cloudml_train: Train a model using Cloud ML

Description Usage Arguments See Also Examples

View source: R/jobs.R

Description

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

Usage

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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

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## Not run: 
library(cloudml)

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

## End(Not run)

rstudio/cloudml documentation built on Aug. 11, 2018, 7:02 a.m.