tidymodels / tidymodels/probably
Error saving int_conformal_split() output to disk
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Description
The problem
I have trained an xgboost model using {tidymodels} and want to generate prediction intervals using int_conformal_split() to incorporate into a {vetiver} model API. I can do this successfully and generate predictions in the current R session. However if I try to save the output to disk or pin it to cloud storage with {pins}, I get an error every time I try to generate a prediction.
I presume this is an issue related to however {probably} is serializing the xgboost model object. I attempt to bundle() it first. When I do this with the fit workflow object it works correctly, but the same approach does not work with int_conformal_split().
Reproducible example
# Load required libraries
library(tidyverse)
library(tidymodels)
library(probably)
#>
#> Attaching package: 'probably'
#> The following objects are masked from 'package:base':
#>
#> as.factor, as.ordered
library(bundle)
# Prepare the data
data("penguins", package = "datasets")
penguins <- penguins |>
drop_na() |>
mutate(species = as.factor(species))
# Split the data into training and testing sets
set.seed(123)
penguins_split <- initial_split(penguins, prop = 0.8)
penguins_train <- training(penguins_split)
penguins_test <- testing(penguins_split)
# Create a recipe
penguins_rec <- recipe(body_mass ~ species + bill_len + bill_dep + flipper_len, data = penguins_train) |>
step_dummy(all_nominal_predictors()) |>
step_impute_mean(all_numeric_predictors())
# Specify the XGBoost model
xgb_spec <- boost_tree(
trees = 1000,
tree_depth = 3,
learn_rate = 0.1,
loss_reduction = 0.01,
min_n = 5
) |>
set_engine("xgboost") |>
set_mode("regression")
# Create a workflow
xgb_wf <- workflow() |>
add_recipe(penguins_rec) |>
add_model(xgb_spec)
# Fit the model
xgb_fit <- fit(xgb_wf, data = penguins_train)
# save the model to disk and reload it to generate predictions
temp_model_path <- tempfile(fileext = ".rds")
xgb_fit |>
bundle() |>
write_rds(file = temp_model_path)
xgb_fit_loaded <- read_rds(temp_model_path)
xgb_fit_loaded |>
unbundle() |>
predict(new_data = penguins_test)
#> # A tibble: 67 × 1
#> .pred
#> <dbl>
#> 1 3796.
#> 2 3788.
#> 3 4430.
#> 4 3828.
#> 5 3899.
#> 6 3967.
#> 7 3387.
#> 8 3655.
#> 9 3885.
#> 10 3728.
#> # ℹ 57 more rows
# Create conformal inference predictions
mod_int <- int_conformal_split(xgb_fit, cal_data = penguins_train)
predict(mod_int, new_data = penguins_test)
#> # A tibble: 67 × 3
#> .pred .pred_lower .pred_upper
#> <dbl> <dbl> <dbl>
#> 1 3796. 3632. 3960.
#> 2 3788. 3624. 3952.
#> 3 4430. 4266. 4594.
#> 4 3828. 3664. 3992.
#> 5 3899. 3735. 4063.
#> 6 3967. 3802. 4131.
#> 7 3387. 3223. 3552.
#> 8 3655. 3491. 3819.
#> 9 3885. 3721. 4049.
#> 10 3728. 3564. 3893.
#> # ℹ 57 more rows
# Save the conformal inference object
temp_int_path <- tempfile(fileext = ".rds")
mod_int |>
bundle() |>
write_rds(file = temp_int_path)
# Load the conformal inference object in a new session
mod_int_load <- read_rds(temp_int_path)
mod_int_load |>
unbundle() |>
predict(new_data = penguins_test)
#> Error in xgb.get.handle(object): invalid 'xgb.Booster' (blank 'externalptr').
Created on 2026-02-17 with reprex v2.1.1
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.5.2 (2025-10-31)
#> os macOS Tahoe 26.3
#> system aarch64, darwin20
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2026-02-17
#> pandoc 3.6.3 @ /Applications/Positron.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
#> quarto 1.9.20 @ /usr/local/bin/quarto
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
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#> bundle * 0.1.3 2025-12-10 [1] RSPM (R 4.5.0)
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#>
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#> * ── Packages attached to the search path.
#>
#> ──────────────────────────────────────────────────────────────────────────────
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at int_conformal_split() and follow how its result passes through bundle(), write_rds(), read_rds(), and unbundle(). Reproduce the new-session failure with the xgboost workflow shown in the issue, then verify that the reloaded object can predict without the invalid external pointer error.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- r
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100