from __future__ import print_function
import collections
import datetime
import numbers
import os
import sys
import textwrap
import time
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
import ray
from ray._private.dict import flatten_dict
from ray.experimental.tqdm_ray import safe_print
from ray.air.util.node import _force_on_current_node
from ray.tune.callback import Callback
from ray.tune.logger import pretty_print
from ray.tune.result import (
AUTO_RESULT_KEYS,
DEFAULT_METRIC,
DONE,
EPISODE_REWARD_MEAN,
EXPERIMENT_TAG,
MEAN_ACCURACY,
MEAN_LOSS,
NODE_IP,
PID,
TIME_TOTAL_S,
TIMESTEPS_TOTAL,
TRAINING_ITERATION,
TRIAL_ID,
)
from ray.tune.experiment.trial import DEBUG_PRINT_INTERVAL, Trial, _Location
from ray.tune.trainable import Trainable
from ray.tune.utils import unflattened_lookup
from ray.tune.utils.log import Verbosity, has_verbosity, set_verbosity
from ray.util.annotations import DeveloperAPI, PublicAPI
from ray.util.queue import Empty, Queue
from ray.widgets import Template
try:
from collections.abc import Mapping, MutableMapping
except ImportError:
from collections import Mapping, MutableMapping
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]'."
)
IS_NOTEBOOK = ray.widgets.util.in_notebook()
SKIP_RESULTS_IN_REPORT = {"config", TRIAL_ID, EXPERIMENT_TAG, DONE}
[docs]@PublicAPI
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.
"""
[docs] def setup(
self,
start_time: Optional[float] = None,
total_samples: Optional[int] = None,
metric: Optional[str] = None,
mode: Optional[str] = None,
**kwargs,
):
"""Setup progress reporter for a new Ray Tune run.
This function is used to initialize parameters that are set on runtime.
It will be called before any of the other methods.
Defaults to no-op.
Args:
start_time: Timestamp when the Ray Tune run is started.
total_samples: Number of samples the Ray Tune run will run.
metric: Metric to optimize.
mode: Must be one of [min, max]. Determines whether objective is
minimizing or maximizing the metric attribute.
**kwargs: Keyword arguments for forward-compatibility.
"""
pass
[docs] def should_report(self, trials: List[Trial], done: bool = False):
"""Returns whether or not progress should be reported.
Args:
trials: Trials to report on.
done: Whether this is the last progress report attempt.
"""
raise NotImplementedError
[docs] def report(self, trials: List[Trial], done: bool, *sys_info: Dict):
"""Reports progress across trials.
Args:
trials: Trials to report on.
done: Whether this is the last progress report attempt.
sys_info: System info.
"""
raise NotImplementedError
@DeveloperAPI
class TuneReporterBase(ProgressReporter):
"""Abstract base class for the default Tune reporters.
If metric_columns is not overridden, Tune will attempt to automatically
infer the metrics being outputted, up to 'infer_limit' number of
metrics.
Args:
metric_columns: 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.
parameter_columns: Names of parameters to
include in progress table. If this is a dict, the keys should
be parameter names and the values should be the displayed names.
If this is a list, the parameter name is used directly. If empty,
defaults to all available parameters.
max_progress_rows: 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: 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_column_length: Maximum column length (in characters). Column
headers and values longer than this will be abbreviated.
max_report_frequency: Maximum report frequency in seconds.
Defaults to 5s.
infer_limit: Maximum number of metrics to automatically infer
from tune results.
print_intermediate_tables: Print intermediate result
tables. If None (default), will be set to True for verbosity
levels above 3, otherwise False. If True, intermediate tables
will be printed with experiment progress. If False, tables
will only be printed at then end of the tuning run for verbosity
levels greater than 2.
metric: Metric used to determine best current trial.
mode: One of [min, max]. Determines whether objective is
minimizing or maximizing the metric attribute.
sort_by_metric: Sort terminated trials by metric in the
intermediate table. Defaults to False.
"""
