README.Rmd

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# sagemaker

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The sagemaker R package provides a simplified interface
to the AWS Sagemaker API by:

1. adding sensible defaults so you can dive in quickly
2. creating helper functions to streamline model analysis
3. supporting `data.frame`s and [tibbles](https://github.com/tidyverse/tibble)

Check out the 
[Get started](https://tmastny.github.io/sagemaker/articles/sagemaker.html) 
guide for examples!

## Less boilerplate

Compare 
[the AWS Sagemaker API vs. the sagemaker R package](https://tmastny.github.io/sagemaker/articles/sagemaker-vs-sagemaker.html).

The R package hides the details for later, 
so you can get started ASAP.

[![](man/figures/side-by-side.png)](https://tmastny.github.io/sagemaker/articles/sagemaker-vs-sagemaker.html)

## Easier predictions

Get Sagemaker endpoint predictions with no string parsing or REST API management. 
Directly use `predict` on the Sagemaker model to get predictions
that conform to the 
[tidymodel](https://tidymodels.github.io/parsnip/reference/predict.model_fit.html)
standard.

```{r eval = FALSE}
model <- sagemaker_hyperparameter_tuner(xgb, s3_split(train, validation))
pred <- predict(model, new_data)
```


Even [download and load](https://tmastny.github.io/sagemaker/articles/load-model.html)
Sagemaker trained models into your R session:

```{r eval = FALSE}
local_model <- sagemaker_load_model(model)
local_pred <- predict(local_model, new_data)
```

## Analyze tuning and fit

Easily get tuning and training stats with `sagemaker_training_job_logs` 
and `sagemaker_tuning_job_logs`.

```{r echo = FALSE}
knitr::include_graphics("man/figures/eval.png")
```

## Installation

You can install sagemaker from [GitHub](https://github.com/) with:
```r
# install.packages("devtools")
devtools::install_github("tmastny/sagemaker")
```

You will also need `boto3`, `sagemaker`, and `awscli` 
python packages.
If you don't have them, or aren't sure, install with:
```r
sagemaker::sagemaker_install()
```

Next, you'll need an [AWS account](https://aws.amazon.com/premiumsupport/knowledge-center/create-and-activate-aws-account/). 
This is a complicated process and unfortunately I don't have a good guide
to share. Please reach out if you find one and I'll post it here!

Once you have the account and user setup you'll want to
save your account access keys. 
[This](https://www.viget.com/articles/set-up-aws-cli-and-download-your-s3-files-from-the-command-line/)
is a good guide to follow.

Lastly, you need a AWS Sagemaker Execution Role.
The easiest way is to create a AWS Sagemaker Notebook Instance. 
Try [this guide](https://sagemaker-workshop.com/introduction/notebook.html#launch-the-notebook-instance)
to get the notebook running.
Once the notebook is deployed, *inside the instance* run:
```python
from sagemaker import get_execution_role

role = get_execution_role()
print(role)
```

Once you have that role, 
run this command locally 
to save it with your local AWS config:
```r
sagemaker::sagemaker_save_execution_role(
  "arn:aws:iam::[account_number]:role/service-role/[SageMakerExecutionRole]"
)
```