mlr_callback_set.checkpoint function

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.

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)

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

• list("CallbackSetCheckpoint$new()")

• list("CallbackSetCheckpoint$on_epoch_end()")

• list("CallbackSetCheckpoint$on_batch_end()")

• list("CallbackSetCheckpoint$on_exit()")

• list("CallbackSetCheckpoint$clone()")

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"`.
  

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()

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()

Method on_exit()

Saves the learner.

Usage

CallbackSetCheckpoint$on_exit()

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.