Run a training script
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")
)Single row data frame with run flags, metrics, etc.
Path to training script (defaults to "train.R")
Run context (defaults to "local")
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.
Named list with flag values (see flags()) or path
to YAML file containing flag values.
Named character vector with run properties. Properties are
additional metadata about the run which will be subsequently available via
ls_runs().
Directory to store run data within
Directory to capture created and modified files within.
Pass NULL to not capture any artifcats.
Print expressions within training script
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
The environment in which the script should be evaluated
The encoding of the training script; see file().
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.