# Truncated representations of column names (to accommodate small screens).
DEFAULT_COLUMNS = collections.OrderedDict(
{
MEAN_ACCURACY: "acc",
MEAN_LOSS: "loss",
TRAINING_ITERATION: "iter",
TIME_TOTAL_S: "total time (s)",
TIMESTEPS_TOTAL: "ts",
EPISODE_REWARD_MEAN: "reward",
}
)
VALID_SUMMARY_TYPES = {
int,
float,
np.float32,
np.float64,
np.int32,
np.int64,
type(None),
}
def __init__(
self,
*,
metric_columns: Optional[Union[List[str], Dict[str, str]]] = None,
parameter_columns: Optional[Union[List[str], Dict[str, str]]] = None,
total_samples: Optional[int] = None,
max_progress_rows: int = 20,
max_error_rows: int = 20,
max_column_length: int = 20,
max_report_frequency: int = 5,
infer_limit: int = 3,
print_intermediate_tables: Optional[bool] = None,
metric: Optional[str] = None,
mode: Optional[str] = None,
sort_by_metric: bool = False,
):
self._total_samples = total_samples
self._metrics_override = metric_columns is not None
self._inferred_metrics = {}
self._metric_columns = metric_columns or self.DEFAULT_COLUMNS.copy()
self._parameter_columns = parameter_columns or []
self._max_progress_rows = max_progress_rows
self._max_error_rows = max_error_rows
self._max_column_length = max_column_length
self._infer_limit = infer_limit
if print_intermediate_tables is None:
self._print_intermediate_tables = has_verbosity(Verbosity.V3_TRIAL_DETAILS)
else:
self._print_intermediate_tables = print_intermediate_tables
self._max_report_freqency = max_report_frequency
self._last_report_time = 0
self._start_time = time.time()
self._metric = metric
self._mode = mode
self._sort_by_metric = sort_by_metric
def setup(
self,
start_time: Optional[float] = None,
total_samples: Optional[int] = None,
metric: Optional[str] = None,
mode: Optional[str] = None,
**kwargs,
):
self.set_start_time(start_time)
self.set_total_samples(total_samples)
self.set_search_properties(metric=metric, mode=mode)
def set_search_properties(self, metric: Optional[str], mode: Optional[str]):
if (self._metric and metric) or (self._mode and mode):
raise ValueError(
"You passed a `metric` or `mode` argument to `tune.TuneConfig()`, but "
"the reporter you are using was already instantiated with their "
"own `metric` and `mode` parameters. Either remove the arguments "
"from your reporter or from your call to `tune.TuneConfig()`"
)
if metric:
self._metric = metric
if mode:
self._mode = mode
if self._metric is None and self._mode:
# If only a mode was passed, use anonymous metric
self._metric = DEFAULT_METRIC
return True
def set_total_samples(self, total_samples: int):
self._total_samples = total_samples
def set_start_time(self, timestamp: Optional[float] = None):
if timestamp is not None:
self._start_time = time.time()
else:
self._start_time = timestamp
def should_report(self, trials: List[Trial], done: bool = 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: str, representation: Optional[str] = None):
"""Adds a metric to the existing columns.
Args:
metric: Metric to add. This must be a metric being returned
in training step results.
representation: Representation to use in table. Defaults to
`metric`.
"""
self._metrics_override = True
if metric in self._metric_columns:
raise ValueError("Column {} already exists.".format(metric))
if isinstance(self._metric_columns, MutableMapping):
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 add_parameter_column(
self, parameter: str, representation: Optional[str] = None
):
"""Adds a parameter to the existing columns.
Args:
parameter: Parameter to add. This must be a parameter
specified in the configuration.
representation: Representation to use in table. Defaults to
`parameter`.
"""
if parameter in self._parameter_columns:
raise ValueError("Column {} already exists.".format(parameter))
if isinstance(self._parameter_columns, MutableMapping):
representation = representation or parameter
self._parameter_columns[parameter] = representation
else:
if representation is not None and representation != parameter:
raise ValueError(
"`representation` cannot differ from `parameter` "
"if this reporter was initialized with a list "
"of metric columns."
)
self._parameter_columns.append(parameter)
def _progress_str(
self,
trials: List[Trial],
done: bool,
*sys_info: Dict,
fmt: str = "psql",
delim: str = "\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: Trials to report on.
done: Whether this is the last progress report attempt.
fmt: Table format. See `tablefmt` in tabulate API.
delim: Delimiter between messages.
"""
if self._sort_by_metric and (self._metric is None or self._mode is None):
self._sort_by_metric = False
warnings.warn(
"Both 'metric' and 'mode' must be set to be able "
"to sort by metric. No sorting is performed."
)
if not self._metrics_override:
user_metrics = self._infer_user_metrics(trials, self._infer_limit)
self._metric_columns.update(user_metrics)
messages = [
"== Status ==",
_time_passed_str(self._start_time, time.time()),
*sys_info,
]
if done:
max_progress = None
max_error = None
else:
max_progress = self._max_progress_rows
max_error = self._max_error_rows
current_best_trial, metric = self._current_best_trial(trials)
if current_best_trial:
messages.append(
_best_trial_str(current_best_trial, metric, self._parameter_columns)
)
if has_verbosity(Verbosity.V1_EXPERIMENT):
# Will filter the table in `trial_progress_str`
messages.append(
_trial_progress_str(
trials,
metric_columns=self._metric_columns,
parameter_columns=self._parameter_columns,
total_samples=self._total_samples,
force_table=self._print_intermediate_tables,
fmt=fmt,
max_rows=max_progress,
max_column_length=self._max_column_length,
done=done,
metric=self._metric,
mode=self._mode,
sort_by_metric=self._sort_by_metric,
)
)
messages.append(_trial_errors_str(trials, fmt=fmt, max_rows=max_error))
return delim.join(messages) + delim
def _infer_user_metrics(self, trials: List[Trial], limit: int = 4):
"""Try to infer the metrics to print out."""
if len(self._inferred_metrics) >= limit:
return self._inferred_metrics
self._inferred_metrics = {}
for t in trials:
if not t.last_result:
continue
for metric, value in t.last_result.items():
if metric not in self.DEFAULT_COLUMNS:
if metric not in AUTO_RESULT_KEYS:
if type(value) in self.VALID_SUMMARY_TYPES:
self._inferred_metrics[metric] = metric
if len(self._inferred_metrics) >= limit:
return self._inferred_metrics
return self._inferred_metrics
def _current_best_trial(self, trials: List[Trial]):
if not trials:
return None, None
metric, mode = self._metric, self._mode
# If no metric has been set, see if exactly one has been reported
# and use that one. `mode` must still be set.
if not metric:
if len(self._inferred_metrics) == 1:
metric = list(self._inferred_metrics.keys())[0]
if not metric or not mode:
return None, metric
metric_op = 1.0 if mode == "max" else -1.0
best_metric = float("-inf")
best_trial = None
for t in trials:
if not t.last_result:
continue
metric_value = unflattened_lookup(metric, t.last_result, default=None)
if pd.isnull(metric_value):
continue
if not best_trial or metric_value * metric_op > best_metric:
best_metric = metric_value * metric_op
best_trial = t
return best_trial, metric
@DeveloperAPI
class RemoteReporterMixin:
"""Remote reporter abstract mixin class.
