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65 lines
2.8 KiB
Python
65 lines
2.8 KiB
Python
from typing import Any, Dict, List, Optional
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from tml.common.wandb import WandbConfig
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from tml.core.config import base_config
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from tml.projects.twhin.data.config import TwhinDataConfig
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from tml.projects.twhin.models.config import TwhinModelConfig
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import pydantic
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class RuntimeConfig(base_config.BaseConfig):
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"""
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Configuration for runtime settings.
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Attributes:
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- wandb (Optional[WandbConfig]): Configuration for Wandb (Weights and Biases) integration.
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- enable_tensorfloat32 (bool): Enable tensorfloat32 if on Ampere devices.
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- enable_amp (bool): Enable automatic mixed precision.
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"""
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wandb: WandbConfig = pydantic.Field(None)
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enable_tensorfloat32: bool = pydantic.Field(
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False, description="Use tensorfloat32 if on Ampere devices."
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)
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enable_amp: bool = pydantic.Field(False, description="Enable automatic mixed precision.")
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class TrainingConfig(base_config.BaseConfig):
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"""
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Configuration for training settings.
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Attributes:
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- save_dir (str): Directory to save checkpoints.
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- num_train_steps (pydantic.PositiveInt): Number of training steps.
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- initial_checkpoint_dir (str): Directory of initial checkpoints (optional).
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- checkpoint_every_n (pydantic.PositiveInt): Save checkpoints every 'n' steps.
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- checkpoint_max_to_keep (pydantic.PositiveInt): Maximum number of checkpoints to keep (optional).
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- train_log_every_n (pydantic.PositiveInt): Log training information every 'n' steps.
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- num_eval_steps (int): Number of evaluation steps. If < 0, the entire dataset will be used.
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- eval_log_every_n (pydantic.PositiveInt): Log evaluation information every 'n' steps.
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- eval_timeout_in_s (pydantic.PositiveFloat): Evaluation timeout in seconds.
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- gradient_accumulation (int): Number of replica steps to accumulate gradients (optional).
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- num_epochs (pydantic.PositiveInt): Number of training epochs.
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"""
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save_dir: str = pydantic.Field("/tmp/model", description="Directory to save checkpoints.")
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num_train_steps: pydantic.PositiveInt = 10000
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initial_checkpoint_dir: str = pydantic.Field(
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None, description="Directory of initial checkpoints", at_most_one_of="initialization"
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)
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checkpoint_every_n: pydantic.PositiveInt = 1000
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checkpoint_max_to_keep: pydantic.PositiveInt = pydantic.Field(
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None, description="Maximum number of checkpoints to keep. Defaults to keeping all."
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)
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train_log_every_n: pydantic.PositiveInt = 1000
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num_eval_steps: int = pydantic.Field(
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16384, description="Number of evaluation steps. If < 0 the entire dataset will be used."
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)
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eval_log_every_n: pydantic.PositiveInt = 5000
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eval_timeout_in_s: pydantic.PositiveFloat = 60 * 60
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gradient_accumulation: int = pydantic.Field(
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None, description="Number of replica steps to accumulate gradients."
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)
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num_epochs: pydantic.PositiveInt = 1
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