tensorflow-serving tensorboard_config

The tensorflow-serving package does not have a specific tensorboard_config module. However, TensorFlow Serving does provide support for TensorBoard integration.

To configure TensorBoard in TensorFlow Serving, you need to specify the --enable_batching and --monitoring_config_file flags when starting the TensorFlow Serving server.

Here is an example command to start the TensorFlow Serving server with TensorBoard integration:

tensorflow_model_server \
  --port=8500 \
  --model_name=my_model \
  --model_base_path=/path/to/saved_model \
  --enable_batching \
  --monitoring_config_file=/path/to/monitoring_config.txt

In the monitoring_config.txt file, you can specify the paths for the metrics and logs that you want to monitor with TensorBoard. Here is an example configuration file:

model_metrics {
  model_name: "my_model"
  model_version: -1
  metrics_spec {
    metrics {
      name: "accuracy"
      threshold {
        value: 0.8
      }
    }
  }
}

log_collector_config {
  log_paths: "/path/to/logs/**/*.log"
}

This configuration file specifies that you want to monitor the "accuracy" metric with a threshold of 0.8, and collect logs from the specified log paths.

Once the TensorFlow Serving server is running with these configurations, you can launch TensorBoard and point it to the server's monitoring endpoint to visualize the metrics and logs.

Note: The above example assumes you have already exported a TensorFlow model as a SavedModel and it is located at /path/to/saved_model. Adjust the paths and configurations according to your setup