Source code for pytorch_lightning.callbacks.progress.rich_progress
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from dataclasses import dataclass
from datetime import timedelta
from typing import Any, Dict, Optional, Union
import pytorch_lightning as pl
from pytorch_lightning.callbacks.progress.base import ProgressBarBase
from pytorch_lightning.utilities.imports import _RICH_AVAILABLE
Task, Style = None, None
if _RICH_AVAILABLE:
from rich import get_console, reconfigure
from rich.console import RenderableType
from rich.progress import BarColumn, Progress, ProgressColumn, Task, TaskID, TextColumn
from rich.progress_bar import ProgressBar
from rich.style import Style
from rich.text import Text
class CustomBarColumn(BarColumn):
"""Overrides ``BarColumn`` to provide support for dataloaders that do not define a size (infinite size)
such as ``IterableDataset``."""
def render(self, task: "Task") -> ProgressBar:
"""Gets a progress bar widget for a task."""
return ProgressBar(
total=max(0, task.total),
completed=max(0, task.completed),
width=None if self.bar_width is None else max(1, self.bar_width),
pulse=not task.started or not math.isfinite(task.remaining),
animation_time=task.get_time(),
style=self.style,
complete_style=self.complete_style,
finished_style=self.finished_style,
pulse_style=self.pulse_style,
)
@dataclass
class CustomInfiniteTask(Task):
"""Overrides ``Task`` to define an infinite task.
This is useful for datasets that do not define a size (infinite size) such as ``IterableDataset``.
"""
@property
def time_remaining(self) -> Optional[float]:
return None
class CustomProgress(Progress):
"""Overrides ``Progress`` to support adding tasks that have an infinite total size."""
def add_task(
self,
description: str,
start: bool = True,
total: float = 100.0,
completed: int = 0,
visible: bool = True,
**fields: Any,
) -> TaskID:
if not math.isfinite(total):
task = CustomInfiniteTask(
self._task_index,
description,
total,
completed,
visible=visible,
fields=fields,
_get_time=self.get_time,
_lock=self._lock,
)
return self.add_custom_task(task)
return super().add_task(description, start, total, completed, visible, **fields)
def add_custom_task(self, task: CustomInfiniteTask, start: bool = True):
with self._lock:
self._tasks[self._task_index] = task
if start:
self.start_task(self._task_index)
new_task_index = self._task_index
self._task_index = TaskID(int(self._task_index) + 1)
self.refresh()
return new_task_index
class CustomTimeColumn(ProgressColumn):
# Only refresh twice a second to prevent jitter
max_refresh = 0.5
def __init__(self, style: Union[str, Style]) -> None:
self.style = style
super().__init__()
def render(self, task) -> Text:
elapsed = task.finished_time if task.finished else task.elapsed
remaining = task.time_remaining
elapsed_delta = "-:--:--" if elapsed is None else str(timedelta(seconds=int(elapsed)))
remaining_delta = "-:--:--" if remaining is None else str(timedelta(seconds=int(remaining)))
return Text(f"{elapsed_delta} • {remaining_delta}", style=self.style)
class BatchesProcessedColumn(ProgressColumn):
def __init__(self, style: Union[str, Style]):
self.style = style
super().__init__()
def render(self, task) -> RenderableType:
total = task.total if task.total != float("inf") else "--"
return Text(f"{int(task.completed)}/{total}", style=self.style)
class ProcessingSpeedColumn(ProgressColumn):
def __init__(self, style: Union[str, Style]):
self.style = style
super().__init__()
def render(self, task) -> RenderableType:
task_speed = f"{task.speed:>.2f}" if task.speed is not None else "0.00"
return Text(f"{task_speed}it/s", style=self.style)
class MetricsTextColumn(ProgressColumn):
"""A column containing text."""
