pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer

class pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer(hidden_size: int = 16, lstm_layers: int = 1, dropout: float = 0.1, output_size: int | list[int] = 7, loss: MultiHorizonMetric = None, attention_head_size: int = 4, max_encoder_length: int = 10, static_categoricals: list[str] | None = None, static_reals: list[str] | None = None, time_varying_categoricals_encoder: list[str] | None = None, time_varying_categoricals_decoder: list[str] | None = None, categorical_groups: dict | list[str] | None = None, time_varying_reals_encoder: list[str] | None = None, time_varying_reals_decoder: list[str] | None = None, x_reals: list[str] | None = None, x_categoricals: list[str] | None = None, hidden_continuous_size: int = 8, hidden_continuous_sizes: dict[str, int] | None = None, embedding_sizes: dict[str, tuple[int, int]] | None = None, embedding_paddings: list[str] | None = None, embedding_labels: dict[str, ndarray] | None = None, learning_rate: float = 0.001, log_interval: int | float = -1, log_val_interval: int | float = None, log_gradient_flow: bool = False, reduce_on_plateau_patience: int = 1000, monotone_constraints: dict[str, int] | None = None, share_single_variable_networks: bool = False, causal_attention: bool = True, logging_metrics: ModuleList = None, mask_bias: float = -1000000000.0, **kwargs)

Temporal Fusion Transformer for forecasting timeseries.

Initialize via from_dataset() method if possible.

Implementation of Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting.

Enhancements compared to the original implementation:

• static variables can be continuous

• multiple categorical variables can be summarized with an EmbeddingBag

• variable encoder and decoder length by sample

• categorical embeddings are not transformed by variable selection network (because it is a redundant operation)

• variable dimension in variable selection network are scaled up via linear interpolation to reduce number of parameters

• non-linear variable processing in variable selection network can be shared among decoder and encoder (not shared by default)

• capabilities added through base model such as monotone constraints

Tune its hyperparameters with optimize_hyperparameters().

Parameters:

• hidden_size (int, default=16) – hidden size of network which is its main hyperparameter. Can range from 8 to 512.

• lstm_layers (int, default=1) – number of LSTM layers (2 is mostly optimal)

• dropout (float, default=0.1) – dropout rate

• output_size (int or list of int, default=7) – number of outputs (e.g. number of quantiles for QuantileLoss and one target or list of output sizes).

• loss (MultiHorizonMetric, default=QuantileLoss()) – loss function taking prediction and targets

• attention_head_size (int, default=4) – number of attention heads (4 is a good default)

• max_encoder_length (int, default=10) – length to encode, can be far longer than the decoder length but does not have to be

• static_categoricals (names of static categorical variables)

• static_reals (names of static continuous variables)

• time_varying_categoricals_encoder (names of categorical variables for encoder)

• time_varying_categoricals_decoder (names of categorical variables for decoder)

• time_varying_reals_encoder (names of continuous variables for encoder)

• time_varying_reals_decoder (names of continuous variables for decoder)

• categorical_groups (dictionary where values) – are list of categorical variables that are forming together a new categorical variable which is the key in the dictionary

• x_reals (order of continuous variables in tensor passed to forward function)

• x_categoricals (order of categorical variables in tensor passed to forward function)

• hidden_continuous_size (default for hidden size for processing continuous variables (similar to categorical) – embedding size)

• hidden_continuous_sizes (dictionary mapping continuous input indices to sizes for variable selection) – (fallback to hidden_continuous_size if index is not in dictionary)

• embedding_sizes (dictionary mapping (string) indices to tuple of number of categorical classes and) – embedding size

• embedding_paddings (list of indices for embeddings which transform the zero's embedding to a zero vector)

• embedding_labels (dictionary mapping (string) indices to list of categorical labels)

• learning_rate (learning rate)

• log_interval (log predictions every x batches, do not log if 0 or less, log interpretation if > 0. If < 1.0) – , will log multiple entries per batch. Defaults to -1.

