mlr_callback_set.checkpoint: Checkpoint Callback

In mlr3torch: Deep Learning with 'mlr3'

Checkpoint Callback

Description

Saves the optimizer and network states during training. The final network and optimizer are always stored.

Details

Saving the learner itself in the callback with a trained model is impossible, as the model slot is set after the last callback step is executed.

Super class

mlr3torch::CallbackSet -> CallbackSetCheckpoint

Methods

Public methods

• CallbackSetCheckpoint$new()

• CallbackSetCheckpoint$on_epoch_end()

• CallbackSetCheckpoint$on_batch_end()

• CallbackSetCheckpoint$on_exit()

• CallbackSetCheckpoint$clone()

Inherited methods

• mlr3torch::CallbackSet$load_state_dict()
• mlr3torch::CallbackSet$print()
• mlr3torch::CallbackSet$state_dict()

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Method new()

Creates a new instance of this R6 class.

Usage

CallbackSetCheckpoint$new(path, freq, freq_type = "epoch")

Arguments

path

(character(1))
The path to a folder where the models are saved.

freq

(integer(1))
The frequency how often the model is saved. Frequency is either per step or epoch, which can be configured through the freq_type parameter.

freq_type

(character(1))
Can be be either "epoch" (default) or "step".

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Method on_epoch_end()

Saves the network and optimizer state dict. Does nothing if freq_type or freq are not met.

Usage

CallbackSetCheckpoint$on_epoch_end()

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Method on_batch_end()

Saves the selected objects defined in save. Does nothing if freq_type or freq are not met.

Usage

CallbackSetCheckpoint$on_batch_end()

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Method on_exit()

Saves the learner.

Usage

CallbackSetCheckpoint$on_exit()

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Method clone()

The objects of this class are cloneable with this method.

Usage

CallbackSetCheckpoint$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

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

Examples

cb = t_clbk("checkpoint", freq = 1)
task = tsk("iris")

pth = tempfile()
learner = lrn("classif.mlp", epochs = 3, batch_size = 1, callbacks = cb)
learner$param_set$set_values(cb.checkpoint.path = pth)

learner$train(task)

list.files(pth)

mlr3torch documentation built on April 4, 2025, 3:03 a.m.