def __init__(self, trainer, style):
self._trainer = trainer
self._tasks = {}
self._current_task_id = 0
self._metrics = {}
self._style = style
super().__init__()
def update(self, metrics):
# Called when metrics are ready to be rendered.
# This is to prevent render from causing deadlock issues by requesting metrics
# in separate threads.
self._metrics = metrics
def render(self, task) -> Text:
if (
self._trainer.state.fn != "fit"
or self._trainer.sanity_checking
or self._trainer.progress_bar_callback.main_progress_bar_id != task.id
):
return Text()
if self._trainer.training and task.id not in self._tasks:
self._tasks[task.id] = "None"
if self._renderable_cache:
self._tasks[self._current_task_id] = self._renderable_cache[self._current_task_id][1]
self._current_task_id = task.id
if self._trainer.training and task.id != self._current_task_id:
return self._tasks[task.id]
text = ""
for k, v in self._metrics.items():
text += f"{k}: {round(v, 3) if isinstance(v, float) else v} "
return Text(text, justify="left", style=self._style)
@dataclass
class RichProgressBarTheme:
"""Styles to associate to different base components.
Args:
description: Style for the progress bar description. For eg., Epoch x, Testing, etc.
progress_bar: Style for the bar in progress.
progress_bar_finished: Style for the finished progress bar.
progress_bar_pulse: Style for the progress bar when `IterableDataset` is being processed.
batch_progress: Style for the progress tracker (i.e 10/50 batches completed).
time: Style for the processed time and estimate time remaining.
processing_speed: Style for the speed of the batches being processed.
metrics: Style for the metrics
https://rich.readthedocs.io/en/stable/style.html
"""
description: Union[str, Style] = "white"
progress_bar: Union[str, Style] = "#6206E0"
progress_bar_finished: Union[str, Style] = "#6206E0"
progress_bar_pulse: Union[str, Style] = "#6206E0"
batch_progress: Union[str, Style] = "white"
time: Union[str, Style] = "grey54"
processing_speed: Union[str, Style] = "grey70"
metrics: Union[str, Style] = "white"
[docs]class RichProgressBar(ProgressBarBase):
"""Create a progress bar with `rich text formatting <https://github.com/willmcgugan/rich>`_.
Install it with pip:
.. code-block:: bash
pip install rich
.. code-block:: python
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import RichProgressBar
trainer = Trainer(callbacks=RichProgressBar())
Args:
refresh_rate: Determines at which rate (in number of batches) the progress bars get updated.
Set it to ``0`` to disable the display.
leave: Leaves the finished progress bar in the terminal at the end of the epoch. Default: False
theme: Contains styles used to stylize the progress bar.
console_kwargs: Args for constructing a `Console`
Raises:
ModuleNotFoundError:
If required `rich` package is not installed on the device.
Note:
PyCharm users will need to enable “emulate terminal” in output console option in
run/debug configuration to see styled output.
Reference: https://rich.readthedocs.io/en/latest/introduction.html#requirements
"""
def __init__(
self,
refresh_rate: int = 1,
leave: bool = False,
theme: RichProgressBarTheme = RichProgressBarTheme(),
console_kwargs: Optional[Dict[str, Any]] = None,
) -> None:
if not _RICH_AVAILABLE:
raise ModuleNotFoundError(
"`RichProgressBar` requires `rich` >= 10.2.2. Install it by running `pip install -U rich`."
