cloudml_train: Train a model using Cloud ML

In rstudio/cloudml: Interface to the Google Cloud Machine Learning Platform

Description

Upload a TensorFlow application to Google Cloud, and use that application to train a model.

Usage

cloudml_train(file = "train.R", master_type = NULL, flags = NULL,
  region = NULL, config = NULL, collect = "ask", dry_run = FALSE)

Arguments

file	File to be used as entrypoint for training.
master_type	Training master node machine type. "standard" provides a basic machine configuration suitable for training simple models with small to moderate datasets. See the documentation at https://cloud.google.com/ml-engine/docs/tensorflow/machine-types#machine_type_table for details on available machine types.
flags	Named list with flag values (see flags()) or path to YAML file containing flag values.
region	The region to be used for training.
config	A list, YAML or JSON configuration file as described https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs.
collect	Logical. If TRUE, collect job when training is completed (blocks waiting for the job to complete). The default ("ask") will interactively prompt the user whether to collect the results or not.
dry_run	Triggers a local dry run over the deployment phase to validate packages and packing work as expected.

See Also

job_status(), job_collect(), job_cancel()

Other CloudML functions: cloudml_deploy, cloudml_predict

Examples

## Not run: 
library(cloudml)

gcloud_install()
job <- cloudml_train("train.R")

## End(Not run)

rstudio/cloudml documentation built on Aug. 12, 2020, 8:22 p.m.