ggml_fit_opt: Fit model with R-side epoch loop and callbacks

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Fit model with R-side epoch loop and callbacks

Description

Trains a model epoch by epoch in R, allowing callbacks for early stopping and learning rate scheduling. Optimizer state (momentum) is preserved across all epochs.

Usage

ggml_fit_opt(
  sched,
  ctx_compute,
  inputs,
  outputs,
  dataset,
  loss_type = ggml_opt_loss_type_mse(),
  optimizer = ggml_opt_optimizer_type_adamw(),
  nepoch = 10L,
  nbatch_logical = 32L,
  val_split = 0,
  shuffle = TRUE,
  shuffle_all = shuffle,
  callbacks = list(),
  silent = FALSE,
  loss_mask_ne0 = NULL,
  weight_decay = 0
)

Arguments

sched	Backend scheduler
ctx_compute	Compute context (for temporary tensors)
inputs	Input tensor with shape [ne_datapoint, batch_size]
outputs	Output tensor with shape [ne_label, batch_size]
dataset	Dataset created with 'ggml_opt_dataset_init()'
loss_type	Loss type (default: MSE)
optimizer	Optimizer type (default: AdamW)
nepoch	Number of epochs
nbatch_logical	Logical batch size (for gradient accumulation)
val_split	Fraction of data for validation (0.0 to 1.0)
shuffle	Shuffle the training portion each epoch (default TRUE). Set to FALSE for time series or to make a run exactly reproducible.
shuffle_all	Shuffle the whole dataset once before the train/validation split is taken, so the validation portion is a random sample rather than the tail of the input. Defaults to shuffle. Callers that append an explicit validation set to the data pass FALSE, since there the split is positional and must be preserved.
callbacks	List of callback lists. Each element may have 'on_epoch_begin(epoch, logs, state)' and/or 'on_epoch_end(epoch, logs, state)'. Built-in factories: 'ggml_callback_early_stopping()', 'ggml_schedule_step_decay()', 'ggml_schedule_cosine_decay()', 'ggml_schedule_reduce_on_plateau()'. 'state' is a mutable environment with fields: 'stop' (set TRUE to stop training), 'lr_ud', 'nepoch'.
silent	Whether to suppress per-epoch progress output
loss_mask_ne0	Width of the weighted-MSE weight tensor: NULL or 1 for per-datapoint weights, or the output width for a per-output mask. Ignored by every other loss type.
weight_decay	Decoupled weight decay in [0, 1], as for ggml_opt_init_for_fit; 0 turns it off.

Value

Data frame with columns epoch, train_loss, train_accuracy, val_loss, val_accuracy

See Also

Other optimization: ggml_fit_opt_gen(), ggml_fit_opt_multi(), ggml_opt_alloc(), ggml_opt_context_optimizer_type(), ggml_opt_dataset_data(), ggml_opt_dataset_free(), ggml_opt_dataset_get_batch(), ggml_opt_dataset_get_batch_head(), ggml_opt_dataset_init(), ggml_opt_dataset_labels(), ggml_opt_dataset_ndata(), ggml_opt_dataset_shuffle(), ggml_opt_dataset_weights(), ggml_opt_default_params(), ggml_opt_epoch(), ggml_opt_eval(), ggml_opt_fit(), ggml_opt_fit_multi(), ggml_opt_free(), ggml_opt_get_lr(), ggml_opt_grad_acc(), ggml_opt_init(), ggml_opt_init_for_fit(), ggml_opt_init_for_fit_multi(), ggml_opt_inputs(), ggml_opt_labels(), ggml_opt_loss(), ggml_opt_loss_i(), ggml_opt_loss_type_binary_cross_entropy(), ggml_opt_loss_type_cross_entropy(), ggml_opt_loss_type_huber(), ggml_opt_loss_type_mae(), ggml_opt_loss_type_mean(), ggml_opt_loss_type_mse(), ggml_opt_loss_type_sum(), ggml_opt_loss_type_weighted_mse(), ggml_opt_n_loss(), ggml_opt_ncorrect(), ggml_opt_optimizer_name(), ggml_opt_optimizer_type_adamw(), ggml_opt_optimizer_type_sgd(), ggml_opt_outputs(), ggml_opt_outputs_i(), ggml_opt_pred(), ggml_opt_prepare_alloc(), ggml_opt_reset(), ggml_opt_result_accuracy(), ggml_opt_result_free(), ggml_opt_result_init(), ggml_opt_result_loss(), ggml_opt_result_loss_i(), ggml_opt_result_n_loss(), ggml_opt_result_ndata(), ggml_opt_result_pred(), ggml_opt_result_reset(), ggml_opt_set_lr(), ggml_opt_static_graphs(), ggml_snapshot_free(), ggml_trainer()

Examples

if (FALSE) {
history <- ggml_fit_opt(sched, ctx_compute, inputs, outputs, dataset,
  nepoch = 50, val_split = 0.2,
  callbacks = list(
    ggml_callback_early_stopping(monitor = "val_loss", patience = 5),
    ggml_schedule_cosine_decay()
  ))
}

ggmlR documentation built on Oct. 23, 2026, 1:07 a.m.