TorchCallback: Torch Callback

In mlr3torch: Deep Learning with 'mlr3'

Torch Callback

Description

This wraps a CallbackSet and annotates it with metadata, most importantly a ParamSet. The callback is created for the given parameter values by calling the ⁠$generate()⁠ method.

This class is usually used to configure the callback of a torch learner, e.g. when constructing a learner of in a ModelDescriptor.

For a list of available callbacks, see mlr3torch_callbacks. To conveniently retrieve a TorchCallback, use t_clbk().

Parameters

Defined by the constructor argument param_set. If no parameter set is provided during construction, the parameter set is constructed by creating a parameter for each argument of the wrapped callback, where the parameters are then of type ParamUty.

Super class

TorchDescriptor -> TorchCallback

Active bindings

weight

(numeric(1) or NULL)
Overwrites the ⁠$weight⁠ of the generated CallbackSet, see its section Ordering.

Methods

Public methods

• TorchCallback$new()

• TorchCallback$generate()

• TorchCallback$clone()

Inherited methods

• TorchDescriptor$help()
• TorchDescriptor$print()

---

TorchCallback$new()

Creates a new instance of this R6 class.

Usage

TorchCallback$new(
  callback_generator,
  param_set = NULL,
  id = NULL,
  label = NULL,
  packages = NULL,
  man = NULL,
  additional_args = NULL,
  weight = NULL
)

Arguments

callback_generator

(R6ClassGenerator)
The class generator for the callback that is being wrapped.

param_set

(ParamSet or NULL)
The parameter set. If NULL (default) it is inferred from callback_generator. Whether inferred or given, it must not contain a weight: that is the callback's ordering weight, which is set via the weight argument.

id

(character(1))
The id for of the new object.

label

(character(1))
Label for the new instance.

packages

(character())
The R packages this object depends on.

man

(character(1))
String in the format ⁠[pkg]::[topic]⁠ pointing to a manual page for this object. The referenced help package can be opened via method ⁠$help()⁠.

additional_args

(any)
Additional arguments if necessary. For learning rate schedulers, this is the torch::LRScheduler.

weight

(numeric(1) or NULL)
Overwrites the ⁠$weight⁠ of the generated CallbackSet, see its section Ordering. If NULL (default), the callback's own weight is kept.

---

TorchCallback$generate()

Generates the CallbackSet, applying ⁠$weight⁠ if it is set.

Usage

TorchCallback$generate()

---

TorchCallback$clone()

The objects of this class are cloneable with this method.

Usage

TorchCallback$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other Callback: as_torch_callback(), as_torch_callbacks(), callback_set(), mlr3torch_callbacks, mlr_callback_set, mlr_callback_set.checkpoint, mlr_callback_set.progress, mlr_callback_set.tb, mlr_callback_set.unfreeze, mlr_context_torch, t_clbk(), torch_callback()

Other Torch Descriptor: TorchDescriptor, TorchLoss, TorchOptimizer, as_torch_callbacks(), as_torch_loss(), as_torch_optimizer(), mlr3torch_losses, mlr3torch_optimizers, t_clbk(), t_loss(), t_opt()

Examples

# Create a new torch callback from an existing callback set
torch_callback = TorchCallback$new(CallbackSetCheckpoint)
# The parameters are inferred
torch_callback$param_set

# Retrieve a torch callback from the dictionary
torch_callback = t_clbk("checkpoint",
  path = tempfile(), freq = 1
)
torch_callback
torch_callback$label
torch_callback$id

# open the help page of the wrapped callback set
# torch_callback$help()

# Create the callback set
callback = torch_callback$generate()
callback
# is the same as
CallbackSetCheckpoint$new(
  path = tempfile(), freq = 1
)

# Use in a learner
learner = lrn("regr.mlp", callbacks = t_clbk("checkpoint"))
# the parameters of the callback are added to the learner's parameter set
learner$param_set

mlr3torch documentation built on Oct. 30, 2026, 5:10 p.m.