Managing Runs

Run Output

Any graphical or console output as well as file artifacts created by a training run (e.g. saved models or saved model weights) can be viewed from the Output tab of the run view:

You can use the copy_run_files() function to export file artifacts from runs into another directory. For example:

copy_run_files("runs/2017-09-24T10-54-00Z", to = "saved-model")

You can also use the copy_run() function to export a run directory in it’s entirety. For example, this code exports the specified run to a “best-run” directory:

copy_run("runs/2017-09-24T10-54-00Z", to = "best-run")

Note that copy_run() will accept any number of runs. For example, this code exports all run directories with an evaluation accuracy greater than 0.98 to a “best-runs” directory:

copy_run(ls_runs(eval_acc >= 0.98), to = "best-runs")

Cleaning Runs

You can use the clean_runs() function to archive a set of runs you no longer need the data from. For example, this code archives all runs with an eval accuracy less than 0.98:

clean_runs(ls_runs(eval_acc < 0.98))

If you don’t specify a set of runs to clean then all runs will be archived:

clean_runs() # archives all runs in the "runs" directory

Note that you’ll always get a confirmation prompt before the runs are actually archived.

Purging Runs

When runs are archived they are moved to the “archive” subdirectory of the “runs” directory. If you want to permanently remove runs from the archive you call the purge_runs() function:


Experiment Scopes

By default all runs go into the “runs” sub-directory of the current working directory. For various types of ad-hoc experimentation this works well, but in some cases you may want to create a separate directory scope for the set of runs that compose an experiment.

To do this you can either call clean_runs() before beginning work on a new experiment, or you can set the tfruns.runs_dir global option to ensure that all run operations (including queries with ls_runs() use a separate scope). To return to the previous example of experimenting with various dropout values, I might have a driver script that looks like this:


# use 'dropout_experiment_runs' as the run_dir
options(tfruns.runs_dir = "dropout_experiment_runs")

# try 9 perumutations of dropout
for (dropout1 in c(0.1, 0.2, 0.3))
  for (dropout2 in c(0.1, 0.2, 0.3))
    training_run('mnist_mlp.R', flags = list(dropout1 = dropout1, dropout2 = dropout2))

# see which combination of dropout values performed best
ls_runs(order = eval_acc)[,c("eval_acc", "metric_acc", "flag_dropout1", "flag_dropout2")]
# A tibble: 9 x 4
  eval_acc metric_acc flag_dropout1 flag_dropout2
     <dbl>      <dbl>         <dbl>         <dbl>
1   0.9832     0.9936           0.2           0.1
2   0.9827     0.9920           0.2           0.3
3   0.9824     0.9903           0.3           0.2
4   0.9817     0.9949           0.1           0.3
5   0.9813     0.9932           0.2           0.2
6   0.9811     0.9968           0.1           0.1
7   0.9810     0.9883           0.3           0.3
8   0.9798     0.9958           0.1           0.2
9   0.9794     0.9904           0.3           0.1