Checkpoint Callback

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.

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()

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.

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)
#> [1] "network1.pt"   "network2.pt"   "network3.pt"   "optimizer1.pt"
#> [5] "optimizer2.pt" "optimizer3.pt"