• log_val_interval (frequency with which to log validation set metrics, defaults to log_interval)

• log_gradient_flow (if to log gradient flow, this takes time and should be only done to diagnose training) – failures

• (int) (reduce_on_plateau_patience)

• (Dict[str (monotone_constraints) – variables mapping position (e.g. "0" for first position) to constraint (-1 for negative and +1 for positive, larger numbers add more weight to the constraint vs. the loss but are usually not necessary). This constraint significantly slows down training. Defaults to {}.

• int]) (dictionary of monotonicity constraints for continuous decoder) – variables mapping position (e.g. "0" for first position) to constraint (-1 for negative and +1 for positive, larger numbers add more weight to the constraint vs. the loss but are usually not necessary). This constraint significantly slows down training. Defaults to {}.

• (bool) (causal_attention) – decoder. Defaults to False.

• (bool) – predictions. Defaults to True.

• (nn.ModuleList[LightningMetric]) (logging_metrics) – Defaults to nn.ModuleList([SMAPE(), MAE(), RMSE(), MAPE()]).

• mask_bias (float, optional) – Bias for the mask in ScaledDotProductAttention.forward, by default -1e9. Set to -float(“inf”) to allow mixed precision training.

• **kwargs (additional arguments to BaseModel.)

BaseModel for timeseries forecasting from which to inherit from

Parameters:

• log_interval (Union[int, float], optional) – Batches after which predictions are logged. If < 1.0, will log multiple entries per batch. Defaults to -1.

• log_val_interval (Union[int, float], optional) – batches after which predictions for validation are logged. Defaults to None/log_interval.

• learning_rate (float, optional) – Learning rate. Defaults to 1e-3.

• log_gradient_flow (bool) – If to log gradient flow, this takes time and should be only done to diagnose training failures. Defaults to False.

• loss (Metric, optional) – metric to optimize, can also be list of metrics. Defaults to SMAPE().

• logging_metrics (nn.ModuleList[MultiHorizonMetric]) – list of metrics that are logged during training. Defaults to [].

• reduce_on_plateau_patience (int) – patience after which learning rate is reduced by a factor of 10. Defaults to 1000

• reduce_on_plateau_reduction (float) – reduction in learning rate when encountering plateau. Defaults to 2.0.

• reduce_on_plateau_min_lr (float) – minimum learning rate for reduce on plateau learning rate scheduler. Defaults to 1e-5

• weight_decay (float) – weight decay. Defaults to 0.0.

• optimizer_params (Dict[str, Any]) – additional parameters for the optimizer. Defaults to {}.

• monotone_constraints (Dict[str, int]) – dictionary of monotonicity constraints for continuous decoder variables mapping position (e.g. "0" for first position) to constraint (-1 for negative and +1 for positive, larger numbers add more weight to the constraint vs. the loss but are usually not necessary). This constraint significantly slows down training. Defaults to {}.

• output_transformer (Callable) – transformer that takes network output and transforms it to prediction space. Defaults to None which is equivalent to lambda out: out["prediction"].

• optimizer (str) – Optimizer, “ranger”, “sgd”, “adam”, “adamw” or class name of optimizer in torch.optim or pytorch_optimizer. Alternatively, a class or function can be passed which takes parameters as first argument and a lr argument (optionally also weight_decay). Defaults to “adam”.

