Horovod is an open-source library for distributed training of deep learning models. TensorBoard is a visualization tool provided by TensorFlow to visualize the training process. This guide will help you integrate TensorBoard with Horovod for better visualization of your distributed training runs.

Prerequisites

Before you start, make sure you have the following prerequisites installed:

Setup

  1. Prepare your TensorFlow model: Make sure your TensorFlow model is ready for distributed training. You can use the tf.distribute.Strategy API for this.

  2. Run your training script with Horovod: Use the horovod command to run your training script with Horovod. For example:

horovod tensorflow train --logdir /path/to/logdir --name my_training_run
  1. Start TensorBoard: Run TensorBoard to start the visualization tool. For example:
tensorboard --logdir /path/to/logdir
  1. Open TensorBoard: Open your web browser and go to the following URL:
http://localhost:6006

You should see the TensorBoard dashboard for your training run.

Visualization

TensorBoard provides various visualizations to help you understand the training process. Here are some of the key visualizations:

  • Summary of the metrics: This shows the metrics for each step of the training process.
  • Histograms: This shows the distribution of the metrics.
  • Graphs: This shows the graphs of the metrics over time.

Tips

  • You can use the --metrics flag to specify additional metrics to be logged.
  • You can use the --tensorboard flag to specify the TensorBoard URL.
  • You can use the --tensorboard-port flag to specify the TensorBoard port.

Learn More

For more information on Horovod and TensorBoard, please refer to the following resources:

[center] TensorBoard Integration