ggmlR (version 0.6.1)

ggml_opt_fit: Fit model to dataset

Description

High-level function to train a model on a dataset. This is the recommended way to train models.

Usage

ggml_opt_fit(
  sched,
  ctx_compute,
  inputs,
  outputs,
  dataset,
  loss_type = ggml_opt_loss_type_mse(),
  optimizer = ggml_opt_optimizer_type_adamw(),
  nepoch = 1,
  nbatch_logical = 32,
  val_split = 0,
  silent = FALSE
)

Value

NULL invisibly

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)

silent

Whether to suppress progress output

See Also

Other optimization: ggml_fit_opt(), ggml_opt_alloc(), ggml_opt_context_optimizer_type(), ggml_opt_dataset_data(), ggml_opt_dataset_free(), ggml_opt_dataset_get_batch(), ggml_opt_dataset_init(), ggml_opt_dataset_labels(), ggml_opt_dataset_ndata(), ggml_opt_dataset_shuffle(), ggml_opt_default_params(), ggml_opt_epoch(), ggml_opt_eval(), ggml_opt_free(), ggml_opt_get_lr(), ggml_opt_grad_acc(), ggml_opt_init(), ggml_opt_init_for_fit(), ggml_opt_inputs(), ggml_opt_labels(), ggml_opt_loss(), ggml_opt_loss_type_cross_entropy(), ggml_opt_loss_type_mean(), ggml_opt_loss_type_mse(), ggml_opt_loss_type_sum(), ggml_opt_ncorrect(), ggml_opt_optimizer_name(), ggml_opt_optimizer_type_adamw(), ggml_opt_optimizer_type_sgd(), ggml_opt_outputs(), 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_ndata(), ggml_opt_result_pred(), ggml_opt_result_reset(), ggml_opt_set_lr(), ggml_opt_static_graphs()

Examples

# Full training requires building a computation graph
# See package vignettes for complete examples
if (FALSE) {
cpu <- ggml_backend_cpu_init()
sched <- ggml_backend_sched_new(list(cpu))
dataset <- ggml_opt_dataset_init(GGML_TYPE_F32, GGML_TYPE_F32, 10, 1, 1000)
# ... build model graph with ctx_compute, inputs, outputs ...
ggml_opt_fit(sched, ctx_compute, inputs, outputs, dataset,
             nepoch = 10, val_split = 0.1)
ggml_opt_dataset_free(dataset)
ggml_backend_sched_free(sched)
ggml_backend_free(cpu)
}