The Determined distribution contains several example experiments in the
examples/ subdirectory. Each experiment consists of a single
model definition and one or more experiment configurations.
TensorFlow (Estimator API)
This example shows how to use Determined with TensorBoard to visualize training and/or validation metrics.
To configure TensorBoard with Determined, follow these steps:
Set up a directory on a shared file system for TensorBoard event files, e.g.
/mnt/tensorboard. All agents must be able to write to this directory.
Add an entry to the experiment config to ensure that the shared directory is mounted into each trial container. In this example, we use
/tensorboardas the container path:
bind_mounts: - host_path: /mnt/tensorboard container_path: /tensorboard
callbacks()interface in your model definition. Use your
/tensorboardin this example) as the
from determined.estimator import TFEventWriter class MNISTTrial(EstimatorTrial): ... def callbacks(self, hparams): return TFEventWriter("/tensorboard") ...
Start TensorBoard using the
host_pathfrom step 2 to view the experiment metrics as the experiment is running or after it has ended.