SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Introduce logging_strategy training argument (#10267) (#10267)

Browse Source 
Introduce logging_strategy training argument
in TrainingArguments and TFTrainingArguments. (#9838)
This commit is contained in:
Tanmay Garg
2021-02-19 22:19:22 +05:30
committed by GitHub
parent 34df26ec3a
commit 709c86b5a9
4 changed files with 39 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
src/transformers/training_args.py
View File

@@ -25,7 +25,7 @@ from .file_utils import (
is_torch_tpu_available,
torch_required,
)
from .trainer_utils import EvaluationStrategy, SchedulerType
from .trainer_utils import EvaluationStrategy, LoggingStrategy, SchedulerType
from .utils import logging
@@ -139,10 +139,17 @@ class TrainingArguments:
logging_dir (:obj:`str`, `optional`):
`TensorBoard <https://www.tensorflow.org/tensorboard>`__ log directory. Will default to
`runs/**CURRENT_DATETIME_HOSTNAME**`.
logging_strategy (:obj:`str` or :class:`~transformers.trainer_utils.LoggingStrategy`, `optional`, defaults to :obj:`"steps"`):
The logging strategy to adopt during training. Possible values are:
* :obj:`"no"`: No logging is done during training.
* :obj:`"epoch"`: Logging is done at the end of each epoch.
* :obj:`"steps"`: Logging is done every :obj:`logging_steps`.
logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to log and evaluate the first :obj:`global_step` or not.
logging_steps (:obj:`int`, `optional`, defaults to 500):
Number of update steps between two logs.
Number of update steps between two logs if :obj:`logging_strategy="steps"`.
save_steps (:obj:`int`, `optional`, defaults to 500):
Number of updates steps before two checkpoint saves.
save_total_limit (:obj:`int`, `optional`):
@@ -339,6 +346,10 @@ class TrainingArguments:
warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
logging_dir: Optional[str] = field(default_factory=default_logdir, metadata={"help": "Tensorboard log dir."})
logging_strategy: LoggingStrategy = field(
default="steps",
metadata={"help": "The logging strategy to use."},
)
logging_first_step: bool = field(default=False, metadata={"help": "Log the first global_step"})
logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
@@ -482,6 +493,7 @@ class TrainingArguments:
if self.disable_tqdm is None:
self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN
self.evaluation_strategy = EvaluationStrategy(self.evaluation_strategy)
self.logging_strategy = LoggingStrategy(self.logging_strategy)
self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type)
if self.do_eval is False and self.evaluation_strategy != EvaluationStrategy.NO:
self.do_eval = True