Add missing client api test jobs

tests/integration_test/data/jobs/decorator/app/config/config_fed_client.conf

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+{
+  format_version = 2
+  app_script = "cifar10_structured_fl.py"
+  app_config = ""
+  executors = [
+    {
+      tasks = [
+        "train"
+      ]
+      executor {
+        path = "nvflare.app_opt.pt.client_api_launcher_executor.PTClientAPILauncherExecutor"
+        args {
+          launcher_id = "launcher"
+          pipe_id = "pipe"
+          heartbeat_timeout = 60
+          params_exchange_format = "pytorch"
+          params_transfer_type = "DIFF"
+          train_with_evaluation = true
+        }
+      }
+    }
+  ]
+  task_data_filters = []
+  task_result_filters = []
+  components = [
+    {
+      id = "launcher"
+      path = "nvflare.app_common.launchers.subprocess_launcher.SubprocessLauncher"
+      args {
+        script = "python3 -u custom/{app_script}  {app_config} "
+        launch_once = true
+      }
+    }
+    {
+      id = "pipe"
+      path = "nvflare.fuel.utils.pipe.cell_pipe.CellPipe"
+      args {
+        mode = "PASSIVE"
+        site_name = "{SITE_NAME}"
+        token = "{JOB_ID}"
+        root_url = "{ROOT_URL}"
+        secure_mode = "{SECURE_MODE}"
+        workspace_dir = "{WORKSPACE}"
+      }
+    }
+    {
+      id = "metrics_pipe"
+      path = "nvflare.fuel.utils.pipe.cell_pipe.CellPipe"
+      args {
+        mode = "PASSIVE"
+        site_name = "{SITE_NAME}"
+        token = "{JOB_ID}"
+        root_url = "{ROOT_URL}"
+        secure_mode = "{SECURE_MODE}"
+        workspace_dir = "{WORKSPACE}"
+      }
+    }
+    {
+      id = "metric_relay"
+      path = "nvflare.app_common.widgets.metric_relay.MetricRelay"
+      args {
+        pipe_id = "metrics_pipe"
+        event_type = "fed.analytix_log_stats"
+        read_interval = 0.1
+      }
+    }
+    {
+      id = "config_preparer"
+      path = "nvflare.app_common.widgets.external_configurator.ExternalConfigurator"
+      args {
+        component_ids = [
+          "metric_relay"
+        ]
+      }
+    }
+  ]
+}

tests/integration_test/data/jobs/decorator/app/config/config_fed_server.conf

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+{
+  format_version = 2
+  task_data_filters = []
+  task_result_filters = []
+  model_class_path = "net.Net"
+  workflows = [
+    {
+      id = "scatter_and_gather"
+      path = "nvflare.app_common.workflows.scatter_and_gather.ScatterAndGather"
+      args {
+        min_clients = 2
+        num_rounds = 2
+        start_round = 0
+        wait_time_after_min_received = 0
+        aggregator_id = "aggregator"
+        persistor_id = "persistor"
+        shareable_generator_id = "shareable_generator"
+        train_task_name = "train"
+        train_timeout = 0
+      }
+    }
+  ]
+  components = [
+    {
+      id = "persistor"
+      path = "nvflare.app_opt.pt.file_model_persistor.PTFileModelPersistor"
+      args {
+        model {
+          path = "{model_class_path}"
+        }
+      }
+    }
+    {
+      id = "shareable_generator"
+      path = "nvflare.app_common.shareablegenerators.full_model_shareable_generator.FullModelShareableGenerator"
+      args {}
+    }
+    {
+      id = "aggregator"
+      path = "nvflare.app_common.aggregators.intime_accumulate_model_aggregator.InTimeAccumulateWeightedAggregator"
+      args {
+        expected_data_kind = "WEIGHT_DIFF"
+      }
+    }
+    {
+      id = "model_selector"
+      path = "nvflare.app_common.widgets.intime_model_selector.IntimeModelSelector"
+      args {
+        key_metric = "accuracy"
+      }
+    }
+    {
+      id = "receiver"
+      path = "nvflare.app_opt.tracking.tb.tb_receiver.TBAnalyticsReceiver"
+      args {
+        events = [
+          "fed.analytix_log_stats"
+        ]
+      }
+    }
+  ]
+}

