Hello PyTorch with TensorBoard


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 hello-pt-tb application in the examples folder, which builds upon Hello PyTorch by adding TensorBoard streaming.

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


This exercise differs from Hello PyTorch, 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 Learner and LearnerExecutor.

Let’s get started. Make sure you have an environment with NVIDIA FLARE installed as described in quickstart guide. 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. Since you will use PyTorch, torchvision, and TensorBoard for this exercise, let’s go ahead and install these libraries:

(nvflare-env) $ python3 -m pip install torch torchvision tensorboard

Adding TensorBoard Streaming to Configurations

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

 2  "format_version": 2,
 4  "executors": [
 5    {
 6      "tasks": [
 7        "train",
 8        "submit_model",
 9        "validate"
10      ],
11      "executor": {
12        "id": "Executor",
13        "path": "nvflare.app_common.executors.learner_executor.LearnerExecutor",
14        "args": {
15          "learner_id": "pt_learner"
16        }
17      }
18    }
19  ],
20  "task_result_filters": [
21  ],
22  "task_data_filters": [
23  ],
24  "components": [
25    {
26      "id": "pt_learner",
27      "path": "pt_learner.PTLearner",
28      "args": {
29        "lr": 0.01,
30        "epochs": 5,
31        "analytic_sender_id": "analytic_sender"
32      }
33    },
34    {
35      "id": "analytic_sender",
36      "name": "AnalyticsSender",
37      "args": {"event_type": "analytix_log_stats"}
38    },
39    {
40      "id": "event_to_fed",
41      "name": "ConvertToFedEvent",
42      "args": {"events_to_convert": ["analytix_log_stats"], "fed_event_prefix": "fed."}
43    }
44  ]

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 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 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.

 2  "format_version": 2,
 4  "server": {
 5    "heart_beat_timeout": 600
 6  },
 7  "task_data_filters": [],
 8  "task_result_filters": [],
 9  "components": [
10    {
11      "id": "persistor",
12      "path": "nvflare.app_common.pt.pt_file_model_persistor.PTFileModelPersistor",
13      "args": {
14        "model": {
15          "path": "simple_network.SimpleNetwork"
16        }
17      }
18    },
19    {
20      "id": "shareable_generator",
21      "path": "nvflare.app_common.shareablegenerators.full_model_shareable_generator.FullModelShareableGenerator",
22      "args": {}
23    },
24    {
25      "id": "aggregator",
26      "path": "nvflare.app_common.aggregators.intime_accumulate_model_aggregator.InTimeAccumulateWeightedAggregator",
27      "args": {
28        "expected_data_kind": "WEIGHTS"
29      }
30    },
31    {
32      "id": "model_locator",
33      "path": "nvflare.app_common.pt.pt_file_model_locator.PTFileModelLocator",
34      "args": {
35        "pt_persistor_id": "persistor"
36      }
37    },
38    {
39      "id": "json_generator",
40      "path": "nvflare.app_common.widgets.validation_json_generator.ValidationJsonGenerator",
41      "args": {}
42    },
43    {
44      "id": "tb_analytics_receiver",
45      "name": "TBAnalyticsReceiver",
46      "args": {"events": ["fed.analytix_log_stats"]}
47    }
48  ],
49  "workflows": [
50      {
51        "id": "scatter_and_gather",
52        "name": "ScatterAndGather",
53        "args": {
54            "min_clients" : 2,
55            "num_rounds" : 1,
56            "start_round": 0,
57            "wait_time_after_min_received": 10,
58            "aggregator_id": "aggregator",
59            "persistor_id": "persistor",
60            "shareable_generator_id": "shareable_generator",
61            "train_task_name": "train",
62            "train_timeout": 0
63        }
64      },
65      {
66        "id": "cross_site_validate",
67        "name": "CrossSiteModelEval",
68        "args": {
69          "model_locator_id": "model_locator"
70        }
71      }
72  ]

Under the component section in the server config, we have the TBAnalyticsReceiver of type 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 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:

61        self.train_loader = None
62        self.train_loader = DataLoader(self.train_dataset, batch_size=32, shuffle=True)
63        self.n_iterations = len(self.train_loader)
65        # Create CIFAR10 dataset for validation.

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():

127        # Check the abort_signal after training.
128        # local_train returns early if abort_signal is triggered.
129        if abort_signal.triggered:
130            return make_reply(ReturnCode.TASK_ABORTED)
132        # Save the local model after training.
133        self.save_local_model(fl_ctx)
135        # Get the new state dict and send as weights
136        new_weights = self.model.state_dict()
137        new_weights = {k: v.cpu().numpy() for k, v in new_weights.items()}
139        outgoing_dxo = DXO(
140            data_kind=DataKind.WEIGHTS, data=new_weights, meta={MetaKey.NUM_STEPS_CURRENT_ROUND: self.n_iterations}
141        )
142        return outgoing_dxo.to_shareable()
144    def local_train(self, fl_ctx, abort_signal):
145        # Basic training
146        for epoch in range(self.epochs):
147            self.model.train()
148            running_loss = 0.0
149            for i, batch in enumerate(self.train_loader):
150                if abort_signal.triggered:
151                    return
153                images, labels = batch[0].to(self.device), batch[1].to(self.device)
154                self.optimizer.zero_grad()
156                predictions = self.model(images)
157                cost = self.loss(predictions, labels)
158                cost.backward()
159                self.optimizer.step()

We use add_scalar(tag, scalar, global_step) on line 155 to send training loss metrics, while on line 159 we send the validation accuracy at the end of each epoch.

You can learn more about other supported writer methods in AnalyticsSender.

Train the Model, Federated!

Now you can use admin command prompt to submit and start this example job. To do this on a proof of concept local FL system, follow the sections Setting Up the Application Environment in POC Mode and Starting the Application Environment in POC Mode if you have not already.

Running the FL System

With the admin client command prompt successfully connected and logged in, enter the command below.

> submit_job hello-pt-tb

Pay close attention to what happens in each of four terminals. You can see how the admin submits the job to the server and how the JobRunner on the server automatically picks up the job to deploy and start the run.

This command uploads the job configuration from the admin client to the server. A job id will be returned, and we can use that id to access job information.


If we use submit_job [app] then that app will be treated as a single app job.

From time to time, you can issue check_status server in the admin client to check the entire training progress.

You should now see how the training does in the very first terminal (the one that started the server).

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


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}: user@server_ip


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.

Accessing the results

The results of each job will usually be stored inside the server side workspace.

Please refer to access server-side workspace for accessing the server side workspace.

Shutdown FL system

Once the FL run is complete and the server has successfully aggregated the client’s results after all the rounds, and cross site model evaluation is finished, run the following commands in the fl_admin to shutdown the system (while inputting admin when prompted with password):

> shutdown client
> shutdown server
> bye


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/hello-pt-tb.