The PEDL distribution contains several example experiments in the
examples/ subdirectory. Each experiment consists of a single model definition and one or more experiment configurations.
|Keras (Simple Model Definition)||MNIST|
|Keras (Functional API)||MNIST|
|TensorFlow (Estimator API)||MNIST|
|TensorFlow (tf.keras)||CIFAR-10 CNN|
Custom Plots with Jupyter¶
The PEDL WebUI includes native support for several plots, but for more complex analysis of experiment metadata, customers can use the plotting functionality provided by Jupyter notebooks. An example notebook is provided in the
examples/notebooks directory. The example describes how to obtain PEDL experiment metadata in CSV format (by running the
pedl CLI) and then demonstrates how to plot several graphs using the Python
This example shows how to use PEDL with TensorBoard to visualize training and/or validation metrics. TensorBoard can be used with PEDL experiments that use TensorFlow, or Keras experiments that use the TensorFlow backend.
To configure TensorBoard with PEDL, 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
- Implement the
callbacks()interface in your model definition using
pedl.callback.TensorBoard. Use your
/tensorboardin this example) as the
from pedl.frameworks.tensorflow import TensorBoard class MNISTTrial(EstimatorTrial): ... def callbacks(self, hparams): return TensorBoard("/tensorboard") ...
- Start TensorBoard using the
host_pathfrom step 2 to view the experiment metrics as the experiment is running or after it has ended.