)
super().__init__()
self._refresh_rate: int = refresh_rate
self._leave: bool = leave
self._console_kwargs = console_kwargs or {}
self._enabled: bool = True
self.progress: Optional[Progress] = None
self.val_sanity_progress_bar_id: Optional[int] = None
self._reset_progress_bar_ids()
self._metric_component = None
self._progress_stopped: bool = False
self.theme = theme
self._update_for_light_colab_theme()
@property
def refresh_rate(self) -> float:
return self._refresh_rate
@property
def is_enabled(self) -> bool:
return self._enabled and self.refresh_rate > 0
@property
def is_disabled(self) -> bool:
return not self.is_enabled
def _update_for_light_colab_theme(self) -> None:
if _detect_light_colab_theme():
attributes = ["description", "batch_progress", "metrics"]
for attr in attributes:
if getattr(self.theme, attr) == "white":
setattr(self.theme, attr, "black")
def _init_progress(self, trainer):
if self.is_enabled and (self.progress is None or self._progress_stopped):
self._reset_progress_bar_ids()
reconfigure(**self._console_kwargs)
self._console = get_console()
self._console.clear_live()
self._metric_component = MetricsTextColumn(trainer, self.theme.metrics)
self.progress = CustomProgress(
*self.configure_columns(trainer),
self._metric_component,
auto_refresh=False,
disable=self.is_disabled,
console=self._console,
)
self.progress.start()
# progress has started
self._progress_stopped = False
def refresh(self) -> None:
if self.progress:
self.progress.refresh()
[docs] def on_sanity_check_end(self, trainer, pl_module):
if self.progress is not None:
self.progress.update(self.val_sanity_progress_bar_id, advance=0, visible=False)
self.refresh()
[docs] def on_train_epoch_start(self, trainer, pl_module):
total_train_batches = self.total_train_batches
total_val_batches = self.total_val_batches
if total_train_batches != float("inf"):
# val can be checked multiple times per epoch
val_checks_per_epoch = total_train_batches // trainer.val_check_batch
total_val_batches = total_val_batches * val_checks_per_epoch
total_batches = total_train_batches + total_val_batches
train_description = self._get_train_description(trainer.current_epoch)
if self.main_progress_bar_id is not None and self._leave:
self._stop_progress()
self._init_progress(trainer)
if self.main_progress_bar_id is None:
self.main_progress_bar_id = self._add_task(total_batches, train_description)
elif self.progress is not None:
self.progress.reset(
self.main_progress_bar_id, total=total_batches, description=train_description, visible=True
)
self.refresh()
[docs] def on_validation_batch_start(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
) -> None:
if not self.has_dataloader_changed(dataloader_idx):
return
if trainer.sanity_checking:
if self.val_sanity_progress_bar_id is not None:
self.progress.update(self.val_sanity_progress_bar_id, advance=0, visible=False)
self.val_sanity_progress_bar_id = self._add_task(
self.total_val_batches_current_dataloader, self.sanity_check_description, visible=False
)
else:
if self.val_progress_bar_id is not None:
self.progress.update(self.val_progress_bar_id, advance=0, visible=False)
# TODO: remove old tasks when new onces are created
self.val_progress_bar_id = self._add_task(
self.total_val_batches_current_dataloader, self.validation_description, visible=False
)
self.refresh()
def _add_task(self, total_batches: int, description: str, visible: bool = True) -> Optional[int]:
if self.progress is not None:
return self.progress.add_task(
f"[{self.theme.description}]{description}", total=total_batches, visible=visible
)
def _update(self, progress_bar_id: int, current: int, visible: bool = True) -> None:
if self.progress is not None and self.is_enabled:
total = self.progress.tasks[progress_bar_id].total
if not self._should_update(current, total):
return
leftover = current % self.refresh_rate
advance = leftover if (current == total and leftover != 0) else self.refresh_rate
self.progress.update(progress_bar_id, advance=advance, visible=visible)
self.refresh()
def _should_update(self, current: int, total: Union[int, float]) -> bool:
return current % self.refresh_rate == 0 or current == total
[docs] def on_validation_epoch_end(self, trainer, pl_module):
if self.val_progress_bar_id is not None and trainer.state.fn == "fit":
self.progress.update(self.val_progress_bar_id, advance=0, visible=False)
self.refresh()
[docs] def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
if trainer.state.fn == "fit":
self._update_metrics(trainer, pl_module)
self.reset_dataloader_idx_tracker()
[docs] def on_test_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
