## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(eval = TRUE)

## -----------------------------------------------------------------------------
library(ggmlR)

## -----------------------------------------------------------------------------
if (ggml_vulkan_available()) {
  cat("Vulkan is available\n")
  ggml_vulkan_status()              # print device list and properties
} else {
  cat("No Vulkan GPU — running on CPU\n")
}

n <- ggml_vulkan_device_count()
cat("Vulkan device count:", n, "\n")

## -----------------------------------------------------------------------------
# Low-level device registry (all backends including CPU)
ggml_backend_load_all()

n_dev <- ggml_backend_dev_count()
for (i in seq_len(n_dev)) {
  dev  <- ggml_backend_dev_get(i - 1L)   # 0-based
  name <- ggml_backend_dev_name(dev)
  desc <- ggml_backend_dev_description(dev)
  mem  <- ggml_backend_dev_memory(dev)
  cat(sprintf("[%d] %s — %s\n", i, name, desc))
  cat(sprintf("    %.1f GB free / %.1f GB total\n",
              mem["free"] / 1e9, mem["total"] / 1e9))
}

## -----------------------------------------------------------------------------
# Select GPU (falls back to CPU if unavailable)
device <- tryCatch({
  ag_device("gpu")
  "gpu"
}, error = function(e) {
  message("GPU not available, using CPU")
  "cpu"
})

cat("Active device:", device, "\n")

## -----------------------------------------------------------------------------
if (device == "gpu") {
  ag_dtype("f16")     # half-precision on Vulkan GPU
  # ag_dtype("bf16") # bfloat16 — falls back to f16 on Vulkan automatically
} else {
  ag_dtype("f32")     # full precision on CPU
}

cat("Active dtype:", ag_default_dtype(), "\n")

## -----------------------------------------------------------------------------
if (ggml_vulkan_available()) {
  mem <- ggml_vulkan_device_memory(0L)
  cat(sprintf("GPU memory: %.1f MB free / %.1f MB total\n",
              mem$free / 1e6, mem$total / 1e6))
}

## -----------------------------------------------------------------------------
n_gpu <- ggml_vulkan_device_count()
cat(sprintf("Using %d GPU(s)\n", n_gpu))

# dp_train handles multi-GPU internally — see vignette("data-parallel-training")

## -----------------------------------------------------------------------------
data(iris)
x_train <- scale(as.matrix(iris[, 1:4]))
y_train <- model.matrix(~ Species - 1, iris)

model <- ggml_model_sequential() |>
  ggml_layer_dense(64L, activation = "relu", input_shape = 4L) |>
  ggml_layer_dense(3L,  activation = "softmax") |>
  ggml_compile(optimizer = "adam", loss = "categorical_crossentropy")

# Training runs on GPU if Vulkan is available
model <- ggml_fit(model, x_train, y_train, epochs = 50L,
                  batch_size = 32L, verbose = 0L)

## ----eval = FALSE-------------------------------------------------------------
# # Weights loaded to GPU once at load time
# model_onnx <- ggml_onnx_load("model.onnx", backend = "vulkan")
# 
# # Repeated inference — no weight re-transfer
# for (i in seq_len(100L)) {
#   out <- ggml_onnx_run(model_onnx, list(input = batch[[i]]))
# }

## -----------------------------------------------------------------------------
cat(ggml_version(), "\n")
ggml_vulkan_status()   # shows "Vulkan not available" if not compiled in

