_columns.keys())
    else:
        keys = metric_columns
    keys = [
        k for k in keys if any(
            t.last_result.get(k) is not None for t in trials)
    ]
    # Build trial rows.
    params = list(set().union(*[t.evaluated_params for t in trials]))
    trial_table = [_get_trial_info(trial, params, keys) for trial in trials]
    # Format column headings
    if isinstance(metric_columns, collections.Mapping):
        formatted_columns = [metric_columns[k] for k in keys]
    else:
        formatted_columns = keys
    columns = ["Trial name", "status", "loc"] + params + formatted_columns
    # Tabulate.
    messages.append(
        tabulate(trial_table, headers=columns, tablefmt=fmt, showindex=False))
    return delim.join(messages)


def trial_errors_str(trials, fmt="psql", max_rows=None):
    """Returns a readable message regarding trial errors.

    Args:
        trials (list[Trial]): List of trials to get progress string for.
        fmt (str): Output format (see tablefmt in tabulate API).
        max_rows (int): Maximum number of rows in the error table. Defaults to
            unlimited.
    """
    messages = []
    failed = [t for t in trials if t.error_file]
    num_failed = len(failed)
    if num_failed > 0:
        messages.append("Number of errored trials: {}".format(num_failed))
        max_rows = max_rows or float("inf")
        if num_failed > max_rows:
            messages.append("Table truncated to {} rows ({} overflow)".format(
                max_rows, num_failed - max_rows))
        error_table = []
        for trial in failed[:max_rows]:
            row = [str(trial), trial.num_failures, trial.error_file]
            error_table.append(row)
        columns = ["Trial name", "# failures", "error file"]
        messages.append(
            tabulate(
                error_table, headers=columns, tablefmt=fmt, showindex=False))
    delim = "<br>" if fmt == "html" else "\n"
    return delim.join(messages)


def _fair_filter_trials(trials_by_state, max_trials):
    """Filters trials such that each state is represented fairly.

    The oldest trials are truncated if necessary.

    Args:
        trials_by_state (dict[str, list[Trial]]: Trials by state.
        max_trials (int): Maximum number of trials to return.
    Returns:
        Dict mapping state to List of fairly represented trials.
    """
    num_trials_by_state = collections.defaultdict(int)
    no_change = False
    # Determine number of trials to keep per state.
    while max_trials > 0 and not no_change:
        no_change = True
        for state in trials_by_state:
            if num_trials_by_state[state] < len(trials_by_state[state]):
                no_change = False
                max_trials -= 1
                num_trials_by_state[state] += 1
    # Sort by start time, descending.
    sorted_trials_by_state = {
        state: sorted(
            trials_by_state[state],
            reverse=True,
            key=lambda t: t.start_time if t.start_time else float("-inf"))
        for state in trials_by_state
    }
    # Truncate oldest trials.
    filtered_trials = {
        state: sorted_trials_by_state[state][:num_trials_by_state[state]]
        for state in trials_by_state
    }
    return filtered_trials


def _get_trial_info(trial, parameters, metrics):
    """Returns the following information about a trial:

    name | status | loc | params... | metrics...

    Args:
        trial (Trial): Trial to get information for.
        parameters (list[str]): Names of trial parameters to include.
        metrics (list[str]): Names of metrics to include.
    """
    result = flatten_dict(trial.last_result)
    trial_info = [str(trial), trial.status, str(trial.location)]
    trial_info += [result.get(CONFIG_PREFIX + param) for param in parameters]
    trial_info += [result.get(metric) for metric in metrics]
    return trial_info