Subclasses of this class will use a Ray Queue to display output
on the driver side when running Ray Client."""
@property
def output_queue(self) -> Queue:
return getattr(self, "_output_queue", None)
@output_queue.setter
def output_queue(self, value: Queue):
self._output_queue = value
def display(self, string: str) -> None:
"""Display the progress string.
Args:
string: String to display.
"""
raise NotImplementedError
[docs]@PublicAPI
class JupyterNotebookReporter(TuneReporterBase, RemoteReporterMixin):
"""Jupyter notebook-friendly Reporter that can update display in-place.
Args:
overwrite: Flag for overwriting the cell contents before initialization.
metric_columns: 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.
parameter_columns: Names of parameters to
include in progress table. If this is a dict, the keys should
be parameter names and the values should be the displayed names.
If this is a list, the parameter name is used directly. If empty,
defaults to all available parameters.
max_progress_rows: 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: 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_column_length: Maximum column length (in characters). Column
headers and values longer than this will be abbreviated.
max_report_frequency: Maximum report frequency in seconds.
Defaults to 5s.
infer_limit: Maximum number of metrics to automatically infer
from tune results.
print_intermediate_tables: Print intermediate result
tables. If None (default), will be set to True for verbosity
levels above 3, otherwise False. If True, intermediate tables
will be printed with experiment progress. If False, tables
will only be printed at then end of the tuning run for verbosity
levels greater than 2.
metric: Metric used to determine best current trial.
mode: One of [min, max]. Determines whether objective is
minimizing or maximizing the metric attribute.
sort_by_metric: Sort terminated trials by metric in the
intermediate table. Defaults to False.
"""
def __init__(
self,
*,
overwrite: bool = True,
metric_columns: Optional[Union[List[str], Dict[str, str]]] = None,
parameter_columns: Optional[Union[List[str], Dict[str, str]]] = None,
total_samples: Optional[int] = None,
max_progress_rows: int = 20,
max_error_rows: int = 20,
max_column_length: int = 20,
max_report_frequency: int = 5,
infer_limit: int = 3,
print_intermediate_tables: Optional[bool] = None,
metric: Optional[str] = None,
mode: Optional[str] = None,
sort_by_metric: bool = False,
):
super(JupyterNotebookReporter, self).__init__(
metric_columns=metric_columns,
parameter_columns=parameter_columns,
total_samples=total_samples,
max_progress_rows=max_progress_rows,
max_error_rows=max_error_rows,
max_column_length=max_column_length,
max_report_frequency=max_report_frequency,
infer_limit=infer_limit,
print_intermediate_tables=print_intermediate_tables,
metric=metric,
mode=mode,
sort_by_metric=sort_by_metric,
)
if not IS_NOTEBOOK:
warnings.warn(
"You are using the `JupyterNotebookReporter`, but not "
"IPython/Jupyter-compatible environment was detected. "
"If this leads to unformatted output (e.g. like "
"<IPython.core.display.HTML object>), consider passing "
"a `CLIReporter` as the `progress_reporter` argument "
"to `air.RunConfig()` instead."
)
self._overwrite = overwrite
self._display_handle = None
self.display("") # initialize empty display to update later
def report(self, trials: List[Trial], done: bool, *sys_info: Dict):
progress = self._progress_html(trials, done, *sys_info)
if self.output_queue is not None:
# If an output queue is set, send string
self.output_queue.put(progress)
else:
# Else, output directly
self.display(progress)
def display(self, string: str) -> None:
from IPython.display import HTML, clear_output, display
if not self._display_handle:
if self._overwrite:
clear_output(wait=True)
self._display_handle = display(HTML(string), display_id=True)
else:
self._display_handle.update(HTML(string))
def _progress_html(self, trials: List[Trial], done: bool, *sys_info) -> str:
"""Generate an HTML-formatted progress update.