__init__(hidden_size: int = 16, lstm_layers: int = 1, dropout: float = 0.1, output_size: int | list[int] = 7, loss: MultiHorizonMetric = None, attention_head_size: int = 4, max_encoder_length: int = 10, static_categoricals: list[str] | None = None, static_reals: list[str] | None = None, time_varying_categoricals_encoder: list[str] | None = None, time_varying_categoricals_decoder: list[str] | None = None, categorical_groups: dict | list[str] | None = None, time_varying_reals_encoder: list[str] | None = None, time_varying_reals_decoder: list[str] | None = None, x_reals: list[str] | None = None, x_categoricals: list[str] | None = None, hidden_continuous_size: int = 8, hidden_continuous_sizes: dict[str, int] | None = None, embedding_sizes: dict[str, tuple[int, int]] | None = None, embedding_paddings: list[str] | None = None, embedding_labels: dict[str, ndarray] | None = None, learning_rate: float = 0.001, log_interval: int | float = -1, log_val_interval: int | float = None, log_gradient_flow: bool = False, reduce_on_plateau_patience: int = 1000, monotone_constraints: dict[str, int] | None = None, share_single_variable_networks: bool = False, causal_attention: bool = True, logging_metrics: ModuleList = None, mask_bias: float = -1000000000.0, **kwargs)

BaseModel for timeseries forecasting from which to inherit from

Parameters:

• log_interval (Union[int, float], optional) – Batches after which predictions are logged. If < 1.0, will log multiple entries per batch. Defaults to -1.

• log_val_interval (Union[int, float], optional) – batches after which predictions for validation are logged. Defaults to None/log_interval.

• learning_rate (float, optional) – Learning rate. Defaults to 1e-3.

• log_gradient_flow (bool) – If to log gradient flow, this takes time and should be only done to diagnose training failures. Defaults to False.

• loss (Metric, optional) – metric to optimize, can also be list of metrics. Defaults to SMAPE().

• logging_metrics (nn.ModuleList[MultiHorizonMetric]) – list of metrics that are logged during training. Defaults to [].

• reduce_on_plateau_patience (int) – patience after which learning rate is reduced by a factor of 10. Defaults to 1000

• reduce_on_plateau_reduction (float) – reduction in learning rate when encountering plateau. Defaults to 2.0.

• reduce_on_plateau_min_lr (float) – minimum learning rate for reduce on plateau learning rate scheduler. Defaults to 1e-5

• weight_decay (float) – weight decay. Defaults to 0.0.

• optimizer_params (Dict[str, Any]) – additional parameters for the optimizer. Defaults to {}.

• monotone_constraints (Dict[str, int]) – dictionary of monotonicity constraints for continuous decoder variables mapping position (e.g. "0" for first position) to constraint (-1 for negative and +1 for positive, larger numbers add more weight to the constraint vs. the loss but are usually not necessary). This constraint significantly slows down training. Defaults to {}.

• output_transformer (Callable) – transformer that takes network output and transforms it to prediction space. Defaults to None which is equivalent to lambda out: out["prediction"].

• optimizer (str) – Optimizer, “ranger”, “sgd”, “adam”, “adamw” or class name of optimizer in torch.optim or pytorch_optimizer. Alternatively, a class or function can be passed which takes parameters as first argument and a lr argument (optionally also weight_decay). Defaults to “adam”.

