minort tweaks to make client.py compatible w/ Flower and Flower Nex…

…t; updates to README

examples/quickstart-tensorflow/README.md

@@ -55,21 +55,19 @@ ______________________________________________________________________
 Afterward, you are ready to start the Flower server as well as the clients. You can simply start the server in a terminal as follows:
 
 ```shell
-poetry run python3 server.py
+python3 server.py
 ```
 
 Now you are ready to start the Flower clients which will participate in the learning. To do so simply open two more terminals and run the following command in each:
 
 ```shell
-poetry run python3 client.py
+python3 client.py --partition-id 0
 ```
 
-Alternatively, you can run all of it in one shell as follows:
+Start client 2 in the second terminal:
 
 ```shell
-poetry run python3 server.py &
-poetry run python3 client.py &
-poetry run python3 client.py
+python3 client.py --partition-id 1
 ```
 
 You will see that Keras is starting a federated training. Have a look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-tensorflow) for a detailed explanation. You can add `steps_per_epoch=3` to `model.fit()` if you just want to evaluate that everything works without having to wait for the client-side training to finish (this will save you a lot of time during development).
@@ -86,7 +84,7 @@ flower-superlink --insecure
 
 ### 2. Start the long-running Flower clients (SuperNodes)
 
-Start 2 long-running Flower clients in 2 separate terminal windows, using:
+Start 2 Flower \`SuperNodes in 2 separate terminal windows, using:
 
 ```bash
 flower-client-app client:app --insecure
@@ -99,15 +97,3 @@ With both the long-running server (SuperLink) and two clients (SuperNode) up and
 ```bash
 flower-server-app server:app --insecure
 ```
-
-Or, to try the workflow example, run:
-
-```bash
-flower-server-app server_workflow:app --insecure
-```
-
-Or, to try the custom server function example, run:
-
-```bash
-flower-server-app server_custom:app --insecure
-```

examples/quickstart-tensorflow/client.py

@@ -14,11 +14,11 @@
     "--partition-id",
     type=int,
     choices=[0, 1, 2],
-    required=True,
+    default=0,
     help="Partition of the dataset (0,1 or 2). "
     "The dataset is divided into 3 partitions created artificially.",
 )
-args = parser.parse_args()
+args, _ = parser.parse_known_args()
 
 # Load model and data (MobileNetV2, CIFAR-10)
 model = tf.keras.applications.MobileNetV2((32, 32, 3), classes=10, weights=None)
@@ -36,7 +36,7 @@
 
 
 # Define Flower client
-class CifarClient(NumPyClient):
+class FlowerClient(NumPyClient):
     def get_parameters(self, config):
         return model.get_weights()