flags: Flags for a training run

In tfruns: Training Run Tools for 'TensorFlow'

Flags for a training run

Description

Define the flags (name, type, default value, description) which paramaterize a training run. Optionally read overrides of the default values from a "flags.yml" config file and/or command line arguments.

Usage

flags(
  ...,
  config = Sys.getenv("R_CONFIG_ACTIVE", unset = "default"),
  file = "flags.yml",
  arguments = commandArgs(TRUE)
)

flag_numeric(name, default, description = NULL)

flag_integer(name, default, description = NULL)

flag_boolean(name, default, description = NULL)

flag_string(name, default, description = NULL)

Arguments

...	One or more flag definitions
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.
file	The flags YAML file to read
arguments	The command line arguments (as a character vector) to be parsed.
name	Flag name
default	Flag default value
description	Flag description

Value

Named list of training flags

Config File Flags

Config file flags are defined a YAML configuration file (by default named "flags.yml"). Flags can either appear at the top-level of the YAML or can be inclued in named configuration sections (see the config package for details).

Command Line Flags

Command line flags should be of the form --key=value or ⁠--key value⁠. The values are assumed to be valid yaml and will be converted using yaml.load().

Examples

## Not run: 
library(tfruns)

# define flags and parse flag values from flags.yml and the command line
FLAGS <- flags(
  flag_numeric('learning_rate', 0.01, 'Initial learning rate.'),
  flag_integer('max_steps', 5000, 'Number of steps to run trainer.'),
  flag_string('data_dir', 'MNIST-data', 'Directory for training data'),
  flag_boolean('fake_data', FALSE, 'If true, use fake data for testing')
)

## End(Not run)

tfruns documentation built on May 29, 2024, 7:31 a.m.