cloudml_train {cloudml} | R Documentation |
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
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 |
region |
The region to be used for training. |
config |
A list, |
collect |
Logical. If TRUE, collect job when training is completed
(blocks waiting for the job to complete). The default ( |
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)