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New console logger with expanded progress tracking (#11972)
* Add `ConsoleLogger.v3` This addition expands the progress bar feature to count up the training/distillation steps to either the next evaluation pass or the maximum number of steps. * Rename progress bar types * Add defaults to docs Minor fixes * Move comment * Minor punctuation fixes * Explicitly check for `None` when validating progress bar type Co-authored-by: Paul O'Leary McCann <polm@dampfkraft.com>
This commit is contained in:
Madeesh Kannan
GitHub
parent
90896504a5
commit
aa2b471a6e
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@ -962,6 +962,7 @@ class Errors(metaclass=ErrorsWithCodes):
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E1046 = ("{cls_name} is an abstract class and cannot be instantiated. If you are looking for spaCy's default "
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"knowledge base, use `InMemoryLookupKB`.")
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E1047 = ("`find_threshold()` only supports components with a `scorer` attribute.")
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E1048 = ("Got '{unexpected}' as console progress bar type, but expected one of the following: {expected}")
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# Deprecated model shortcuts, only used in errors and warnings
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@ -26,6 +26,8 @@ def setup_table(
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return final_cols, final_widths, ["r" for _ in final_widths]
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# We cannot rename this method as it's directly imported
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# and used by external packages such as spacy-loggers.
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@registry.loggers("spacy.ConsoleLogger.v2")
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def console_logger(
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progress_bar: bool = False,
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@ -33,7 +35,27 @@ def console_logger(
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output_file: Optional[Union[str, Path]] = None,
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):
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"""The ConsoleLogger.v2 prints out training logs in the console and/or saves them to a jsonl file.
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progress_bar (bool): Whether the logger should print the progress bar.
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progress_bar (bool): Whether the logger should print a progress bar tracking the steps till the next evaluation pass.
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console_output (bool): Whether the logger should print the logs on the console.
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output_file (Optional[Union[str, Path]]): The file to save the training logs to.
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"""
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return console_logger_v3(
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progress_bar=None if progress_bar is False else "eval",
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console_output=console_output,
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output_file=output_file,
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)
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@registry.loggers("spacy.ConsoleLogger.v3")
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def console_logger_v3(
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progress_bar: Optional[str] = None,
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console_output: bool = True,
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output_file: Optional[Union[str, Path]] = None,
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):
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"""The ConsoleLogger.v3 prints out training logs in the console and/or saves them to a jsonl file.
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progress_bar (Optional[str]): Type of progress bar to show in the console. Allowed values:
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train - Tracks the number of steps from the beginning of training until the full training run is complete (training.max_steps is reached).
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eval - Tracks the number of steps between the previous and next evaluation (training.eval_frequency is reached).
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console_output (bool): Whether the logger should print the logs on the console.
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output_file (Optional[Union[str, Path]]): The file to save the training logs to.
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"""
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@ -70,6 +92,7 @@ def console_logger(
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for name, proc in nlp.pipeline
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if hasattr(proc, "is_trainable") and proc.is_trainable
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]
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max_steps = nlp.config["training"]["max_steps"]
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eval_frequency = nlp.config["training"]["eval_frequency"]
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score_weights = nlp.config["training"]["score_weights"]
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score_cols = [col for col, value in score_weights.items() if value is not None]
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@ -84,6 +107,13 @@ def console_logger(
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write(msg.row(table_header, widths=table_widths, spacing=spacing))
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write(msg.row(["-" * width for width in table_widths], spacing=spacing))
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progress = None
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expected_progress_types = ("train", "eval")
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if progress_bar is not None and progress_bar not in expected_progress_types:
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raise ValueError(
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Errors.E1048.format(
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unexpected=progress_bar, expected=expected_progress_types
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)
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)
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def log_step(info: Optional[Dict[str, Any]]) -> None:
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nonlocal progress
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@ -141,11 +171,23 @@ def console_logger(
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)
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)
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if progress_bar:
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if progress_bar == "train":
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total = max_steps
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desc = f"Last Eval Epoch: {info['epoch']}"
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initial = info["step"]
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else:
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total = eval_frequency
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desc = f"Epoch {info['epoch']+1}"
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initial = 0
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# Set disable=None, so that it disables on non-TTY
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progress = tqdm.tqdm(
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total=eval_frequency, disable=None, leave=False, file=stderr
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total=total,
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disable=None,
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leave=False,
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file=stderr,
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initial=initial,
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)
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progress.set_description(f"Epoch {info['epoch']+1}")
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progress.set_description(desc)
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def finalize() -> None:
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if output_stream:
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@ -513,7 +513,7 @@ a [Weights & Biases](https://www.wandb.com/) dashboard.
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Instead of using one of the built-in loggers, you can
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[implement your own](/usage/training#custom-logging).
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#### spacy.ConsoleLogger.v2 {#ConsoleLogger tag="registered function"}
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#### spacy.ConsoleLogger.v2 {tag="registered function"}
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> #### Example config
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>
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@ -564,11 +564,33 @@ start decreasing across epochs.
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</Accordion>
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| Name | Description |
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| ---------------- | --------------------------------------------------------------------- |
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| `progress_bar` | Whether the logger should print the progress bar ~~bool~~ |
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| `console_output` | Whether the logger should print the logs on the console. ~~bool~~ |
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| `output_file` | The file to save the training logs to. ~~Optional[Union[str, Path]]~~ |
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| Name | Description |
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| ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
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| `progress_bar` | Whether the logger should print a progress bar tracking the steps till the next evaluation pass (default: `False`). ~~bool~~ |
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| `console_output` | Whether the logger should print the logs in the console (default: `True`). ~~bool~~ |
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| `output_file` | The file to save the training logs to (default: `None`). ~~Optional[Union[str, Path]]~~ |
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#### spacy.ConsoleLogger.v3 {#ConsoleLogger tag="registered function"}
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> #### Example config
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>
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> ```ini
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> [training.logger]
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> @loggers = "spacy.ConsoleLogger.v3"
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> progress_bar = "all_steps"
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> console_output = true
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> output_file = "training_log.jsonl"
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> ```
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Writes the results of a training step to the console in a tabular format and
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optionally saves them to a `jsonl` file.
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| Name | Description |
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| ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `progress_bar` | Type of progress bar to show in the console: `"train"`, `"eval"` or `None`. |
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| | The bar tracks the number of steps until `training.max_steps` and `training.eval_frequency` are reached respectively (default: `None`). ~~Optional[str]~~ |
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| `console_output` | Whether the logger should print the logs in the console (default: `True`). ~~bool~~ |
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| `output_file` | The file to save the training logs to (default: `None`). ~~Optional[Union[str, Path]]~~ |
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## Readers {#readers}
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