Args:
trials: List of trials for which progress should be
displayed
done: True if the trials are finished, False otherwise
*sys_info: System information to be displayed
Returns:
Progress update to be rendered in a notebook, including HTML
tables and formatted error messages. Includes
- Duration of the tune job
- Memory consumption
- Trial progress table, with information about each experiment
"""
if not self._metrics_override:
user_metrics = self._infer_user_metrics(trials, self._infer_limit)
self._metric_columns.update(user_metrics)
current_time, running_for = _get_time_str(self._start_time, time.time())
used_gb, total_gb, memory_message = _get_memory_usage()
status_table = tabulate(
[
("Current time:", current_time),
("Running for:", running_for),
("Memory:", f"{used_gb}/{total_gb} GiB"),
],
tablefmt="html",
)
trial_progress_data = _trial_progress_table(
trials=trials,
metric_columns=self._metric_columns,
parameter_columns=self._parameter_columns,
fmt="html",
max_rows=None if done else self._max_progress_rows,
metric=self._metric,
mode=self._mode,
sort_by_metric=self._sort_by_metric,
max_column_length=self._max_column_length,
)
trial_progress = trial_progress_data[0]
trial_progress_messages = trial_progress_data[1:]
trial_errors = _trial_errors_str(
trials, fmt="html", max_rows=None if done else self._max_error_rows
)
if any([memory_message, trial_progress_messages, trial_errors]):
msg = Template("tune_status_messages.html.j2").render(
memory_message=memory_message,
trial_progress_messages=trial_progress_messages,
trial_errors=trial_errors,
)
else:
msg = None
return Template("tune_status.html.j2").render(
status_table=status_table,
sys_info_message=_generate_sys_info_str(*sys_info),
trial_progress=trial_progress,
messages=msg,
)
[docs]@PublicAPI
class CLIReporter(TuneReporterBase):
"""Command-line reporter
Args:
metric_columns: 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.
parameter_columns: Names of parameters to
include in progress table. If this is a dict, the keys should
be parameter names and the values should be the displayed names.
If this is a list, the parameter name is used directly. If empty,
defaults to all available parameters.
max_progress_rows: 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: 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_column_length: Maximum column length (in characters). Column
headers and values longer than this will be abbreviated.
max_report_frequency: Maximum report frequency in seconds.
Defaults to 5s.
infer_limit: Maximum number of metrics to automatically infer
from tune results.
print_intermediate_tables: Print intermediate result
tables. If None (default), will be set to True for verbosity
levels above 3, otherwise False. If True, intermediate tables
will be printed with experiment progress. If False, tables
will only be printed at then end of the tuning run for verbosity
levels greater than 2.
metric: Metric used to determine best current trial.
mode: One of [min, max]. Determines whether objective is
minimizing or maximizing the metric attribute.
sort_by_metric: Sort terminated trials by metric in the
intermediate table. Defaults to False.
"""
def __init__(
self,
*,
metric_columns: Optional[Union[List[str], Dict[str, str]]] = None,
parameter_columns: Optional[Union[List[str], Dict[str, str]]] = None,
total_samples: Optional[int] = None,
max_progress_rows: int = 20,
max_error_rows: int = 20,
max_column_length: int = 20,
max_report_frequency: int = 5,
infer_limit: int = 3,
print_intermediate_tables: Optional[bool] = None,
metric: Optional[str] = None,
mode: Optional[str] = None,
sort_by_metric: bool = False,
):
super(CLIReporter, self).__init__(
metric_columns=metric_columns,
parameter_columns=parameter_columns,
total_samples=total_samples,
max_progress_rows=max_progress_rows,
max_error_rows=max_error_rows,
max_column_length=max_column_length,
max_report_frequency=max_report_frequency,
infer_limit=infer_limit,
print_intermediate_tables=print_intermediate_tables,
metric=metric,
mode=mode,
sort_by_metric=sort_by_metric,
)
def _print(self, msg: str):
safe_print(msg)
def report(self, trials: List[Trial], done: bool, *sys_info: Dict):
self._print(self._progress_str(trials, done, *sys_info))
def _get_memory_usage() -> Tuple[float, float, Optional[str]]:
"""Get the current memory consumption.
Returns:
Memory used, memory available, and optionally a warning
message to be shown to the user when memory consumption is higher
than 90% or if `psutil` is not installed
"""
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:
message = (
": ***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:
message = None
return round(used_gb, 1), round(total_gb, 1), message
except ImportError:
return (
np.nan,
np.nan,
"Unknown memory usage. Please run `pip install psutil` to resolve",
)
def _get_time_str(start_time: float, current_time: float) -> Tuple[str, str]:
"""Get strings representing the current and elapsed time.
Args:
start_time: POSIX timestamp of the start of the tune run
current_time: POSIX timestamp giving the current time
Returns:
Current time and elapsed time for the current run
"""
current_time_dt = datetime.datetime.fromtimestamp(current_time)
start_time_dt = datetime.datetime.fromtimestamp(start_time)
delta: datetime.timedelta = current_time_dt - start_time_dt
rest = delta.total_seconds()
days = rest // (60 * 60 * 24)
rest -= days * (60 * 60 * 24)
hours = rest // (60 * 60)
rest -= hours * (60 * 60)
minutes = rest // 60
seconds = rest - minutes * 60
if days > 0:
running_for_str = f"{days:.0f} days, "
else:
running_for_str = ""
running_for_str += f"{hours:02.0f}:{minutes:02.0f}:{seconds:05.2f}"
return f"{current_time_dt:%Y-%m-%d %H:%M:%S}", running_for_str
def _time_passed_str(start_time: float, current_time: float) -> str:
"""Generate a message describing the current and elapsed time in the run.