self.reset_dataloader_idx_tracker()
[docs] def on_predict_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
self.reset_dataloader_idx_tracker()
[docs] def on_test_batch_start(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
) -> None:
if not self.has_dataloader_changed(dataloader_idx):
return
if self.test_progress_bar_id is not None:
self.progress.update(self.test_progress_bar_id, advance=0, visible=False)
self.test_progress_bar_id = self._add_task(self.total_test_batches_current_dataloader, self.test_description)
self.refresh()
[docs] def on_predict_batch_start(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
) -> None:
if not self.has_dataloader_changed(dataloader_idx):
return
if self.predict_progress_bar_id is not None:
self.progress.update(self.predict_progress_bar_id, advance=0, visible=False)
self.predict_progress_bar_id = self._add_task(
self.total_predict_batches_current_dataloader, self.predict_description
)
self.refresh()
[docs] def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
self._update(self.main_progress_bar_id, self.train_batch_idx + self._val_processed)
self._update_metrics(trainer, pl_module)
self.refresh()
[docs] def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
self._update_metrics(trainer, pl_module)
[docs] def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
if trainer.sanity_checking:
self._update(self.val_sanity_progress_bar_id, self.val_batch_idx)
elif self.val_progress_bar_id is not None:
# check to see if we should update the main training progress bar
if self.main_progress_bar_id is not None:
self._update(self.main_progress_bar_id, self.train_batch_idx + self._val_processed)
self._update(self.val_progress_bar_id, self.val_batch_idx)
self.refresh()
[docs] def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
self._update(self.test_progress_bar_id, self.test_batch_idx)
self.refresh()
[docs] def on_predict_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
self._update(self.predict_progress_bar_id, self.predict_batch_idx)
self.refresh()
def _get_train_description(self, current_epoch: int) -> str:
train_description = f"Epoch {current_epoch}"
if len(self.validation_description) > len(train_description):
# Padding is required to avoid flickering due of uneven lengths of "Epoch X"
# and "Validation" Bar description
num_digits = len(str(current_epoch))
required_padding = (len(self.validation_description) - len(train_description) + 1) - num_digits
for _ in range(required_padding):
train_description += " "
return train_description
def _stop_progress(self) -> None:
if self.progress is not None:
self.progress.stop()
# # signals for progress to be re-initialized for next stages
self._progress_stopped = True
def _reset_progress_bar_ids(self):
self.main_progress_bar_id: Optional[int] = None
self.val_progress_bar_id: Optional[int] = None
self.test_progress_bar_id: Optional[int] = None
self.predict_progress_bar_id: Optional[int] = None
def _update_metrics(self, trainer, pl_module) -> None:
metrics = self.get_metrics(trainer, pl_module)
if self._metric_component:
self._metric_component.update(metrics)
[docs] def teardown(self, trainer, pl_module, stage: Optional[str] = None) -> None:
self._stop_progress()
[docs] def on_exception(self, trainer, pl_module, exception: BaseException) -> None:
self._stop_progress()
@property
def val_progress_bar(self) -> Task:
return self.progress.tasks[self.val_progress_bar_id]
@property
def val_sanity_check_bar(self) -> Task:
return self.progress.tasks[self.val_sanity_progress_bar_id]
@property
def main_progress_bar(self) -> Task:
return self.progress.tasks[self.main_progress_bar_id]
@property
def test_progress_bar(self) -> Task:
return self.progress.tasks[self.test_progress_bar_id]
def configure_columns(self, trainer) -> list:
return [
TextColumn("[progress.description]{task.description}"),
CustomBarColumn(
complete_style=self.theme.progress_bar,
finished_style=self.theme.progress_bar_finished,
pulse_style=self.theme.progress_bar_pulse,
),
BatchesProcessedColumn(style=self.theme.batch_progress),
CustomTimeColumn(style=self.theme.time),
ProcessingSpeedColumn(style=self.theme.processing_speed),
]
def _detect_light_colab_theme() -> bool:
"""Detect if it's light theme in Colab."""
try:
get_ipython # type: ignore
except NameError:
return False
ipython = get_ipython() # noqa: F821
if "google.colab" in str(ipython.__class__):
try:
from google.colab import output
return output.eval_js('document.documentElement.matches("[theme=light]")')
except ModuleNotFoundError:
return False
return False
