tensorboard¶
Classes
Log to local file system in TensorBoard format. |
TensorBoard Logger¶
- class lightning.pytorch.loggers.tensorboard.TensorBoardLogger(save_dir, name='lightning_logs', version=None, log_graph=False, default_hp_metric=True, prefix='', sub_dir=None, **kwargs)[source]¶
Bases:
lightning.pytorch.loggers.logger.Logger
,lightning.fabric.loggers.tensorboard.TensorBoardLogger
Log to local file system in TensorBoard format.
Implemented using
SummaryWriter
. Logs are saved toos.path.join(save_dir, name, version)
. This is the default logger in Lightning, it comes preinstalled.Example:
from lightning.pytorch import Trainer from lightning.pytorch.loggers import TensorBoardLogger logger = TensorBoardLogger("tb_logs", name="my_model") trainer = Trainer(logger=logger)
- Parameters
name¶ (
Optional
[str
]) – Experiment name. Defaults to'default'
. If it is the empty string then no per-experiment subdirectory is used.version¶ (
Union
[int
,str
,None
]) – Experiment version. If version is not specified the logger inspects the save directory for existing versions, then automatically assigns the next available version. If it is a string then it is used as the run-specific subdirectory name, otherwise'version_${version}'
is used.log_graph¶ (
bool
) – Adds the computational graph to tensorboard. This requires that the user has defined the self.example_input_array attribute in their model.default_hp_metric¶ (
bool
) – Enables a placeholder metric with key hp_metric when log_hyperparams is called without a metric (otherwise calls to log_hyperparams without a metric are ignored).prefix¶ (
str
) – A string to put at the beginning of metric keys.sub_dir¶ (
Union
[str
,Path
,None
]) – Sub-directory to group TensorBoard logs. If a sub_dir argument is passed then logs are saved in/save_dir/name/version/sub_dir/
. Defaults toNone
in which logs are saved in/save_dir/name/version/
.**kwargs¶ – Additional arguments used by
tensorboardX.SummaryWriter
can be passed as keyword arguments in this logger. To automatically flush to disk, max_queue sets the size of the queue for pending logs before flushing. flush_secs determines how many seconds elapses before flushing.
Example
>>> import shutil, tempfile >>> tmp = tempfile.mkdtemp() >>> tbl = TensorBoardLogger(tmp) >>> tbl.log_hyperparams({"epochs": 5, "optimizer": "Adam"}) >>> tbl.log_metrics({"acc": 0.75}) >>> tbl.log_metrics({"acc": 0.9}) >>> tbl.finalize("success") >>> shutil.rmtree(tmp)
- after_save_checkpoint(checkpoint_callback)[source]¶
Called after model checkpoint callback saves a new checkpoint.
- Parameters
checkpoint_callback¶ (
ModelCheckpoint
) – the model checkpoint callback instance- Return type
- log_hyperparams(params, metrics=None)[source]¶
Record hyperparameters. TensorBoard logs with and without saved hyperparameters are incompatible, the hyperparameters are then not displayed in the TensorBoard. Please delete or move the previously saved logs to display the new ones with hyperparameters.
- property log_dir: str¶
The directory for this run’s tensorboard checkpoint.
By default, it is named
'version_${self.version}'
but it can be overridden by passing a string value for the constructor’s version parameter instead ofNone
or an int.- Return type