tests/integration_test/data/jobs/decorator/app/custom/cifar10_structured_fl.py

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+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+from net import Net
+
+# (1) import nvflare client API
+import nvflare.client as flare
+
+# (optional) set a fix place so we don't need to download everytime
+DATASET_PATH = "/tmp/nvflare/data"
+# (optional) We change to use GPU to speed things up.
+# if you want to use CPU, change DEVICE="cpu"
+DEVICE = "cuda:0"
+PATH = "./cifar_net.pth"
+
+
+def main():
+    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+
+    batch_size = 4
+
+    trainset = torchvision.datasets.CIFAR10(root=DATASET_PATH, train=True, download=True, transform=transform)
+    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)
+
+    testset = torchvision.datasets.CIFAR10(root=DATASET_PATH, train=False, download=True, transform=transform)
+    testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=2)
+
+    net = Net()
+
+    # (2) initializes NVFlare client API
+    flare.init()
+
+    # (3) decorates with flare.train and load model from the first argument
+    # wraps training logic into a method
+    @flare.train
+    def train(input_model=None, total_epochs=2, lr=0.001):
+        net.load_state_dict(input_model.params)
+
+        criterion = nn.CrossEntropyLoss()
+        optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9)
+
+        # (optional) use GPU to speed things up
+        net.to(DEVICE)
+        # (optional) calculate total steps
+        steps = total_epochs * len(trainloader)
+
+        for epoch in range(total_epochs):  # loop over the dataset multiple times
+
+            running_loss = 0.0
+            for i, data in enumerate(trainloader, 0):
+                # get the inputs; data is a list of [inputs, labels]
+                # (optional) use GPU to speed things up
+                inputs, labels = data[0].to(DEVICE), data[1].to(DEVICE)
+
+                # zero the parameter gradients
+                optimizer.zero_grad()
+
+                # forward + backward + optimize
+                outputs = net(inputs)
+                loss = criterion(outputs, labels)
+                loss.backward()
+                optimizer.step()
+
+                # print statistics
+                running_loss += loss.item()
+                if i % 2000 == 1999:  # print every 2000 mini-batches
+                    print(f"[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}")
+                    running_loss = 0.0
+
+        print("Finished Training")
+
+        torch.save(net.state_dict(), PATH)
+
+        # (4) construct trained FL model
+        output_model = flare.FLModel(params=net.cpu().state_dict(), meta={"NUM_STEPS_CURRENT_ROUND": steps})
+        return output_model
+
+    # (5) decorates with flare.evaluate and load model from the first argument
+    @flare.evaluate
+    def fl_evaluate(input_model=None):
+        return evaluate(input_weights=input_model.params)
+
+    # wraps evaluate logic into a method
+    def evaluate(input_weights):
+        net.load_state_dict(input_weights)
+        # (optional) use GPU to speed things up
+        net.to(DEVICE)
+
+        correct = 0
+        total = 0
+        # since we're not training, we don't need to calculate the gradients for our outputs
+        with torch.no_grad():
+            for data in testloader:
+                # (optional) use GPU to speed things up
+                images, labels = data[0].to(DEVICE), data[1].to(DEVICE)
+                # calculate outputs by running images through the network
+                outputs = net(images)
+                # the class with the highest energy is what we choose as prediction
+                _, predicted = torch.max(outputs.data, 1)
+                total += labels.size(0)
+                correct += (predicted == labels).sum().item()
+
+        # return evaluation metrics
+        return 100 * correct // total
+
+    while flare.is_running():
+        # (6) receives FLModel from NVFlare
+        input_model = flare.receive()
+        print(f"current_round={input_model.current_round}")
+
+        # (7) call fl_evaluate method before training
+        #       to evaluate on the received/aggregated model
+        global_metric = fl_evaluate(input_model)
+        print(f"Accuracy of the global model on the 10000 test images: {global_metric} %")
+        # call train method
+        train(input_model, total_epochs=2, lr=0.001)
+        # call evaluate method
+        metric = evaluate(input_weights=torch.load(PATH))
+        print(f"Accuracy of the trained model on the 10000 test images: {metric} %")
+
+
+if __name__ == "__main__":
+    main()

tests/integration_test/data/jobs/decorator/app/custom/net.py

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+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = torch.flatten(x, 1)  # flatten all dimensions except batch
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x

tests/integration_test/data/jobs/decorator/meta.conf

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+{
+  name = "decorator"
+  resource_spec {}
+  deploy_map {
+    app = [
+      "@ALL"
+    ]
+  }
+  min_clients = 2
+  mandatory_clients = []
+}