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Unverified Commit 8fb4d0e4 authored by Steven Liu's avatar Steven Liu Committed by GitHub
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Fix callback docstrings (#21005) 

* fix callback docstrings

* format as markdown list

* apply feedback
parent b7417bee
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Showing with 45 additions and 46 deletions
src/transformers/integrations.py
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......@@ -668,16 +668,16 @@ class WandbCallback(TrainerCallback):
variables:
Environment:
WANDB_LOG_MODEL (`bool`, *optional*, defaults to `False`):
Whether or not to log model as artifact at the end of training. Use along with
*TrainingArguments.load_best_model_at_end* to upload best model.
WANDB_WATCH (`str`, *optional* defaults to `"gradients"`):
Can be `"gradients"`, `"all"` or `"false"`. Set to `"false"` to disable gradient logging or `"all"` to
log gradients and parameters.
WANDB_PROJECT (`str`, *optional*, defaults to `"huggingface"`):
Set this to a custom string to store results in a different project.
WANDB_DISABLED (`bool`, *optional*, defaults to `False`):
Whether or not to disable wandb entirely. Set *WANDB_DISABLED=true* to disable.
- **WANDB_LOG_MODEL** (`bool`, *optional*, defaults to `False`):
Whether or not to log model as artifact at the end of training. Use along with
[`~transformers.TrainingArguments.load_best_model_at_end`] to upload best model.
- **WANDB_WATCH** (`str`, *optional*, defaults to `gradients`):
Can be `gradients`, `all` or `false`. Set to `false` to disable gradient logging or `all` to log gradients
and parameters.
- **WANDB_PROJECT** (`str`, *optional*, defaults to `huggingface`):
Set this to a custom string to store results in a different project.
- **WANDB_DISABLED** (`bool`, *optional*, defaults to `False`):
Whether or not to disable wandb entirely. Set `WANDB_DISABLED=True` to disable.
"""
if self._wandb is None:
return
......@@ -784,16 +784,16 @@ class CometCallback(TrainerCallback):
Setup the optional Comet.ml integration.
Environment:
COMET_MODE (`str`, *optional*):
Whether to create an online, offline experiment or disable Comet logging. Can be "OFFLINE", "ONLINE",
or "DISABLED". Defaults to "ONLINE".
COMET_PROJECT_NAME (`str`, *optional*):
Comet project name for experiments
COMET_OFFLINE_DIRECTORY (`str`, *optional*):
Folder to use for saving offline experiments when `COMET_MODE` is "OFFLINE"
COMET_LOG_ASSETS (`str`, *optional*):
Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be "TRUE", or
"FALSE". Defaults to "TRUE".
- **COMET_MODE** (`str`, *optional*, defaults to `ONLINE`):
Whether to create an online, offline experiment or disable Comet logging. Can be `OFFLINE`, `ONLINE`, or
`DISABLED`.
- **COMET_PROJECT_NAME** (`str`, *optional*):
Comet project name for experiments.
- **COMET_OFFLINE_DIRECTORY** (`str`, *optional*):
Folder to use for saving offline experiments when `COMET_MODE` is `OFFLINE`.
- **COMET_LOG_ASSETS** (`str`, *optional*, defaults to `TRUE`):
Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be `TRUE`, or
`FALSE`.
For a number of configurable items in the environment, see
[here](https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables).
......@@ -892,28 +892,27 @@ class MLflowCallback(TrainerCallback):
Setup the optional MLflow integration.
Environment:
HF_MLFLOW_LOG_ARTIFACTS (`str`, *optional*):
Whether to use MLflow .log_artifact() facility to log artifacts. This only makes sense if logging to a
remote server, e.g. s3 or GCS. If set to `True` or *1*, will copy each saved checkpoint on each save in
[`TrainingArguments`]'s `output_dir` to the local or remote artifact storage. Using it without a remote
storage will just copy the files to your artifact location.
MLFLOW_EXPERIMENT_NAME (`str`, *optional*):
Whether to use an MLflow experiment_name under which to launch the run. Default to "None" which will
point to the "Default" experiment in MLflow. Otherwise, it is a case sensitive name of the experiment
to be activated. If an experiment with this name does not exist, a new experiment with this name is
created.
MLFLOW_TAGS (`str`, *optional*):
A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example:
os.environ['MLFLOW_TAGS']='{"release.candidate": "RC1", "release.version": "2.2.0"}'
MLFLOW_NESTED_RUN (`str`, *optional*):
Whether to use MLflow nested runs. If set to `True` or *1*, will create a nested run inside the current
run.
MLFLOW_RUN_ID (`str`, *optional*):
Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint.
When MLFLOW_RUN_ID environment variable is set, start_run attempts to resume a run with the specified
run ID and other parameters are ignored.
MLFLOW_FLATTEN_PARAMS (`str`, *optional*):
Whether to flatten the parameters dictionary before logging. Default to `False`.
- **HF_MLFLOW_LOG_ARTIFACTS** (`str`, *optional*):
Whether to use MLflow `.log_artifact()` facility to log artifacts. This only makes sense if logging to a
remote server, e.g. s3 or GCS. If set to `True` or *1*, will copy each saved checkpoint on each save in
[`TrainingArguments`]'s `output_dir` to the local or remote artifact storage. Using it without a remote
storage will just copy the files to your artifact location.
- **MLFLOW_EXPERIMENT_NAME** (`str`, *optional*, defaults to `None`):
Whether to use an MLflow experiment_name under which to launch the run. Default to `None` which will point
to the `Default` experiment in MLflow. Otherwise, it is a case sensitive name of the experiment to be
activated. If an experiment with this name does not exist, a new experiment with this name is created.
- **MLFLOW_TAGS** (`str`, *optional*):
A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example:
`os.environ['MLFLOW_TAGS']='{"release.candidate": "RC1", "release.version": "2.2.0"}'`.
- **MLFLOW_NESTED_RUN** (`str`, *optional*):
Whether to use MLflow nested runs. If set to `True` or *1*, will create a nested run inside the current
run.
- **MLFLOW_RUN_ID** (`str`, *optional*):
Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint. When
`MLFLOW_RUN_ID` environment variable is set, `start_run` attempts to resume a run with the specified run ID
and other parameters are ignored.
- **MLFLOW_FLATTEN_PARAMS** (`str`, *optional*, defaults to `False`):
Whether to flatten the parameters dictionary before logging.
"""
self._log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
self._nested_run = os.getenv("MLFLOW_NESTED_RUN", "FALSE").upper() in ENV_VARS_TRUE_VALUES
......@@ -1310,10 +1309,10 @@ class ClearMLCallback(TrainerCallback):
A [`TrainerCallback`] that sends the logs to [ClearML](https://clear.ml/).
Environment:
CLEARML_PROJECT (`str`, *optional*, defaults to `"HuggingFace Transformers"`):
ClearML project name.
CLEARML_TASK (`str`, *optional* defaults to `"Trainer"`):
ClearML task name.
- **CLEARML_PROJECT** (`str`, *optional*, defaults to `HuggingFace Transformers`):
ClearML project name.
- **CLEARML_TASK** (`str`, *optional*, defaults to `Trainer`):
ClearML task name.
"""
def __init__(self):
......
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