TensorBoard is a tool inspecting and understanding your TensorFlow runs and graphs.
tensorboard(
log_dir,
action = c("start", "stop"),
host = "127.0.0.1",
port = "auto",
launch_browser = getOption("tensorflow.tensorboard.browser", interactive()),
reload_interval = 5,
purge_orphaned_data = TRUE
)URL for browsing TensorBoard (invisibly).
Directories to scan for training logs. If this is a named character vector then the specified names will be used as aliases within TensorBoard.
Specify whether to start or stop TensorBoard (TensorBoard will be stopped automatically when the R session from which it is launched is terminated).
Host for serving TensorBoard
Port for serving TensorBoard. If "auto" is specified (the default) then an unused port will be chosen automatically.
Open a web browser for TensorBoard after launching.
Defaults to TRUE in interactive sessions. When running under RStudio uses
an RStudio window by default (pass a function e.g. utils::browseURL() to
open in an external browser). Use the tensorflow.tensorboard.browser
option to establish a global default behavior.
How often the backend should load more data.
Whether to purge data that may have been orphaned due to TensorBoard restarts. Disabling purge_orphaned_data can be used to debug data disappearance.
When TensorBoard is passed a logdir at startup, it recursively walks the directory tree rooted at logdir looking for subdirectories that contain tfevents data. Every time it encounters such a subdirectory, it loads it as a new run, and the frontend will organize the data accordingly.
The TensorBoard process will be automatically destroyed when the R session
in which it is launched exits. You can pass action = "stop" to manually
terminate TensorBoard.