Methods

__call__(*args, **kwargs)	Call self as a function.
__delattr__(name)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattr__(name)
__getattribute__(name, /)	Return getattr(self, name).
__getstate__()	Helper for pickle.
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(*args, **kwargs)
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value)	Implement setattr(self, name, value).
__setstate__(state)
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_apply(fn[, recurse])
_apply_batch_transfer_handler(batch[, ...])
_call_batch_hook(hook_name, *args)
_call_impl(*args, **kwargs)
_get_backward_hooks()	Return the backward hooks for use in the call function.
_get_backward_pre_hooks()
_get_name()
_load_from_state_dict(state_dict, prefix, ...)	Copy parameters and buffers from state_dict into only this module, but not its descendants.
_log_dict_through_fabric(dictionary[, logger])
_log_interpretation(out)
_logger_supports(method)	Whether logger supports method.
_maybe_warn_non_full_backward_hook(inputs, ...)
_named_members(get_members_fn[, prefix, ...])	Help yield various names + members of modules.
_on_before_batch_transfer(batch[, ...])
_pkg()	Package containing the model.
_register_load_state_dict_pre_hook(hook[, ...])	See register_load_state_dict_pre_hook() for details.
_register_state_dict_hook(hook)	Register a post-hook for the state_dict() method.
_replicate_for_data_parallel()
_save_to_state_dict(destination, prefix, ...)	Save module state to the destination dictionary.
_set_hparams(hp)
_slow_forward(*input, **kwargs)
_to_hparams_dict(hp)
_verify_is_manual_optimization(fn_name)
_wrapped_call_impl(*args, **kwargs)
add_module(name, module)	Add a child module to the current module.
all_gather(data[, group, sync_grads])	Gather tensors or collections of tensors from multiple processes.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
backward(loss, *args, **kwargs)	Called to perform backward on the loss returned in training_step().
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
calculate_prediction_actual_by_variable(x, ...)	Calculate predictions and actuals by variable averaged by bins bins spanning from -std to +std
children()	Return an iterator over immediate children modules.
clip_gradients(optimizer[, ...])	Handles gradient clipping internally.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
configure_callbacks()	Configure model-specific callbacks.
configure_gradient_clipping(optimizer[, ...])	Perform gradient clipping for the optimizer parameters.
configure_model()	Hook to create modules in a strategy and precision aware context.
configure_optimizers()	Configure optimizers.
configure_sharded_model()	Deprecated.
cpu()	See torch.nn.Module.cpu().
create_log(x, y, out, batch_idx, **kwargs)	Create the log used in the training and validation step.
cuda([device])	Moves all model parameters and buffers to the GPU.
deduce_default_output_parameters(dataset, kwargs)	Deduce default parameters for output for from_dataset() method.
double()	See torch.nn.Module.double().
eval()	Set the module in evaluation mode.
expand_static_context(context, timesteps)	add time dimension to static context
extra_repr()	Return extra information about parameters for representation/logging.
extract_features(x[, embeddings, period])	Extract features
float()	See torch.nn.Module.float().
forward(x)	input dimensions: n_samples x time x variables
freeze()	Freeze all params for inference.
from_dataset(dataset[, ...])	Create model from dataset.
get_attention_mask(encoder_lengths, ...)	Returns causal mask to apply for self-attention layer.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	See torch.nn.Module.half().
interpret_output(out[, reduction, ...])	interpret output of model
ipu([device])	Move all model parameters and buffers to the IPU.
load_from_checkpoint(checkpoint_path[, ...])	Primary way of loading a model from a checkpoint.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
log(*args, **kwargs)	See lightning.pytorch.core.lightning.LightningModule.log().
log_dict(dictionary[, prog_bar, logger, ...])	Log a dictionary of values at once.
log_embeddings()	Log embeddings to tensorboard
log_gradient_flow(named_parameters)	log distribution of gradients to identify exploding / vanishing gradients
log_interpretation(outputs)	Log interpretation metrics to tensorboard.
log_metrics(x, y, out[, prediction_kwargs])	Log metrics every training/validation step.
log_prediction(x, out, batch_idx, **kwargs)	Log metrics every training/validation step.
lr_scheduler_step(scheduler, metric)	Override this method to adjust the default way the Trainer calls each scheduler.
lr_schedulers()	Returns the learning rate scheduler(s) that are being used during training.
manual_backward(loss, *args, **kwargs)	Call this directly from your training_step() when doing optimizations manually.
modules([remove_duplicate])	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
on_after_backward()	Log gradient flow for debugging.
on_after_batch_transfer(batch, dataloader_idx)	Override to alter or apply batch augmentations to your batch after it is transferred to the device.
on_before_backward(loss)	Called before loss.backward().
on_before_batch_transfer(batch, dataloader_idx)	Override to alter or apply batch augmentations to your batch before it is transferred to the device.
on_before_optimizer_step(optimizer)	Called before optimizer.step().
on_before_zero_grad(optimizer)	Called after training_step() and before optimizer.zero_grad().
on_epoch_end(outputs)	run at epoch end for training or validation
on_fit_end()	Called at the very end of fit.
on_fit_start()	Called at the very beginning of fit.
on_load_checkpoint(checkpoint)	Called by Lightning to restore your model.
on_predict_batch_end(outputs, batch, batch_idx)	Called in the predict loop after the batch.
on_predict_batch_start(batch, batch_idx[, ...])	Called in the predict loop before anything happens for that batch.
