transformers/docs/source/en/main_classes/logging.md
Nilay Bhatnagar eedd21b9e7
Fixed Majority of the Typos in transformers[en] Documentation (#33350)
* Fixed typo: insted to instead

* Fixed typo: relase to release

* Fixed typo: nighlty to nightly

* Fixed typos: versatible, benchamarks, becnhmark to versatile, benchmark, benchmarks

* Fixed typo in comment: quantizd to quantized

* Fixed typo: architecutre to architecture

* Fixed typo: contibution to contribution

* Fixed typo: Presequities to Prerequisites

* Fixed typo: faste to faster

* Fixed typo: extendeding to extending

* Fixed typo: segmetantion_maps to segmentation_maps

* Fixed typo: Alternativelly to Alternatively

* Fixed incorrectly defined variable: output to output_disabled

* Fixed typo in library name: tranformers.onnx to transformers.onnx

* Fixed missing import: import tensorflow as tf

* Fixed incorrectly defined variable: token_tensor to tokens_tensor

* Fixed missing import: import torch

* Fixed incorrectly defined variable and typo: uromaize to uromanize

* Fixed incorrectly defined variable and typo: uromaize to uromanize

* Fixed typo in function args: numpy.ndarry to numpy.ndarray

* Fixed Inconsistent Library Name: Torchscript to TorchScript

* Fixed Inconsistent Class Name: OneformerProcessor to OneFormerProcessor

* Fixed Inconsistent Class Named Typo: TFLNetForMultipleChoice to TFXLNetForMultipleChoice

* Fixed Inconsistent Library Name Typo: Pytorch to PyTorch

* Fixed Inconsistent Function Name Typo: captureWarning to captureWarnings

* Fixed Inconsistent Library Name Typo: Pytorch to PyTorch

* Fixed Inconsistent Class Name Typo: TrainingArgument to TrainingArguments

* Fixed Inconsistent Model Name Typo: Swin2R to Swin2SR

* Fixed Inconsistent Model Name Typo: EART to BERT

* Fixed Inconsistent Library Name Typo: TensorFLow to TensorFlow

* Fixed Broken Link for Speech Emotion Classification with Wav2Vec2

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed Punctuation: Two commas

* Fixed Punctuation: No Space between XLM-R and is

* Fixed Punctuation: No Space between [~accelerate.Accelerator.backward] and method

* Added backticks to display model.fit() in codeblock

* Added backticks to display openai-community/gpt2 in codeblock

* Fixed Minor Typo: will to with

* Fixed Minor Typo: is to are

* Fixed Minor Typo: in to on

* Fixed Minor Typo: inhibits to exhibits

* Fixed Minor Typo: they need to it needs

* Fixed Minor Typo: cast the load the checkpoints To load the checkpoints

* Fixed Inconsistent Class Name Typo: TFCamembertForCasualLM to TFCamembertForCausalLM

* Fixed typo in attribute name: outputs.last_hidden_states to outputs.last_hidden_state

* Added missing verbosity level: fatal

* Fixed Minor Typo: take To takes

* Fixed Minor Typo: heuristic To heuristics

* Fixed Minor Typo: setting To settings

* Fixed Minor Typo: Content To Contents

* Fixed Minor Typo: millions To million

* Fixed Minor Typo: difference To differences

* Fixed Minor Typo: while extract To which extracts

* Fixed Minor Typo: Hereby To Here

* Fixed Minor Typo: addition To additional

* Fixed Minor Typo: supports To supported

* Fixed Minor Typo: so that benchmark results TO as a consequence, benchmark

* Fixed Minor Typo: a To an

* Fixed Minor Typo: a To an

* Fixed Minor Typo: Chain-of-though To Chain-of-thought
2024-09-09 10:47:24 +02:00

4.4 KiB

Logging

🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.

Currently the default verbosity of the library is WARNING.

To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity to the INFO level.

import transformers

transformers.logging.set_verbosity_info()

You can also use the environment variable TRANSFORMERS_VERBOSITY to override the default verbosity. You can set it to one of the following: debug, info, warning, error, critical, fatal. For example:

TRANSFORMERS_VERBOSITY=error ./myprogram.py

Additionally, some warnings can be disabled by setting the environment variable TRANSFORMERS_NO_ADVISORY_WARNINGS to a true value, like 1. This will disable any warning that is logged using [logger.warning_advice]. For example:

TRANSFORMERS_NO_ADVISORY_WARNINGS=1 ./myprogram.py

Here is an example of how to use the same logger as the library in your own module or script:

from transformers.utils import logging

logging.set_verbosity_info()
logger = logging.get_logger("transformers")
logger.info("INFO")
logger.warning("WARN")

All the methods of this logging module are documented below, the main ones are [logging.get_verbosity] to get the current level of verbosity in the logger and [logging.set_verbosity] to set the verbosity to the level of your choice. In order (from the least verbose to the most verbose), those levels (with their corresponding int values in parenthesis) are:

  • transformers.logging.CRITICAL or transformers.logging.FATAL (int value, 50): only report the most critical errors.
  • transformers.logging.ERROR (int value, 40): only report errors.
  • transformers.logging.WARNING or transformers.logging.WARN (int value, 30): only reports error and warnings. This is the default level used by the library.
  • transformers.logging.INFO (int value, 20): reports error, warnings and basic information.
  • transformers.logging.DEBUG (int value, 10): report all information.

By default, tqdm progress bars will be displayed during model download. [logging.disable_progress_bar] and [logging.enable_progress_bar] can be used to suppress or unsuppress this behavior.

logging vs warnings

Python has two logging systems that are often used in conjunction: logging, which is explained above, and warnings, which allows further classification of warnings in specific buckets, e.g., FutureWarning for a feature or path that has already been deprecated and DeprecationWarning to indicate an upcoming deprecation.

We use both in the transformers library. We leverage and adapt logging's captureWarnings method to allow management of these warning messages by the verbosity setters above.

What does that mean for developers of the library? We should respect the following heuristics:

  • warnings should be favored for developers of the library and libraries dependent on transformers
  • logging should be used for end-users of the library using it in every-day projects

See reference of the captureWarnings method below.

autodoc logging.captureWarnings

Base setters

autodoc logging.set_verbosity_error

autodoc logging.set_verbosity_warning

autodoc logging.set_verbosity_info

autodoc logging.set_verbosity_debug

Other functions

autodoc logging.get_verbosity

autodoc logging.set_verbosity

autodoc logging.get_logger

autodoc logging.enable_default_handler

autodoc logging.disable_default_handler

autodoc logging.enable_explicit_format

autodoc logging.reset_format

autodoc logging.enable_progress_bar

autodoc logging.disable_progress_bar