Args:
start_time: POSIX timestamp of the start of the tune run
current_time: POSIX timestamp giving the current time
Returns:
Message with the current and elapsed time for the current tune run,
formatted to be displayed to the user
"""
current_time_str, running_for_str = _get_time_str(start_time, current_time)
return f"Current time: {current_time_str} " f"(running for {running_for_str})"
def _get_trials_by_state(trials: List[Trial]):
trials_by_state = collections.defaultdict(list)
for t in trials:
trials_by_state[t.status].append(t)
return trials_by_state
def _trial_progress_str(
trials: List[Trial],
metric_columns: Union[List[str], Dict[str, str]],
parameter_columns: Optional[Union[List[str], Dict[str, str]]] = None,
total_samples: int = 0,
force_table: bool = False,
fmt: str = "psql",
max_rows: Optional[int] = None,
max_column_length: int = 20,
done: bool = False,
metric: Optional[str] = None,
mode: Optional[str] = None,
sort_by_metric: bool = False,
):
"""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 of trials to get progress string for.
metric_columns: 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.
parameter_columns: Names of parameters to
include. If this is a dict, the keys are parameter names and the
values are the names to use in the message. If this is a list,
the parameter name is used in the message directly. If this is
empty, all parameters are used in the message.
total_samples: Total number of trials that will be generated.
force_table: Force printing a table. If False, a table will
be printed only at the end of the training for verbosity levels
above `Verbosity.V2_TRIAL_NORM`.
fmt: Output format (see tablefmt in tabulate API).
max_rows: Maximum number of rows in the trial table. Defaults to
unlimited.
max_column_length: Maximum column length (in characters).
done: True indicates that the tuning run finished.
metric: Metric used to sort trials.
mode: One of [min, max]. Determines whether objective is
minimizing or maximizing the metric attribute.
sort_by_metric: Sort terminated trials by metric in the
intermediate table. Defaults to False.
"""
messages = []
delim = "<br>" if fmt == "html" else "\n"
if len(trials) < 1:
return delim.join(messages)
num_trials = len(trials)
trials_by_state = _get_trials_by_state(trials)
for local_dir in sorted({t.local_experiment_path for t in trials}):
messages.append("Result logdir: {}".format(local_dir))
num_trials_strs = [
"{} {}".format(len(trials_by_state[state]), state)
for state in sorted(trials_by_state)
]
if total_samples and total_samples >= sys.maxsize:
total_samples = "infinite"
messages.append(
"Number of trials: {}{} ({})".format(
num_trials,
f"/{total_samples}" if total_samples else "",
", ".join(num_trials_strs),
)
)
if force_table or (has_verbosity(Verbosity.V2_TRIAL_NORM) and done):
messages += _trial_progress_table(
trials=trials,
metric_columns=metric_columns,
parameter_columns=parameter_columns,
fmt=fmt,
max_rows=max_rows,
metric=metric,
mode=mode,
sort_by_metric=sort_by_metric,
max_column_length=max_column_length,
)
return delim.join(messages)
def _max_len(
value: Any, max_len: int = 20, add_addr: bool = False, wrap: bool = False
) -> Any:
"""Abbreviate a string representation of an object to `max_len` characters.
For numbers, booleans and None, the original value will be returned for
correct rendering in the table formatting tool.
Args:
value: Object to be represented as a string.
max_len: Maximum return string length.
add_addr: If True, will add part of the object address to the end of the
string, e.g. to identify different instances of the same class. If
False, three dots (``...``) will be used instead.
"""
if value is None or isinstance(value, (int, float, numbers.Number, bool)):
return value
string = str(value)
if len(string) <= max_len:
return string
if wrap:
# Maximum two rows.
# Todo: Make this configurable in the refactor
if len(value) > max_len * 2:
value = "..." + string[(3 - (max_len * 2)) :]
wrapped = textwrap.wrap(value, width=max_len)
return "\n".join(wrapped)
if add_addr and not isinstance(value, (int, float, bool)):
result = f"{string[: (max_len - 5)]}_{hex(id(value))[-4:]}"
return result
result = "..." + string[(3 - max_len) :]
return result
def _get_progress_table_data(
trials: List[Trial],
metric_columns: Union[List[str], Dict[str, str]],
parameter_columns: Optional[Union[List[str], Dict[str, str]]] = None,
max_rows: Optional[int] = None,
metric: Optional[str] = None,
mode: Optional[str] = None,
sort_by_metric: bool = False,
max_column_length: int = 20,
) -> Tuple[List, List[str], Tuple[bool, str]]:
"""Generate a table showing the current progress of tuning trials.
Args:
trials: List of trials for which progress is to be shown.
metric_columns: Metrics to be displayed in the table.
parameter_columns: List of parameters to be included in the data
max_rows: Maximum number of rows to show. If there's overflow, a
message will be shown to the user indicating that some rows
are not displayed
metric: Metric which is being tuned
mode: Sort the table in descending order if mode is "max";
ascending otherwise
sort_by_metric: If true, the table will be sorted by the metric
max_column_length: Max number of characters in each column
Returns:
- Trial data
- List of column names
- Overflow tuple:
- boolean indicating whether the table has rows which are hidden
- string with info about the overflowing rows
"""
num_trials = len(trials)
trials_by_state = _get_trials_by_state(trials)
# Sort terminated trials by metric and mode, descending if mode is "max"
if sort_by_metric:
trials_by_state[Trial.TERMINATED] = sorted(
trials_by_state[Trial.TERMINATED],
reverse=(mode == "max"),
key=lambda t: unflattened_lookup(metric, t.last_result, default=None),
)
state_tbl_order = [
Trial.RUNNING,
Trial.PAUSED,
Trial.PENDING,
Trial.TERMINATED,
Trial.ERROR,
]
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, sort_by_metric
)
trials = []
overflow_strs = []
for state in state_tbl_order:
if state not in trials_by_state:
continue
trials += trials_by_state_trunc[state]
num = len(trials_by_state[state]) - len(trials_by_state_trunc[state])
if num > 0:
overflow_strs.append("{} {}".format(num, state))