on_predict_end()	Called at the end of predicting.
on_predict_epoch_end()	Called at the end of predicting.
on_predict_epoch_start()	Called at the beginning of predicting.
on_predict_model_eval()	Called when the predict loop starts.
on_predict_start()	Called at the beginning of predicting.
on_save_checkpoint(checkpoint)	Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.
on_test_batch_end(outputs, batch, batch_idx)	Called in the test loop after the batch.
on_test_batch_start(batch, batch_idx[, ...])	Called in the test loop before anything happens for that batch.
on_test_end()	Called at the end of testing.
on_test_epoch_end()	Called in the test loop at the very end of the epoch.
on_test_epoch_start()	Called in the test loop at the very beginning of the epoch.
on_test_model_eval()	Called when the test loop starts.
on_test_model_train()	Called when the test loop ends.
on_test_start()	Called at the beginning of testing.
on_train_batch_end(outputs, batch, batch_idx)	Called in the training loop after the batch.
on_train_batch_start(batch, batch_idx)	Called in the training loop before anything happens for that batch.
on_train_end()	Called at the end of training before logger experiment is closed.
on_train_epoch_end()	Called in the training loop at the very end of the epoch.
on_train_epoch_start()	Called in the training loop at the very beginning of the epoch.
on_train_start()	Called at the beginning of training after sanity check.
on_validation_batch_end(outputs, batch, ...)	Called in the validation loop after the batch.
on_validation_batch_start(batch, batch_idx)	Called in the validation loop before anything happens for that batch.
on_validation_end()	Called at the end of validation.
on_validation_epoch_end()	Called in the validation loop at the very end of the epoch.
on_validation_epoch_start()	Called in the validation loop at the very beginning of the epoch.
on_validation_model_eval()	Called when the validation loop starts.
on_validation_model_train()	Called when the validation loop ends.
on_validation_model_zero_grad()	Called by the training loop to release gradients before entering the validation loop.
on_validation_start()	Called at the beginning of validation.
optimizer_step(epoch, batch_idx, optimizer)	Override this method to adjust the default way the Trainer calls the optimizer.
optimizer_zero_grad(epoch, batch_idx, optimizer)	Override this method to change the default behaviour of optimizer.zero_grad().
optimizers([use_pl_optimizer])	Returns the optimizer(s) that are being used during training.
parameters([recurse])	Return an iterator over module parameters.
plot_interpretation(interpretation)	Make figures that interpret model.
plot_prediction(x, out, idx[, ...])	Plot actuals vs prediction and attention
plot_prediction_actual_by_variable(data[, ...])	Plot predicions and actual averages by variables
predict(data[, mode, return_index, ...])	Run inference / prediction.
predict_dataloader()	An iterable or collection of iterables specifying prediction samples.
predict_dependency(data, variable, values[, ...])	Predict partial dependency.
predict_step(batch, batch_idx)	Step function called during predict().
prepare_data()	Use this to download and prepare data.
print(*args, **kwargs)	Prints only from process 0.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
remove_ignored_hparams(ignore_list)	Remove ignored hyperparameters from the stored state.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
save_hyperparameters(*args[, ignore, frame, ...])	Save arguments to hparams attribute.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
setup(stage)	Called at the beginning of fit (train + validate), validate, test, or predict.
share_memory()	See torch.Tensor.share_memory_().
size()	get number of parameters in model
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
step(x, y, batch_idx, **kwargs)	Run for each train/val step.
teardown(stage)	Called at the end of fit (train + validate), validate, test, or predict.
test_dataloader()	An iterable or collection of iterables specifying test samples.
test_step(batch, batch_idx)	Operates on a single batch of data from the test set.
to(*args, **kwargs)	See torch.nn.Module.to().
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
to_network_output(**results)	Convert output into a named (and immutable) tuple.
to_onnx([file_path, input_sample])	Saves the model in ONNX format.
to_prediction(out[, use_metric])	Convert output to prediction using the loss metric.
to_quantiles(out[, use_metric])	Convert output to quantiles using the loss metric.
to_tensorrt([file_path, input_sample, ir, ...])	Export the model to ScriptModule or GraphModule using TensorRT compile backend.
to_torchscript([file_path, method, ...])	By default compiles the whole model to a torch.jit.ScriptModule.
toggle_optimizer(optimizer)	Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.
toggled_optimizer(optimizer)	Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.
train([mode])	Set the module in training mode.
train_dataloader()	An iterable or collection of iterables specifying training samples.
training_step(batch, batch_idx)	Train on batch.
transfer_batch_to_device(batch, device, ...)	Override this hook if your DataLoader returns tensors wrapped in a custom data structure.
transform_output(prediction, target_scale[, ...])	Extract prediction from network output and rescale it to real space / de-normalize it.
type(dst_type)	See torch.nn.Module.type().
unfreeze()	Unfreeze all parameters for training.
untoggle_optimizer(optimizer)	Resets the state of required gradients that were toggled with toggle_optimizer().
val_dataloader()	An iterable or collection of iterables specifying validation samples.
validation_step(batch, batch_idx)	Operates on a single batch of data from the validation set.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

