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Update docs to explain disabling callbacks using report_to #26155

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Oct 11, 2023
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Update docs to explain disabling callbacks using report_to #26155

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4d24457
feat: update callback doc to explain disabling callbacks using report_to
nebrelbug Sep 13, 2023
f71f558
docs: update report_to docstring
nebrelbug Sep 14, 2023
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4 changes: 3 additions & 1 deletion docs/source/en/main_classes/callback.md
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Expand Up @@ -25,7 +25,7 @@ Callbacks are "read only" pieces of code, apart from the [`TrainerControl`] obje
cannot change anything in the training loop. For customizations that require changes in the training loop, you should
subclass [`Trainer`] and override the methods you need (see [trainer](trainer) for examples).

By default a [`Trainer`] will use the following callbacks:
By default, `TrainingArguments.report_to` is set to `"all"`, so a [`Trainer`] will use the following callbacks.

- [`DefaultFlowCallback`] which handles the default behavior for logging, saving and evaluation.
- [`PrinterCallback`] or [`ProgressCallback`] to display progress and print the
Expand All @@ -45,6 +45,8 @@ By default a [`Trainer`] will use the following callbacks:
- [`~integrations.DagsHubCallback`] if [dagshub](https://dagshub.com/) is installed.
- [`~integrations.FlyteCallback`] if [flyte](https://flyte.org/) is installed.

If a package is installed but you don't wish to use the accompanying integration, you can change `TrainingArguments.report_to` to a list of just those integrations you want to use (e.g. `["azure_ml", "wandb"]`).

The main class that implements callbacks is [`TrainerCallback`]. It gets the
[`TrainingArguments`] used to instantiate the [`Trainer`], can access that
Trainer's internal state via [`TrainerState`], and can take some actions on the training loop via
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7 changes: 4 additions & 3 deletions src/transformers/training_args.py
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Expand Up @@ -2382,10 +2382,11 @@ def set_logging(
Logger log level to use on the main process. Possible choices are the log levels as strings: `"debug"`,
`"info"`, `"warning"`, `"error"` and `"critical"`, plus a `"passive"` level which doesn't set anything
and lets the application set the level.
report_to (`str` or `List[str]`, *optional*, defaults to `"none"`):
report_to (`str` or `List[str]`, *optional*, defaults to `"all"`):
The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,
`"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. Use `"all"` to report
to all integrations installed, `"none"` for no integrations.
`"clearml"`, `"codecarbon"`, `"comet_ml"`, `"dagshub"`, `"flyte"`, `"mlflow"`, `"neptune"`,
`"tensorboard"`, and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"` for no
integrations.
first_step (`bool`, *optional*, defaults to `False`):
Whether to log and evaluate the first `global_step` or not.
nan_inf_filter (`bool`, *optional*, defaults to `True`):
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