mattertune.configs.loggers

class mattertune.configs.loggers.CSVLoggerConfig(*, type='csv', save_dir, name='lightning_logs', version=None, prefix='', flush_logs_every_n_steps=100)[source]
Parameters:

  • type (Literal['csv'])

  • save_dir (str)

  • name (str)

  • version (int | str | None)

  • prefix (str)

  • flush_logs_every_n_steps (int)

type: Literal['csv']
save_dir: str

Save directory for logs.

name: str

'lightning_logs'.

Type:

Experiment name. Default

version: int | str | None

Experiment version. If not specified, automatically assigns the next available version. Default: None.

prefix: str

''.

Type:

String to put at the beginning of metric keys. Default

flush_logs_every_n_steps: int

100.

Type:

How often to flush logs to disk. Default

create_logger()[source]

Creates a CSVLogger instance from this config.

class mattertune.configs.loggers.TensorBoardLoggerConfig(*, type='tensorboard', save_dir, name='lightning_logs', version=None, log_graph=False, default_hp_metric=True, prefix='', sub_dir=None, additional_params={})[source]
Parameters:

  • type (Literal['tensorboard'])

  • save_dir (str)

  • name (str | None)

  • version (int | str | None)

  • log_graph (bool)

  • default_hp_metric (bool)

  • prefix (str)

  • sub_dir (str | None)

  • additional_params (dict[str, Any])

type: Literal['tensorboard']
save_dir: str

Save directory where TensorBoard logs will be saved.

name: str | None

'lightning_logs'. If empty string, no per-experiment subdirectory is used.

Type:

Experiment name. Default

version: int | str | None

Experiment version. If not specified, logger auto-assigns next available version. If string, used as run-specific subdirectory name. Default: None.

log_graph: bool

Whether to add computational graph to tensorboard. Requires model.example_input_array to be defined. Default: False.

default_hp_metric: bool

Enables placeholder metric with key hp_metric when logging hyperparameters without a metric. Default: True.

prefix: str

''.

Type:

String to put at beginning of metric keys. Default

sub_dir: str | None

Sub-directory to group TensorBoard logs. If provided, logs are saved in /save_dir/name/version/sub_dir/. Default: None.

additional_params: dict[str, Any]

{}.

Type:

Additional parameters passed to tensorboardX.SummaryWriter. Default

create_logger()[source]

Creates a TensorBoardLogger instance from this config.

class mattertune.configs.loggers.WandbLoggerConfig(*, type='wandb', name=None, save_dir='.', version=None, offline=False, dir=None, id=None, anonymous=None, project=None, log_model=False, prefix='', experiment=None, checkpoint_name=None, additional_init_parameters={})[source]
Parameters:

  • type (Literal['wandb'])

  • name (str | None)

  • save_dir (str)

  • version (str | None)

  • offline (bool)

  • dir (str | None)

  • id (str | None)

  • anonymous (bool | None)

  • project (str | None)

  • log_model (Literal['all'] | bool)

  • prefix (str)

  • experiment (Any | None)

  • checkpoint_name (str | None)

  • additional_init_parameters (dict[str, Any])

type: Literal['wandb']
name: str | None

None.

Type:

Display name for the run. Default

save_dir: str

..

Type:

Path where data is saved. Default

version: str | None

None.

Type:

Sets the version, mainly used to resume a previous run. Default

offline: bool

False.

Type:

Run offline (data can be streamed later to wandb servers). Default

dir: str | None

None.

Type:

Same as save_dir. Default

id: str | None

None.

Type:

Same as version. Default

anonymous: bool | None

None.

Type:

Enables or explicitly disables anonymous logging. Default

project: str | None

None.

Type:

The name of the project to which this run will belong. Default

log_model: Literal['all'] | bool

False. If ‘all’, checkpoints are logged during training. If True, checkpoints are logged at the end of training. If False, no checkpoints are logged.

Type:

Whether/how to log model checkpoints as W&B artifacts. Default

prefix: str

''.

Type:

A string to put at the beginning of metric keys. Default

experiment: Any | None

None.

Type:

WandB experiment object. Automatically set when creating a run. Default

checkpoint_name: str | None

None.

Type:

Name of the model checkpoint artifact being logged. Default

additional_init_parameters: dict[str, Any]

{}.

Type:

Additional parameters to pass to wandb.init(). Default

create_logger()[source]

Creates a WandbLogger instance from this config.