CHECKPOINT_HYPER_PARAMS_KEY
CHECKPOINT_HYPER_PARAMS_NAME
CHECKPOINT_HYPER_PARAMS_SPECIAL_KEY
CHECKPOINT_HYPER_PARAMS_TYPE
T_destination
__annotations__
__dict__
__doc__
__jit_unused_properties__
__module__
__weakref__	list of weak references to the object
_compiled_call_impl
_jit_is_scripting
_version	This allows better BC support for load_state_dict().
automatic_optimization	If set to False you are responsible for calling .backward(), .step(), .zero_grad().
call_super_init
categorical_groups_mapping	Mapping of categorical variables to categorical groups
categoricals	List of all categorical variables in model
current_epoch	The current epoch in the Trainer, or 0 if not attached.
current_stage	Available inside lightning loops.
decoder_variables	List of all decoder variables in model (excluding static variables)
device
device_mesh	Strategies like ModelParallelStrategy will create a device mesh that can be accessed in the configure_model() hook to parallelize the LightningModule.
dtype
dump_patches
encoder_variables	List of all encoder variables in model (excluding static variables)
example_input_array	The example input array is a specification of what the module can consume in the forward() method.
fabric
global_rank	The index of the current process across all nodes and devices.
global_step	Total training batches seen across all epochs.
hparams	The collection of hyperparameters saved with save_hyperparameters().
hparams_initial	The collection of hyperparameters saved with save_hyperparameters().
local_rank	The index of the current process within a single node.
log_interval	Log interval depending if training or validating
logger	Reference to the logger object in the Trainer.
loggers	Reference to the list of loggers in the Trainer.
n_targets	Number of targets to forecast.
on_gpu	Returns True if this model is currently located on a GPU.
predicting
reals	List of all continuous variables in model
static_variables	List of all static variables in model
strict_loading	Determines how Lightning loads this model using .load_state_dict(..., strict=model.strict_loading).
target_names	List of targets that are predicted.
target_positions	Positions of target variable(s) in covariates.
trainer
training
_parameters
_buffers
_non_persistent_buffers_set
_backward_pre_hooks
_backward_hooks
_is_full_backward_hook
_forward_hooks
_forward_hooks_with_kwargs
_forward_hooks_always_called
_forward_pre_hooks
_forward_pre_hooks_with_kwargs
_state_dict_hooks
_load_state_dict_pre_hooks
_state_dict_pre_hooks
_load_state_dict_post_hooks
_modules