Monitor Your Experiments with TensorBoard

TensorBoard is a powerful tool that allows you to monitor and visualize an experiment. This section introduces how to set up TensorBoard logging in garage, and how to view/share your TensorBoard logs.


The TensorBoard package should be installed upon garage’s installation. It can be also manually installed via:

pip install tensorboard

Setup Your Experiment

Use @wrap_experiment to setup your experiment. This will automatically setup TensorBoard output and allocate an experiment directory for you.

Find more about how to use @wrap_experiment here.

Log to TensorBoard

This section introduces how to log TensorBoard output with logger, and how to log different types of TensorBoard output.

Find more about how to use logger here.

Log Scalar Values to TensorBoard

You would need dowel.TabularInput.

To prepare your TensorBoard output, import dowel’s TabularInput instance tabular by:

from dowel import tabular

Add scalar values:

tabular.record('Epoch', epoch)
tabular.record('# Sample', i_sample)
tabular.record('AverageDiscountedReturn', return)

Finally, log your scalar values with logger:


Log Histograms to TensorBoard

To log histograms to TensorBoard, you would need to pass dowel.Histogram to tabular.

dowel.Histogram is implemented as a typed view of a numpy array. It will accept input that numpy.asarray will.

For example:

from dowel import Histogram, logger, tabular

samples = norm.rvs(100)  # ndarray of 100 samples of a normal distribution
hist = Histogram(samples)
tabular.record('Sample', hist)


You can use Histogram() to log distribution types from scipy.stats.

View Your TensorBoard Logs

You can view your TensorBoard results via the TensorBoard command line tool:

$ tensorboard --logdir logs

Once you run the command, you will see

$ tensorboard --logdir logs
Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
TensorBoard 2.2.2 at http://localhost:6006/ (Press CTRL+C to quit)

Open your browser and navigate to the link to see a dashboard of your TensorBoard results.

Note that if your --logdir directory contains multiple TensorBoard logs, the dashboard will show all of them. This is useful when comparing results from multiple runs.

You can find more TensorBoard dashboard details here.

Share Your TensorBoard Logs

TensorBoard Dev allows you to share your TensorBoard results with others.

Upload your logs by:

$ tensorboard dev upload --logdir data/log \
    --name "(optional) My latest experiment" \
    --description "(optional) Simple comparison of several hyperparameters"

You should see:

$ tensorboard dev upload --logdir data/log

Upload started and will continue reading any new data as it's added
to the logdir. To stop uploading, press Ctrl-C.
View your TensorBoard live at:

Now you can go to the link provided to view your experiment, or share it with others.

Find more about TensorBoard Dev here.

Advanced Features

Change the output directory or x-axis

By default, @wrap_experiment sets up TensorBoard output via:

logger.add_output(dowel.TensorBoardOutput(log_dir, x_axis='TotalEnvSteps'))

The default log_dir is $(pwd)/data/local, and the x_axis is 'TotalEnvSteps'.

To manually output to a different directory, you can set up logger in your experiment by:


Add additional x-axes

If you’d like to view your logs on more than one x-axis, you can configure dowel to log to additional x-axes by passing a list of tabular keys to the additional_x_axes parameter of TensorBoardOutput.

    additional_x_axes=['Itr'],  # Logs keys by optimization iteration as well

Using this configuration, the key Loss will appear in TensorBoard under both the tag Loss using TotalEnvSteps as its x-axis, and under the tag Loss/Itr using Itr as its x-axis.

This page was authored by Eric Yihan Chen (@AiRuiChen).