# Build overflow string.
overflow = num_trials - max_rows
overflow_str = ", ".join(overflow_strs)
else:
overflow = False
overflow_str = ""
trials = []
for state in state_tbl_order:
if state not in trials_by_state:
continue
trials += trials_by_state[state]
# Pre-process trials to figure out what columns to show.
if isinstance(metric_columns, Mapping):
metric_keys = list(metric_columns.keys())
else:
metric_keys = metric_columns
metric_keys = [
k
for k in metric_keys
if any(
unflattened_lookup(k, t.last_result, default=None) is not None
for t in trials
)
]
if not parameter_columns:
parameter_keys = sorted(set().union(*[t.evaluated_params for t in trials]))
elif isinstance(parameter_columns, Mapping):
parameter_keys = list(parameter_columns.keys())
else:
parameter_keys = parameter_columns
# Build trial rows.
trial_table = [
_get_trial_info(
trial, parameter_keys, metric_keys, max_column_length=max_column_length
)
for trial in trials
]
# Format column headings
if isinstance(metric_columns, Mapping):
formatted_metric_columns = [
_max_len(
metric_columns[k], max_len=max_column_length, add_addr=False, wrap=True
)
for k in metric_keys
]
else:
formatted_metric_columns = [
_max_len(k, max_len=max_column_length, add_addr=False, wrap=True)
for k in metric_keys
]
if isinstance(parameter_columns, Mapping):
formatted_parameter_columns = [
_max_len(
parameter_columns[k],
max_len=max_column_length,
add_addr=False,
wrap=True,
)
for k in parameter_keys
]
else:
formatted_parameter_columns = [
_max_len(k, max_len=max_column_length, add_addr=False, wrap=True)
for k in parameter_keys
]
columns = (
["Trial name", "status", "loc"]
+ formatted_parameter_columns
+ formatted_metric_columns
)
return trial_table, columns, (overflow, overflow_str)
def _trial_progress_table(
trials: List[Trial],
metric_columns: Union[List[str], Dict[str, str]],
parameter_columns: Optional[Union[List[str], Dict[str, str]]] = None,
fmt: str = "psql",
max_rows: Optional[int] = None,
metric: Optional[str] = None,
mode: Optional[str] = None,
sort_by_metric: bool = False,
max_column_length: int = 20,
) -> List[str]:
"""Generate a list of trial progress table messages.
Args:
trials: List of trials for which progress is to be shown.
metric_columns: Metrics to be displayed in the table.
parameter_columns: List of parameters to be included in the data
fmt: Format of the table; passed to tabulate as the fmtstr argument
max_rows: Maximum number of rows to show. If there's overflow, a
message will be shown to the user indicating that some rows
are not displayed
metric: Metric which is being tuned
mode: Sort the table in descenting order if mode is "max";
ascending otherwise
sort_by_metric: If true, the table will be sorted by the metric
max_column_length: Max number of characters in each column
Returns:
Messages to be shown to the user containing progress tables
"""
data, columns, (overflow, overflow_str) = _get_progress_table_data(
trials,
metric_columns,
parameter_columns,
max_rows,
metric,
mode,
sort_by_metric,
max_column_length,
)
messages = [tabulate(data, headers=columns, tablefmt=fmt, showindex=False)]
if overflow:
messages.append(f"... {overflow} more trials not shown ({overflow_str})")
return messages
def _generate_sys_info_str(*sys_info) -> str:
"""Format system info into a string.
*sys_info: System info strings to be included.
Returns:
Formatted string containing system information.
"""
if sys_info:
return "<br>".join(sys_info).replace("\n", "<br>")
return ""
def _trial_errors_str(
trials: List[Trial], fmt: str = "psql", max_rows: Optional[int] = None
):
"""Returns a readable message regarding trial errors.
Args:
trials: List of trials to get progress string for.
fmt: Output format (see tablefmt in tabulate API).
max_rows: 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))
if num_failed > (max_rows or float("inf")):
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 _best_trial_str(
trial: Trial,
metric: str,
parameter_columns: Optional[Union[List[str], Dict[str, str]]] = None,
):
"""Returns a readable message stating the current best trial."""
val = unflattened_lookup(metric, trial.last_result, default=None)
config = trial.last_result.get("config", {})
parameter_columns = parameter_columns or list(config.keys())
if isinstance(parameter_columns, Mapping):
parameter_columns = parameter_columns.keys()
params = {p: unflattened_lookup(p, config) for p in parameter_columns}
return (
f"Current best trial: {trial.trial_id} with {metric}={val} and "
f"parameters={params}"
)
def _fair_filter_trials(
trials_by_state: Dict[str, List[Trial]],
max_trials: int,
sort_by_metric: bool = False,
):
"""Filters trials such that each state is represented fairly.
The oldest trials are truncated if necessary.
