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:
vetiver_0.2.5.9000.tar.gz
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vetiver.pdf |vetiver.html✨
vetiver/json (API)
NEWS
| # Install 'vetiver' 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
Last updated 1 months agofrom:54996d51a9. Checks:OK: 6 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 08 2024 |
| R-4.5-win | OK | Nov 08 2024 |
| R-4.5-linux | OK | Nov 08 2024 |
| R-4.4-win | OK | Nov 08 2024 |
| R-4.4-mac | NOTE | Nov 08 2024 |
| R-4.3-win | OK | Nov 08 2024 |
| R-4.3-mac | OK | Nov 08 2024 |
Exports:api_specattach_pkgsaugmentget_vetiver_dashboard_pinsglue_spec_summaryhandler_predicthandler_startupload_pkgsmap_request_bodynew_vetiver_modelpin_example_kc_housing_modelrequired_pkgsvetiver_apivetiver_compute_metricsvetiver_create_descriptionvetiver_create_metavetiver_create_ptypevetiver_create_rsconnect_bundlevetiver_dashboardvetiver_deploy_rsconnectvetiver_deploy_sagemakervetiver_endpointvetiver_endpoint_sagemakervetiver_metavetiver_modelvetiver_pin_metricsvetiver_pin_readvetiver_pin_writevetiver_plot_metricsvetiver_pr_docsvetiver_pr_postvetiver_pr_predictvetiver_prepare_dockervetiver_prepare_modelvetiver_ptypevetiver_python_requirementsvetiver_renviron_requirementsvetiver_sm_buildvetiver_sm_deletevetiver_sm_endpointvetiver_sm_modelvetiver_type_convertvetiver_write_dockervetiver_write_plumber
Dependencies:askpassbitbit64bundlebutchercerealclicliprcpp11crayoncurldigestfansifsgenericsgluehardhathmshttrjsonlitelifecyclelobstrmagrittrmimeopensslpillarpinspkgconfigprettyunitsprogresspurrrR6rapidocrappdirsreadrrlangsystibbletidyselecttzdbutf8vctrsvroomwhiskerwithryaml
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 |
