Tensorboard Class

Represents a TensorBoard instance for visualizing experiment performance and structure.

Initialize the Tensorboard.

Inheritance

builtins.object

Tensorboard

Constructor

Tensorboard(runs, local_root=None, port=6006, use_display_name=False)

Parameters

Name	Description
runs Required	list An empty list or a list of one or more experiment Run objects to attach to this Tensorboard instance.
local_root	str An optional local directory to store the run logs in. Default value: None
port	int The port to run this Tensorboard instance on. Default value: 6006
runs Required	list An empty list or a list of one or more experiment Run objects to attach to this Tensorboard instance.
local_root Required	str An optional local directory to store the run logs in.
port Required	int The port to run this Tensorboard instance on.
use_display_name	bool An optional parameter to load tensorboard logs using experiment run's display name instead of ID. Default value: False

Remarks

Create a Tensorboard instance to consume run history from machine learning experiments that output Tensorboard logs including those generated in TensorFlow, PyTorch, and Chainer. In these scenarios, the Tensorboard instance monitors the runs specified and downloads log data to the local_root location in real time after starting the instance with the start method. For long running processes, such as deep neural network training that could take days to complete, the Tensorboard instance will continue to download logs and persist them across multiple instantiations. Child runs of specified runs aren't monitored.

If a Tensorboard instance is created with no runs specified (an empty list), then the instance will work against any logs in local_root.

Start the Tensorboard instance with the start method. Stop the instance with the stop method when you are finished with it. For more information about using Tensorboard, see Visualize experiment runs and metrics with Tensorboard.

The following example shows how to create a Tensorboard instance to track run history from a Tensorflow experiment.

   from azureml.tensorboard import Tensorboard

   # The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here
   tb = Tensorboard([run])

   # If successful, start() returns a string with the URI of the instance.
   tb.start()

Full sample is available from https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.ipynb

Methods

start	Start the Tensorboard instance, and begin processing logs.
stop	Stop the Tensorboard instance.

start

Start the Tensorboard instance, and begin processing logs.

start(start_browser=False)

Parameters

Name	Description
start_browser	bool Specifies whether to open a browser upon starting the instance. Default value: False

Returns

Type	Description
str	The URL for accessing the Tensorboard instance.

stop

Stop the Tensorboard instance.

stop()

Returns

Type	Description
	None

Attributes

LOGS_ARTIFACT_PREFIX

LOGS_ARTIFACT_PREFIX = 'logs/'