Sagemaker API vs. Sagemaker R Package

Introduction

AWS Sagemaker is a powerful tool to efficently build and deploy machine learning models. However, I don’t think the API is suitable for exploratory training and data analysis. Too many of the minor details are left to the user. My goal with this package is to create a simplified user interface, with sensible defaults, that gets you training and analyzing with Sagemaker faster than ever.

Side-by-side comparsion

library(dplyr)
library(stringr)

R sagemaker

library(sagemaker)

xgb <- sagemaker_xgb_estimator()

AWS Sagemaker

library(reticulate)
sagemaker <- reticulate::import("sagemaker")

xgb_container <- sagemaker$amazon$amazon_estimator$get_image_uri(
  boto3$Session()$region_name,
  "xgboost",
  repo_version = "latest"
)

xgb <- sagemaker$estimator$Estimator(
  xgb_container,
  sagemaker_get_execution_role(),
  train_instance_count = 1L,
  train_instance_type = "ml.m4.xlarge",
  output_path = s3(s3_bucket(), "/models/"),
  sagemaker_session = sagemaker$Session()
)

R sagemaker

xgb$set_hyperparameters(
  eval_metric = "rmse",
  objective = "reg:linear",
  eta = 0.1,
  gamma = 0.0,
  min_child_weight = 1,
  num_round = 100L,
  early_stopping_rounds = 50L
)

grid <- list(
  max_depth = sagemaker_integer(3, 20),
  colsample_bytree = sagemaker_continuous(0, 1),
  subsample = sagemaker_continuous(0, 1)
)

split <- s3_split(
  s3_train = s3(s3_bucket(), "abalone-train.csv"),
  s3_validation = s3(s3_bucket(), "abalone-test.csv")
)

tune <- sagemaker_hyperparameter_tuner(
  xgb, split, grid, max_jobs = 1
)

AWS Sagemaker

xgb$set_hyperparameters(
  eval_metric = "rmse",
  objective = "reg:linear",
  eta = 0.1,
  gamma = 0.0,
  min_child_weight = 1,
  num_round = 100L,
  early_stopping_rounds = 50L
)

grid <- list(
  max_depth = 
    sagemaker$tuner$IntegerParameter(3L, 20L),
  colsample_bytree = 
    sagemaker$tuner$ContinuousParameter(0, 1),
  subsample = 
    sagemaker$tuner$ContinuousParameter(0, 1)
)

tune <- sagemaker$tuner$HyperparameterTuner(
  xgb,
  "validation:rmse",
  objective_type = "Minimize",
  grid,
  strategy = "Random",
  max_jobs = 1L,
  max_parallel_jobs = 1L,
  early_stopping_type = "Auto"
)

train_data <- sagemaker$s3_input(
  s3(s3_bucket(), "/abalone-train.csv"),
  content_type = "text/csv"
)

validation_data <- sagemaker$s3_input(
  s3(s3_bucket(), "/abalone-test.csv"),
  content_type = "text/csv"
)

tuning$fit(
  reticulate::dict(
    train = train_data,
    validation = validation_data
  )
)

R sagemaker

sagemaker_tuning_job_logs(tune)
sagemaker_training_job_logs(tune$model_name)

AWS Sagemaker

tuning_analytics <- sagemaker$HyperparameterTuningJobAnalytics(
  tuning$latest_tuning_job$job_name
)

tuning_stas <- tuning_analytics$dataframe()

best_tuned_model <- tuning_stats %>%
  filter(FinalObjectiveValue == min(FinalObjectiveValue)) %>%
  pull(TrainingJobName)

sagemaker$TrainingJobAnalytics(best_tuned_model)$dataframe()

R sagemaker

sagemaker_deploy_endpoint(tune)
predict(tune, sagemaker::abalone[1:100, -1])

AWS Sagemaker

predictor <- xgb$deploy(
  initial_instance_count = 1L,
  instance_type = "ml.t2.medium",
)

predictor$content_type <- "text/csv"
predictor$serializer <- sagemaker$predictor$csv_serializer

new_data <- as.matrix(sagemaker::abalone[1:100, -1])
dimnames(new_data)[[2]] <- NULL

predictions <- predictor$predict(new_data)

predictions %>%
  str_split(pattern = ",", simplify = TRUE) %>%
  as.numeric()

R sagemaker

batch_predict(
  tune, 
  s3_input = s3(s3_bucket(), "abalone-inference.csv"),
  s3_output = s3(s3_bucket(), "abalone_predictions")
)

AWS Sagemaker

transfomer <- xgb$transformer(
  instance_count = 1L,
  instance_type = "ml.c4.xlarge",
  output_path = s3(s3_bucket(), "abalone_predictions"),
  assemble_with = 'Line'
)

transfomer$transform(
  s3(s3_bucket(), "abalone-inference.csv"),
  content_type = "text/csv",
  split_type = "Line",
  wait = TRUE,
  logs = FALSE
)