Package: vetiver 0.2.5.9000

Julia Silge

vetiver:Version, Share, Deploy, and Monitor Models

The goal of 'vetiver' is to provide fluent tooling to version, share, deploy, and monitor a trained model. Functions handle both recording and checking the model's input data prototype, and predicting from a remote API endpoint. The 'vetiver' package is extensible, with generics that can support many kinds of models.

Authors:Julia Silge [cre, aut], Posit Software, PBC [cph, fnd]

vetiver_0.2.5.9000.tar.gz
vetiver_0.2.5.9000.zip(r-4.5)vetiver_0.2.5.9000.zip(r-4.4)vetiver_0.2.5.9000.zip(r-4.3)
vetiver_0.2.5.9000.tgz(r-4.4-any)vetiver_0.2.5.9000.tgz(r-4.3-any)
vetiver_0.2.5.9000.tar.gz(r-4.5-noble)vetiver_0.2.5.9000.tar.gz(r-4.4-noble)
vetiver_0.2.5.9000.tgz(r-4.4-emscripten)vetiver_0.2.5.9000.tgz(r-4.3-emscripten)
vetiver.pdf |vetiver.html✨
vetiver/json (API)
NEWS

# Installvetiver in R:

install.packages('vetiver',repos = c('https://rstudio.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rstudio/vetiver-r/issues

On CRAN:

44 exports 177 stars 5.50 score 46 dependencies 1 dependents 3 mentions 1.2k downloads

Last updated 2 days agofrom:5c321a4154

Exports:api_spec attach_pkgs augment get_vetiver_dashboard_pins glue_spec_summary handler_predict handler_startup load_pkgs map_request_body new_vetiver_model pin_example_kc_housing_model required_pkgs vetiver_api vetiver_compute_metrics vetiver_create_description vetiver_create_meta vetiver_create_ptype vetiver_create_rsconnect_bundle vetiver_dashboard vetiver_deploy_rsconnect vetiver_deploy_sagemaker vetiver_endpoint vetiver_endpoint_sagemaker vetiver_meta vetiver_model vetiver_pin_metrics vetiver_pin_read vetiver_pin_write vetiver_plot_metrics vetiver_pr_docs vetiver_pr_post vetiver_pr_predict vetiver_prepare_docker vetiver_prepare_model vetiver_ptype vetiver_python_requirements vetiver_renviron_requirements vetiver_sm_build vetiver_sm_delete vetiver_sm_endpoint vetiver_sm_model vetiver_type_convert vetiver_write_docker vetiver_write_plumber

Dependencies:askpass bit bit64 bundle butcher cereal cli clipr cpp11 crayon curl digest fansi fs generics glue hardhat hms httr jsonlite lifecycle lobstr magrittr mime openssl pillar pins pkgconfig prettyunits progress purrr R6 rapidoc rappdirs readr rlang sys tibble tidyselect tzdb utf8 vctrs vroom whisker withr yaml

Version, share, and deploy a model with vetiver

Rendered fromvetiver.Rmdusingknitr::rmarkdownon Jul 03 2024.

