ggml_opt_init_for_fit: Initialize optimizer context for R-side epoch loop

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Initialize optimizer context for R-side epoch loop

Description

Returns a list with 'opt_ctx' and 'lr_ud' (learning rate userdata pointer). Use 'ggml_opt_set_lr()' to update LR between epochs. The optimizer state (momentum) is preserved across epochs.

Usage

ggml_opt_init_for_fit(
  sched,
  loss_type,
  optimizer = ggml_opt_optimizer_type_adamw(),
  opt_period = 1L,
  ctx_compute = NULL,
  inputs = NULL,
  outputs = NULL
)

Arguments

sched	Backend scheduler
loss_type	Loss type constant
optimizer	Optimizer type constant
opt_period	Gradient accumulation period
ctx_compute	Compute context (for static graphs)
inputs	Input tensor (for static graphs)
outputs	Output tensor (for static graphs)

Value

List with elements 'opt_ctx' and 'lr_ud'

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_dataset_weights(), ggml_opt_default_params(), ggml_opt_epoch(), ggml_opt_eval(), ggml_opt_fit(), ggml_opt_free(), ggml_opt_get_lr(), ggml_opt_grad_acc(), ggml_opt_init(), 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_loss_type_weighted_mse(), 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()

ggmlR documentation built on July 14, 2026, 1:08 a.m.