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58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
"""This is a very limited feature training loop useful for interactive debugging.
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It is not intended for actual model tranining (it is not fast, doesn't compile the model).
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It does not support checkpointing.
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suggested use:
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from tml.core import debug_training_loop
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debug_training_loop.train(...)
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"""
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from typing import Iterable, Optional, Dict, Callable, List
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import torch
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from torch.optim.lr_scheduler import _LRScheduler
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import torchmetrics as tm
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from tml.ml_logging.torch_logging import logging
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def train(
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model: torch.nn.Module,
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optimizer: torch.optim.Optimizer,
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train_steps: int,
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dataset: Iterable,
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scheduler: _LRScheduler = None,
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# Accept any arguments (to be compatible with the real training loop)
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# but just ignore them.
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*args,
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**kwargs,
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) -> None:
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"""
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Debugging training loop. Do not use for actual model training.
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Args:
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model (torch.nn.Module): The neural network model.
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optimizer (torch.optim.Optimizer): The optimizer for model optimization.
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train_steps (int): The number of training steps to perform.
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dataset (Iterable): Data iterator for training data.
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scheduler (_LRScheduler, optional): Learning rate scheduler (default: None).
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*args: Additional arguments (ignored).
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**kwargs: Additional keyword arguments (ignored).
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"""
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logging.warning("Running debug training loop, don't use for model training.")
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data_iter = iter(dataset)
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for step in range(0, train_steps + 1):
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x = next(data_iter)
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optimizer.zero_grad()
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loss, outputs = model.forward(x)
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loss.backward()
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optimizer.step()
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if scheduler:
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scheduler.step()
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logging.info(f"Step {step} completed. Loss = {loss}")
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