Args:
trials_by_state: 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 sorted(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 if the trails is not sorted by metric.
sorted_trials_by_state = dict()
for state in sorted(trials_by_state):
if state == Trial.TERMINATED and sort_by_metric:
sorted_trials_by_state[state] = trials_by_state[state]
else:
sorted_trials_by_state[state] = sorted(
trials_by_state[state], reverse=False, key=lambda t: t.trial_id
)
# Truncate oldest trials.
filtered_trials = {
state: sorted_trials_by_state[state][: num_trials_by_state[state]]
for state in sorted(trials_by_state)
}
return filtered_trials
def _get_trial_location(trial: Trial, result: dict) -> _Location:
# we get the location from the result, as the one in trial will be
# reset when trial terminates
node_ip, pid = result.get(NODE_IP, None), result.get(PID, None)
if node_ip and pid:
location = _Location(node_ip, pid)
else:
# fallback to trial location if there hasn't been a report yet
location = trial.location
return location
def _get_trial_info(
trial: Trial, parameters: List[str], metrics: List[str], max_column_length: int = 20
):
"""Returns the following information about a trial:
name | status | loc | params... | metrics...
Args:
trial: Trial to get information for.
parameters: Names of trial parameters to include.
metrics: Names of metrics to include.
max_column_length: Maximum column length (in characters).
"""
result = trial.last_result
config = trial.config
location = _get_trial_location(trial, result)
trial_info = [str(trial), trial.status, str(location)]
trial_info += [
_max_len(
unflattened_lookup(param, config, default=None),
max_len=max_column_length,
add_addr=True,
)
for param in parameters
]
trial_info += [
_max_len(
unflattened_lookup(metric, result, default=None),
max_len=max_column_length,
add_addr=True,
)
for metric in metrics
]
return trial_info
@DeveloperAPI
class TrialProgressCallback(Callback):
"""Reports (prints) intermediate trial progress.
This callback is automatically added to the callback stack. When a
result is obtained, this callback will print the results according to
the specified verbosity level.
For ``Verbosity.V3_TRIAL_DETAILS``, a full result list is printed.
For ``Verbosity.V2_TRIAL_NORM``, only one line is printed per received
result.
All other verbosity levels do not print intermediate trial progress.
Result printing is throttled on a per-trial basis. Per default, results are
printed only once every 30 seconds. Results are always printed when a trial
finished or errored.
"""
def __init__(
self, metric: Optional[str] = None, progress_metrics: Optional[List[str]] = None
):
self._last_print = collections.defaultdict(float)
self._last_print_iteration = collections.defaultdict(int)
self._completed_trials = set()
self._last_result_str = {}
self._metric = metric
self._progress_metrics = set(progress_metrics or [])
# Only use progress metrics if at least two metrics are in there
if self._metric and self._progress_metrics:
self._progress_metrics.add(self._metric)
self._last_result = {}
self._display_handle = None
def _print(self, msg: str):
safe_print(msg)
def on_trial_result(
self,
iteration: int,
trials: List["Trial"],
trial: "Trial",
result: Dict,
**info,
):
self.log_result(trial, result, error=False)
def on_trial_error(
self, iteration: int, trials: List["Trial"], trial: "Trial", **info
):
self.log_result(trial, trial.last_result, error=True)
def on_trial_complete(
self, iteration: int, trials: List["Trial"], trial: "Trial", **info
):
# Only log when we never logged that a trial was completed
if trial not in self._completed_trials:
self._completed_trials.add(trial)
print_result_str = self._print_result(trial.last_result)
last_result_str = self._last_result_str.get(trial, "")
# If this is a new result, print full result string
if print_result_str != last_result_str:
self.log_result(trial, trial.last_result, error=False)
else:
self._print(f"Trial {trial} completed. Last result: {print_result_str}")
def log_result(self, trial: "Trial", result: Dict, error: bool = False):
done = result.get("done", False) is True
last_print = self._last_print[trial]
should_print = done or error or time.time() - last_print > DEBUG_PRINT_INTERVAL
if done and trial not in self._completed_trials:
self._completed_trials.add(trial)
if should_print:
if IS_NOTEBOOK:
self.display_result(trial, result, error, done)
else:
self.print_result(trial, result, error, done)
self._last_print[trial] = time.time()
if TRAINING_ITERATION in result:
self._last_print_iteration[trial] = result[TRAINING_ITERATION]
def print_result(self, trial: Trial, result: Dict, error: bool, done: bool):
"""Print the most recent results for the given trial to stdout.
Args:
trial: Trial for which results are to be printed
result: Result to be printed
error: True if an error has occurred, False otherwise
done: True if the trial is finished, False otherwise
"""
last_print_iteration = self._last_print_iteration[trial]
if has_verbosity(Verbosity.V3_TRIAL_DETAILS):
if result.get(TRAINING_ITERATION) != last_print_iteration:
self._print(f"Result for {trial}:")
self._print(" {}".format(pretty_print(result).replace("\n", "\n ")))
if done:
self._print(f"Trial {trial} completed.")
elif has_verbosity(Verbosity.V2_TRIAL_NORM):
metric_name = self._metric or "_metric"
metric_value = result.get(metric_name, -99.0)
error_file = os.path.join(trial.local_path, "error.txt")
info = ""
if done:
info = " This trial completed."
print_result_str = self._print_result(result)
self._last_result_str[trial] = print_result_str
if error:
message = (
f"The trial {trial} errored with "
f"parameters={trial.config}. "
f"Error file: {error_file}"
)
elif self._metric:
message = (
f"Trial {trial} reported "
f"{metric_name}={metric_value:.2f} "
f"with parameters={trial.config}.{info}"
)
else:
message = (
f"Trial {trial} reported "
f"{print_result_str} "
f"with parameters={trial.config}.{info}"
)
self._print(message)
def generate_trial_table(
self, trials: Dict[Trial, Dict], columns: List[str]
) -> str:
"""Generate an HTML table of trial progress info.