Last update: 2023-06-12
Started: 2021-11-17

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Update the OpenAPI specification using model metadata	api_spec glue_spec_summary glue_spec_summary.array glue_spec_summary.data.frame glue_spec_summary.default
Fully attach or load packages for making model predictions	attach_pkgs load_pkgs
Post new data to a deployed model API endpoint and augment with predictions	augment.vetiver_endpoint
Post new data to a deployed SageMaker model endpoint and augment with predictions	augment.vetiver_endpoint_sagemaker
Model handler functions for API endpoint	handler_predict handler_predict.default handler_predict.gam handler_predict.glm handler_predict.keras.engine.training.Model handler_predict.kproto handler_predict.Learner handler_predict.lm handler_predict.luz_module_fitted handler_predict.model_stack handler_predict.ranger handler_predict.recipe handler_predict.train handler_predict.workflow handler_predict.xgb.Booster handler_startup handler_startup.default handler_startup.gam handler_startup.keras.engine.training.Model handler_startup.Learner handler_startup.luz_module_fitted handler_startup.model_stack handler_startup.ranger handler_startup.recipe handler_startup.train handler_startup.workflow handler_startup.xgb.Booster
Identify data types for each column in an input data prototype	map_request_body
Post new data to a deployed model API endpoint and return predictions	predict.vetiver_endpoint
Post new data to a deployed SageMaker model endpoint and return predictions	predict.vetiver_endpoint_sagemaker
Create a Plumber API to predict with a deployable 'vetiver_model()' object	vetiver_api vetiver_pr_docs vetiver_pr_post
Aggregate model metrics over time for monitoring	vetiver_compute_metrics
Model constructor methods	vetiver_create_description vetiver_create_description.default vetiver_create_description.gam vetiver_create_description.glm vetiver_create_description.keras.engine.training.Model vetiver_create_description.kproto vetiver_create_description.Learner vetiver_create_description.lm vetiver_create_description.luz_module_fitted vetiver_create_description.model_stack vetiver_create_description.ranger vetiver_create_description.recipe vetiver_create_description.train vetiver_create_description.workflow vetiver_create_description.xgb.Booster vetiver_prepare_model vetiver_prepare_model.default vetiver_prepare_model.gam vetiver_prepare_model.glm vetiver_prepare_model.keras.engine.training.Model vetiver_prepare_model.kproto vetiver_prepare_model.Learner vetiver_prepare_model.lm vetiver_prepare_model.luz_module_fitted vetiver_prepare_model.model_stack vetiver_prepare_model.ranger vetiver_prepare_model.recipe vetiver_prepare_model.train vetiver_prepare_model.workflow vetiver_prepare_model.xgb.Booster
Metadata constructors for 'vetiver_model()' object	vetiver_create_meta vetiver_create_meta.default vetiver_create_meta.gam vetiver_create_meta.keras.engine.training.Model vetiver_create_meta.kproto vetiver_create_meta.Learner vetiver_create_meta.luz_module_fitted vetiver_create_meta.model_stack vetiver_create_meta.ranger vetiver_create_meta.recipe vetiver_create_meta.train vetiver_create_meta.workflow vetiver_create_meta.xgb.Booster vetiver_meta
Create an Posit Connect bundle for a vetiver model API	vetiver_create_rsconnect_bundle
R Markdown format for model monitoring dashboards	get_vetiver_dashboard_pins pin_example_kc_housing_model vetiver_dashboard
Deploy a vetiver model API to Posit Connect	vetiver_deploy_rsconnect
Deploy a vetiver model API to Amazon SageMaker	vetiver_deploy_sagemaker
Create a model API endpoint object for prediction	vetiver_endpoint
Create a SageMaker model API endpoint object for prediction	vetiver_endpoint_sagemaker
Create a vetiver object for deployment of a trained model	new_vetiver_model vetiver_model
Update model metrics over time for monitoring	vetiver_pin_metrics
Read and write a trained model to a board of models	vetiver_pin_read vetiver_pin_write
Plot model metrics over time for monitoring	vetiver_plot_metrics
Generate files necessary to build a Docker container for a vetiver model	vetiver_prepare_docker
Create a vetiver input data prototype	vetiver_create_ptype vetiver_ptype vetiver_ptype.default vetiver_ptype.gam vetiver_ptype.glm vetiver_ptype.keras.engine.training.Model vetiver_ptype.kproto vetiver_ptype.Learner vetiver_ptype.lm vetiver_ptype.luz_module_fitted vetiver_ptype.model_stack vetiver_ptype.ranger vetiver_ptype.recipe vetiver_ptype.train vetiver_ptype.workflow vetiver_ptype.xgb.Booster
Deploy a vetiver model API to Amazon SageMaker with modular functions	vetiver_sm_build vetiver_sm_endpoint vetiver_sm_model
Delete Amazon SageMaker model, endpoint, and endpoint configuration	vetiver_sm_delete
Convert new data at prediction time using input data prototype	vetiver_type_convert
Write a Dockerfile for a vetiver model	vetiver_write_docker
Write a deployable Plumber file for a vetiver model	vetiver_write_plumber