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Fix some edge cases in report_to and add deprecation warnings (#10100)

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Sylvain Gugger
2021-02-09 10:38:12 -05:00
committed by GitHub
parent 78f4a0e7e5
commit 77c0ce8c0c
2 changed files with 18 additions and 2 deletions
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16
src/transformers/training_args.py
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@@ -239,9 +239,10 @@ class TrainingArguments:
group_by_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to group together samples of roughly the same legnth in the training dataset (to minimize
padding applied and be more efficient). Only useful if applying dynamic padding.
report_to (:obj:`List[str]`, `optional`, defaults to the list of integrations platforms installed):
report_to (:obj:`str` or :obj:`List[str]`, `optional`, defaults to :obj:`"all"`):
The list of integrations to report the results and logs to. Supported platforms are :obj:`"azure_ml"`,
:obj:`"comet_ml"`, :obj:`"mlflow"`, :obj:`"tensorboard"` and :obj:`"wandb"`.
:obj:`"comet_ml"`, :obj:`"mlflow"`, :obj:`"tensorboard"` and :obj:`"wandb"`. Use :obj:`"all"` to report to
all integrations installed, :obj:`"none"` for no integrations.
ddp_find_unused_parameters (:obj:`bool`, `optional`):
When using distributed training, the value of the flag :obj:`find_unused_parameters` passed to
:obj:`DistributedDataParallel`. Will default to :obj:`False` if gradient checkpointing is used, :obj:`True`
@@ -478,10 +479,21 @@ class TrainingArguments:
if is_torch_available() and self.device.type != "cuda" and self.fp16:
raise ValueError("Mixed precision training with AMP or APEX (`--fp16`) can only be used on CUDA devices.")
if self.report_to is None:
logger.info(
"The default value for the training argument `--report_to` will change in v5 (from all installed "
"integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as "
"now. You should start updating your code and make this info disappear :-)."
)
self.report_to = "all"
if self.report_to == "all" or self.report_to == ["all"]:
# Import at runtime to avoid a circular import.
from .integrations import get_available_reporting_integrations
self.report_to = get_available_reporting_integrations()
elif self.report_to == "none" or self.report_to == ["none"]:
self.report_to = []
elif not isinstance(self.report_to, list):
self.report_to = [self.report_to]
def __repr__(self):
# We override the default repr to remove deprecated arguments from the repr. This method should be removed once