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Save and restore supported cuda.ml models

Source: R/model.R
cuda_ml_serialize.Rd

cuda_ml_serialize() saves the explicit state of a fitted cuda.ml model. cuda_ml_unserialize() restores that state as a fitted model.

Usage

cuda_ml_serialize(model, connection = NULL, ...)

cuda_ml_unserialize(
  connection,
  ...,
  device = NULL,
  device_id = NULL,
  layout = NULL,
  precision = NULL,
  default_chunk_size = NULL,
  align_bytes = NULL
)

Arguments

model

The model object.

connection

For cuda_ml_serialize(), a file path, an open connection, or NULL; a file path writes a gzip-compressed state and NULL returns the state as a raw vector. For cuda_ml_unserialize(), a file path, an open connection, or a raw vector.

...

Additional arguments passed to base::serialize() or base::unserialize().

device, device_id, layout, precision, default_chunk_size, align_bytes

Named nvForest inference options. They are supported only for nvForest and random-forest states. When device is omitted, those states restore for GPU inference. When precision is omitted, the saved prediction precision is used. The remaining omitted options use nvForest defaults.

Value

cuda_ml_serialize() returns NULL when writing to a file or connection and otherwise returns a raw vector. cuda_ml_unserialize() returns the restored fitted model.

Supported models

Explicit state is supported for:

  • OLS, ridge, lasso, elastic-net, and SGD linear models;

  • logistic and multinomial regression;

  • PCA;

  • binary and one-vs-rest SVC models and SVR models;

  • UMAP;

  • random forests and other nvForest-backed models.

KNN and TSVD fits are not currently supported. The pinned KNN API does not expose portable approximate-index state, and the current TSVD binding retains native transform parameters that cuda.ml does not reconstruct.

Deployment

cuda.ml validates the model state and required backend before loading it. Prepare the backend in the target process with cuda_ml_install() for GPU operation or cuda_ml_install(device = "cpu") for CPU-only nvForest inference.

Random-forest and nvForest states contain device-neutral Treelite model bytes. They retain prediction precision, class labels, preprocessing, and model semantics, but not the inference device, device identifier, tree layout, chunk size, or memory alignment. Select those settings while restoring; omitting device selects GPU inference.

bundle::bundle() stores the same explicit state. For an nvForest-backed model, the bundle also stores its chosen deployment device separately from the device-neutral state. A bundle is not required for deployment; cuda_ml_serialize() returns the complete state artifact directly.

See also

serialize, unserialize, and bundle