R/jobs.R

cloudml_train

Train a model using Cloud ML

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

Arguments Description
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.

Examples

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

See Also

job_status(), job_collect(), job_cancel() Other CloudML functions: cloudml_deploy, cloudml_predict