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TensorBoard Streaming

Introduction

In this exercise, you will learn how to stream TensorBoard events from the clients to the server in order to visualize live training metrics from a central place on the server.

This exercise will be working with the tensorboard-streaming example in the examples folder, which builds upon :doc:`hello_pt` by adding TensorBoard streaming.

The setup of this exercise consists of one server and two clients.

Note

This exercise differs from :doc:`hello_pt`, as it uses the Learner API along with the LearnerExecutor. In short, the execution flow is abstracted away into the LearnerExecutor, allowing you to only need to implement the required methods in the Learner class. This will not be the focus of this guide, however you can learn more at :class:`Learner<nvflare.app_common.abstract.learner_spec.Learner>` and :class:`LearnerExecutor<nvflare.app_common.executors.learner_executor.LearnerExecutor>`.

Let's get started. Make sure you have an environment with NVIDIA FLARE installed as described in :ref:`getting_started`. First clone the repo:

$ git clone https://github.com/NVIDIA/NVFlare.git

Now remember to activate your NVIDIA FLARE Python virtual environment from the installation guide. And install the required dependencies.

(nvflare-env) $ python3 -m pip install -r

Adding TensorBoard Streaming to Configurations

Inside the config folder there are two files, config_fed_client.json and config_fed_server.json.

.. literalinclude:: ../../examples/advanced/experiment-tracking/tensorboard-streaming/jobs/tensorboard-streaming/app/config/config_fed_client.json
   :language: json
   :linenos:
   :caption: config_fed_client.json

Take a look at the components section of the client config at line 24. The first component is the pt_learner which contains the initialization, training, and validation logic. pt_learner.py is where we will add our TensorBoard streaming changes.

Next we have the :class:`AnalyticsSender<nvflare.app_common.widgets.streaming.AnalyticsSender>`, which implements some common methods that follow the signatures from the PyTorch SummaryWriter. This makes it easy for the pt_learner to log metrics and send events.

Finally, we have the :class:`ConvertToFedEvent<nvflare.app_common.widgets.convert_to_fed_event.ConvertToFedEvent>`, which converts local events to federated events. This changes the event analytix_log_stats into a fed event fed.analytix_log_stats, which will then be streamed from the clients to the server.

.. literalinclude:: ../../examples/advanced/experiment-tracking/tensorboard-streaming/jobs/tensorboard-streaming/app/config/config_fed_server.json
   :language: json
   :linenos:
   :caption: config_fed_server.json

Under the component section in the server config, we have the :class:`TBAnalyticsReceiver<nvflare.app_common.pt.tb_receiver.TBAnalyticsReceiver>` of type :class:`AnalyticsReceiver<nvflare.app_common.widgets.streaming.AnalyticsReceiver>`.

This component receives TensorBoard events from the clients and saves them to a specified folder (default tb_events) under the server's run folder.

Notice how the accepted event type "fed.analytix_log_stats" matches the output of :class:`ConvertToFedEvent<nvflare.app_common.widgets.convert_to_fed_event.ConvertToFedEvent>` in the client config.

Adding TensorBoard Streaming to your Code

In this exercise, all of the TensorBoard code additions will be made in pt_learner.py.

First we must initialize our TensorBoard writer to the AnalyticsSender we defined in the client config:

.. literalinclude:: ../../examples/advanced/experiment-tracking/tensorboard-streaming/jobs/tensorboard-streaming/app/custom/pt_learner.py
   :language: python
   :lines: 103-106
   :lineno-start: 103
   :linenos:

The LearnerExecutor passes in the component dictionary into the parts parameter of initialize(). We can then access the AnalyticsSender component we defined in config_fed_client.json by using the self.analytic_sender_id as the key in the parts dictionary. Note that self.analytic_sender_id defaults to "analytic_sender", but we can also define it in the client config to be passed into the constructor.

Now that our TensorBoard writer is set to AnalyticsSender, we can write and stream training metrics to the server in local_train():

.. literalinclude:: ../../examples/advanced/experiment-tracking/tensorboard-streaming/jobs/tensorboard-streaming/app/custom/pt_learner.py
   :language: python
   :lines: 144-174
   :lineno-start: 144
   :linenos:

We use add_scalar(tag, scalar, global_step) on line 170 to send training loss metrics, while on line 174 we send the validation accuracy at the end of each epoch.

You can learn more about other supported writer methods in :class:`AnalyticsSender<nvflare.app_common.widgets.streaming.AnalyticsSender>`.

Train the Model, Federated!

Viewing the TensorBoard Dashboard during Training

On the client side, the AnalyticsSender works as a TensorBoard SummaryWriter. Instead of writing to TB files, it actually generates NVFLARE events of type analytix_log_stats.

The ConvertToFedEvent widget will turn the event analytix_log_stats into a fed event fed.analytix_log_stats, which will be delivered to the server side.

On the server side, the TBAnalyticsReceiver is configured to process fed.analytix_log_stats events, which writes received TB data into appropriate TB files on the server (defaults to server/[JOB ID]/tb_events).

To view training metrics that are being streamed to the server, run:

tensorboard --logdir=poc/server/[JOB ID]/tb_events

Note

if the server is running on a remote machine, use port forwarding to view the TensorBoard dashboard in a browser. For example:

ssh -L {local_machine_port}:127.0.0.1:6006 user@server_ip

Attention!

The server/[JOB ID] folder only exists when job is running. After the job is finished, please use download_job [JOB ID] to get the workspace data as explained below.

Congratulations!

Now you will be able to see the live training metrics of each client from a central place on the server.

The full source code for this exercise can be found in examples/advanced/experiment-tracking/tensorboard-streaming.