training_run: Run a training script
In tfruns: Training Run Tools for 'TensorFlow'

training_run

R Documentation

Run a training script

Description

Run a training script

Usage

training_run(
  file = "train.R",
  context = "local",
  config = Sys.getenv("R_CONFIG_ACTIVE", unset = "default"),
  flags = NULL,
  properties = NULL,
  run_dir = NULL,
  artifacts_dir = getwd(),
  echo = TRUE,
  view = "auto",
  envir = parent.frame(),
  encoding = getOption("encoding")
)

Arguments

`file`	Path to training script (defaults to "train.R")
`context`	Run context (defaults to "local")
`config`	The configuration to use. Defaults to the active configuration for the current environment (as specified by the `R_CONFIG_ACTIVE` environment variable), or `default` when unset.
`flags`	Named list with flag values (see `flags()`) or path to YAML file containing flag values.
`properties`	Named character vector with run properties. Properties are additional metadata about the run which will be subsequently available via `ls_runs()`.
`run_dir`	Directory to store run data within
`artifacts_dir`	Directory to capture created and modified files within. Pass `NULL` to not capture any artifcats.
`echo`	Print expressions within training script
`view`	View the results of the run after training. The default "auto" will view the run when executing a top-level (printed) statement in an interactive session. Pass `TRUE` or `FALSE` to control whether the view is shown explictly. You can also pass "save" to save a copy of the run report at `tfruns.d/view.html`
`envir`	The environment in which the script should be evaluated
`encoding`	The encoding of the training script; see `file()`.

Details

The training run will by default use a unique new run directory within the "runs" sub-directory of the current working directory (or to the value of the tfruns.runs_dir R option if specified).

The directory name will be a timestamp (in GMT time). If a duplicate name is generated then the function will wait long enough to return a unique one.

If you want to use an alternate directory to store run data you can either set the global tfruns.runs_dir R option, or you can pass a run_dir explicitly to training_run(), optionally using the unique_run_dir() function to generate a timestamp-based directory name.