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(training_utils) add NeptuneCallback
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,105 @@ | ||
| from abc import ABC | ||
| from os import PathLike | ||
| from typing import Any, Literal | ||
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| from neptune import Run, init_run # type: ignore | ||
| from neptune.internal.init.parameters import ( # type: ignore | ||
| ASYNC_LAG_THRESHOLD, | ||
| ASYNC_NO_PROGRESS_THRESHOLD, | ||
| DEFAULT_FLUSH_PERIOD, | ||
| ) | ||
| from neptune.metadata_containers.abstract import NeptuneObjectCallback # type: ignore | ||
| from neptune.types.atoms.git_ref import GitRef, GitRefDisabled # type: ignore | ||
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| from refiners.training_utils.callback import Callback, CallbackConfig | ||
| from refiners.training_utils.config import BaseConfig | ||
| from refiners.training_utils.trainer import Trainer, register_callback | ||
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| AnyTrainer = Trainer[BaseConfig, Any] | ||
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| class NeptuneConfig(CallbackConfig): | ||
| """Neptune.ai run configuration | ||
| See https://docs.neptune.ai/api/neptune#init_run | ||
| and https://github.com/neptune-ai/neptune-client/blob/1cd8452045e8524318f59216d151d73328f85bd1/src/neptune/objects/run.py#L131 | ||
| """ | ||
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| project: str | None = None | ||
| api_token: str | None = None | ||
| with_id: str | None = None | ||
| custom_run_id: str | None = None | ||
| mode: Literal["async", "sync", "offline", "read-only", "debug"] | None = None | ||
| name: str | None = None | ||
| description: str | None = None | ||
| tags: str | list[str] | None = None | ||
| source_files: str | list[str] | None = None | ||
| capture_stdout: bool | None = None | ||
| capture_stderr: bool | None = None | ||
| capture_hardware_metrics: bool | None = None | ||
| fail_on_exception: bool = True | ||
| monitoring_namespace: str | None = None | ||
| flush_period: float = DEFAULT_FLUSH_PERIOD | ||
| proxies: dict[str, str] | None = None | ||
| capture_traceback: bool = True | ||
| git_ref: GitRef | GitRefDisabled | None = None | ||
| dependencies: PathLike[str] | str | None = None | ||
| async_lag_callback: NeptuneObjectCallback | None = None | ||
| async_no_progress_callback: NeptuneObjectCallback | None = None | ||
| async_lag_threshold: float = ASYNC_LAG_THRESHOLD | ||
| async_no_progress_threshold: float = ASYNC_NO_PROGRESS_THRESHOLD | ||
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| class NeptuneCallback(Callback[AnyTrainer]): | ||
| """Neptune.ai callback for logging metrics""" | ||
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| run: Run | ||
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| def __init__(self, config: NeptuneConfig) -> None: | ||
| """Initialize Neptune.ai callback | ||
| Args: | ||
| config: Neptune.ai run configuration | ||
| """ | ||
| self.config = config | ||
| self.epoch_losses: list[float] = [] | ||
| self.iteration_losses: list[float] = [] | ||
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| def on_train_begin(self, trainer: AnyTrainer) -> None: | ||
| # initialize Neptune `Run` (see https://docs.neptune.ai/api/run/) | ||
| self.run = init_run(**self.config.model_dump()) | ||
| self.run["config"] = trainer.config.model_dump() | ||
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| # reset epoch and iteration losses | ||
| self.epoch_losses = [] | ||
| self.iteration_losses = [] | ||
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| def on_compute_loss_end(self, trainer: AnyTrainer) -> None: | ||
| loss_value = trainer.loss.detach().cpu().item() | ||
| self.epoch_losses.append(loss_value) | ||
| self.iteration_losses.append(loss_value) | ||
| self.run["train/step_loss"].append(loss_value, step=trainer.clock.step) # type: ignore | ||
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| def on_optimizer_step_end(self, trainer: AnyTrainer) -> None: | ||
| self.run["train/total_grad_norm"].append(trainer.grad_norm, step=trainer.clock.step) # type: ignore | ||
| avg_iteration_loss = sum(self.iteration_losses) / len(self.iteration_losses) | ||
| self.run["train/average_iteration_loss"].append(avg_iteration_loss, step=trainer.clock.step) # type: ignore | ||
| self.iteration_losses = [] | ||
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| def on_epoch_end(self, trainer: AnyTrainer) -> None: | ||
| avg_epoch_loss = sum(self.epoch_losses) / len(self.epoch_losses) | ||
| self.run["train/average_epoch_loss"].append(avg_epoch_loss, step=trainer.clock.step) # type: ignore | ||
| self.run["train/epoch"].append(trainer.clock.epoch, step=trainer.clock.step) # type: ignore | ||
| self.epoch_losses = [] | ||
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| def on_lr_scheduler_step_end(self, trainer: AnyTrainer) -> None: | ||
| self.run["train/learning_rate"].append(trainer.optimizer.param_groups[0]["lr"], step=trainer.clock.step) # type: ignore | ||
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| def on_train_end(self, trainer: AnyTrainer) -> None: | ||
| self.run.stop() | ||
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| class NeptuneMixin(ABC): | ||
| @register_callback() | ||
| def neptune(self, config: NeptuneConfig) -> NeptuneCallback: | ||
| return NeptuneCallback(config) |