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Continue training args and tqdm in notebooks (#3939)

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* Continue training args

* Continue training args

* added explaination

* added explaination

* added explaination

* Fixed tqdm auto

* Update src/transformers/training_args.py

Co-Authored-By: Julien Chaumond <chaumond@gmail.com>

* Update src/transformers/training_args.py

* Update src/transformers/training_args.py

Co-authored-by: Julien Chaumond <chaumond@gmail.com>
This commit is contained in:
Suraj Parmar 2020-05-01 07:44:08 +05:30 committed by GitHub
parent ab90353f1a
commit 8b5e5ebcf9
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2 changed files with 19 additions and 7 deletions
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2
src/transformers/trainer.py
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@ -15,7 +15,7 @@ from torch.utils.data.dataloader import DataLoader
from torch.utils.data.dataset import Dataset
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data.sampler import RandomSampler
from tqdm import tqdm, trange
from tqdm.auto import tqdm, trange
from .data.data_collator import DataCollator, DefaultDataCollator
from .modeling_utils import PreTrainedModel

24
src/transformers/training_args.py
View File

@ -29,20 +29,27 @@ class TrainingArguments:
metadata={"help": "The output directory where the model predictions and checkpoints will be written."}
)
overwrite_output_dir: bool = field(
default=False, metadata={"help": "Overwrite the content of the output directory"}
default=False,
metadata={
"help": (
"Overwrite the content of the output directory."
"Use this to continue training if output_dir points to a checkpoint directory."
)
},
)
do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."})
evaluate_during_training: bool = field(
default=False, metadata={"help": "Run evaluation during training at each logging step."}
default=False, metadata={"help": "Run evaluation during training at each logging step."},
)
per_gpu_train_batch_size: int = field(default=8, metadata={"help": "Batch size per GPU/CPU for training."})
per_gpu_eval_batch_size: int = field(default=8, metadata={"help": "Batch size per GPU/CPU for evaluation."})
gradient_accumulation_steps: int = field(
default=1, metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."}
default=1,
metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."},
)
learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for Adam."})
@ -64,7 +71,10 @@ class TrainingArguments:
save_total_limit: Optional[int] = field(
default=None,
metadata={
"help": "Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default"
"help": (
"Limit the total amount of checkpoints."
"Deletes the older checkpoints in the output_dir. Default is unlimited checkpoints"
)
},
)
no_cuda: bool = field(default=False, metadata={"help": "Avoid using CUDA even if it is available"})
@ -77,8 +87,10 @@ class TrainingArguments:
fp16_opt_level: str = field(
default="O1",
metadata={
"help": "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
"help": (
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
)
},
)
local_rank: int = field(default=-1, metadata={"help": "For distributed training: local_rank"})