Exports the estimator component of an mlr3 learner to ONNX format for
cross-language deployment. Supports xgboost and glmnet learners.
Usage
export_onnx(learner, path, task, export_preprocessing = TRUE)
Arguments
- learner
A trained mlr3 Learner or GraphLearner
- path
File path for the ONNX model (with .onnx extension)
- task
The mlr3 Task used for training (for metadata)
- export_preprocessing
Logical, whether to also export preprocessing manifest
Value
List with export status and paths to exported files
Details
ONNX export is currently supported for:
- xgboost: via native xgboost save + Python conversion (most reliable)
- glmnet: via coefficient extraction (linear models)
For xgboost, this function exports the native .xgb format. A separate Python
script can convert to ONNX. For glmnet, coefficients are exported as JSON.
For other learners, only the preprocessing manifest is exported. Use vetiver
for R-based deployment of these models.
Examples
if (FALSE) { # \dontrun{
# Train xgboost model
task <- tsk("iris")
learner <- lrn("classif.xgboost", nrounds = 50)
learner$train(task)
# Export to ONNX-compatible format
export_onnx(learner, "model.onnx", task)
} # }