Trials (rows) are sorted by name; progress stats (columns) are sorted
as well.
Args:
trials: Trials and their associated latest results
columns: Columns to show in the table; must be a list of valid
keys for each Trial result
Returns:
HTML template containing a rendered table of progress info
"""
data = []
columns = sorted(columns)
sorted_trials = collections.OrderedDict(
sorted(self._last_result.items(), key=lambda item: str(item[0]))
)
for trial, result in sorted_trials.items():
data.append([str(trial)] + [result.get(col, "") for col in columns])
return Template("trial_progress.html.j2").render(
table=tabulate(
data, tablefmt="html", headers=["Trial name"] + columns, showindex=False
)
)
def display_result(self, trial: Trial, result: Dict, error: bool, done: bool):
"""Display a formatted HTML table of trial progress results.
Trial progress is only shown if verbosity is set to level 2 or 3.
Args:
trial: Trial for which results are to be printed
result: Result to be printed
error: True if an error has occurred, False otherwise
done: True if the trial is finished, False otherwise
"""
from IPython.display import display, HTML
self._last_result[trial] = result
if has_verbosity(Verbosity.V3_TRIAL_DETAILS):
ignored_keys = {
"config",
"hist_stats",
}
elif has_verbosity(Verbosity.V2_TRIAL_NORM):
ignored_keys = {
"config",
"hist_stats",
"trial_id",
"experiment_tag",
"done",
} | set(AUTO_RESULT_KEYS)
else:
return
table = self.generate_trial_table(
self._last_result, set(result.keys()) - ignored_keys
)
if not self._display_handle:
self._display_handle = display(HTML(table), display_id=True)
else:
self._display_handle.update(HTML(table))
def _print_result(self, result: Dict):
if self._progress_metrics:
# If progress metrics are given, only report these
flat_result = flatten_dict(result)
print_result = {}
for metric in self._progress_metrics:
print_result[metric] = flat_result.get(metric)
else:
# Else, skip auto populated results
print_result = result.copy()
for skip_result in SKIP_RESULTS_IN_REPORT:
print_result.pop(skip_result, None)
for auto_result in AUTO_RESULT_KEYS:
print_result.pop(auto_result, None)
print_result_str = ",".join(
[f"{k}={v}" for k, v in print_result.items() if v is not None]
)
return print_result_str
def _detect_reporter(**kwargs) -> TuneReporterBase:
"""Detect progress reporter class.
Will return a :class:`JupyterNotebookReporter` if a IPython/Jupyter-like
session was detected, and a :class:`CLIReporter` otherwise.
Keyword arguments are passed on to the reporter class.
"""
if IS_NOTEBOOK:
kwargs.setdefault("overwrite", not has_verbosity(Verbosity.V2_TRIAL_NORM))
progress_reporter = JupyterNotebookReporter(**kwargs)
else:
progress_reporter = CLIReporter(**kwargs)
return progress_reporter
def _detect_progress_metrics(
trainable: Optional[Union["Trainable", Callable]]
) -> Optional[List[str]]:
"""Detect progress metrics to report."""
if not trainable:
return None
return getattr(trainable, "_progress_metrics", None)
def _prepare_progress_reporter_for_ray_client(
progress_reporter: ProgressReporter,
verbosity: Union[int, Verbosity],
string_queue: Optional[Queue] = None,
) -> Tuple[ProgressReporter, Queue]:
"""Prepares progress reported for Ray Client by setting the string queue.
The string queue will be created if it's None."""
set_verbosity(verbosity)
progress_reporter = progress_reporter or _detect_reporter()
# JupyterNotebooks don't work with remote tune runs out of the box
# (e.g. via Ray client) as they don't have access to the main
# process stdout. So we introduce a queue here that accepts
# strings, which will then be displayed on the driver side.
if isinstance(progress_reporter, RemoteReporterMixin):
if string_queue is None:
string_queue = Queue(
actor_options={"num_cpus": 0, **_force_on_current_node(None)}
)
progress_reporter.output_queue = string_queue
return progress_reporter, string_queue
def _stream_client_output(
remote_future: ray.ObjectRef,
progress_reporter: ProgressReporter,
string_queue: Queue,
) -> Any:
"""
Stream items from string queue to progress_reporter until remote_future resolves
"""
if string_queue is None:
return
def get_next_queue_item():
try:
return string_queue.get(block=False)
except Empty:
return None
def _handle_string_queue():
string_item = get_next_queue_item()
while string_item is not None:
# This happens on the driver side
progress_reporter.display(string_item)
string_item = get_next_queue_item()
# ray.wait(...)[1] returns futures that are not ready, yet
while ray.wait([remote_future], timeout=0.2)[1]:
# Check if we have items to execute
_handle_string_queue()
# Handle queue one last time
_handle_string_queue()