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https://github.com/juliasilge/deploytidymodels
Version, share, and deploy tidymodels workflows
https://github.com/juliasilge/deploytidymodels
Last synced: about 1 month ago
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Version, share, and deploy tidymodels workflows
- Host: GitHub
- URL: https://github.com/juliasilge/deploytidymodels
- Owner: juliasilge
- License: other
- Archived: true
- Created: 2021-06-12T04:00:50.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-11-16T20:03:33.000Z (about 3 years ago)
- Last Synced: 2024-08-06T03:03:25.338Z (5 months ago)
- Language: R
- Homepage:
- Size: 117 KB
- Stars: 21
- Watchers: 4
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# deploytidymodels
**This repo is archived in favor of the [vetiver](https://github.com/tidymodels/vetiver/) package.**
The goal of deploytidymodels is to provide fluent tooling to version, share, and deploy a trained model [workflow](https://workflows.tidymodels.org/) using the [vetiver](https://github.com/tidymodels/vetiver) framework. Functions handle both recording and checking the model's input data prototype, and loading the packages needed for prediction.
## Installation
~~You can install the released version of deploytidymodels from [CRAN](https://CRAN.R-project.org) with:~~
``` r
install.packages("deploytidymodels") ## not yet
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("juliasilge/deploytidymodels")
```
## Example
You can use the [tidymodels](https://www.tidymodels.org/) ecosystem to train a model, with a wide variety of preprocessing and model estimation options.
```{r}
library(parsnip)
library(workflows)
data(Sacramento, package = "modeldata")
rf_spec <- rand_forest(mode = "regression")
rf_form <- price ~ type + sqft + beds + baths
rf_fit <-
workflow(rf_form, rf_spec) %>%
fit(Sacramento)
```
You can **version** and **share** your model by [pinning](https://pins.rstudio.com/dev/) it, to a local folder, RStudio Connect, Amazon S3, and more.
```{r}
library(deploytidymodels)
library(pins)
model_board <- board_temp()
m <- vetiver_model(rf_fit, "sacramento_rf", model_board)
vetiver_pin_write(m)
```
You can **deploy** your pinned model via a [Plumber API](https://www.rplumber.io/), which can be [hosted in a variety of ways](https://www.rplumber.io/articles/hosting.html).
```{r, eval=FALSE}
library(plumber)
pr() %>%
vetiver_pr_predict(m) %>%
pr_run(port = 8088)
```
Make predictions with your deployed model by creating an endpoint object:
```{r}
endpoint <- vetiver_endpoint("http://127.0.0.1:8088/predict")
endpoint
```
A model API endpoint deployed with `vetiver_pr_predict()` will return predictions with appropriate new data.
``` r
library(tidyverse)
new_sac <- Sacramento %>%
slice_sample(n = 20) %>%
select(type, sqft, beds, baths)
predict(endpoint, new_sac)
#> # A tibble: 20 x 1
#> .pred
#>
#> 1 165042.
#> 2 212461.
#> 3 119008.
#> 4 201752.
#> 5 223096.
#> 6 115696.
#> 7 191262.
#> 8 211706.
#> 9 259336.
#> 10 206826.
#> 11 234952.
#> 12 221993.
#> 13 204983.
#> 14 548052.
#> 15 151186.
#> 16 299365.
#> 17 213439.
#> 18 287993.
#> 19 272017.
#> 20 226629.
```
## Contributing
This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on RStudio Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
- If you think you have encountered a bug, please [submit an issue](https://github.com/juliasilge/deploytidymodels/issues).
- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html) (a minimal, reproducible example), to clearly communicate about your code.
- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).