Rename examples using dash instead of underscore

README.md

@@ -113,25 +113,25 @@ Several code examples show different usage scenarios of Flower (in combination w
 
 Quickstart examples:
 
-* [Quickstart (TensorFlow)](https://github.com/adap/flower/tree/main/examples/quickstart_tensorflow)
-* [Quickstart (PyTorch)](https://github.com/adap/flower/tree/main/examples/quickstart_pytorch)
-* [Quickstart (Hugging Face)](https://github.com/adap/flower/tree/main/examples/quickstart_huggingface)
-* [Quickstart (PyTorch Lightning)](https://github.com/adap/flower/tree/main/examples/quickstart_pytorch_lightning)
-* [Quickstart (fastai)](https://github.com/adap/flower/tree/main/examples/quickstart_fastai)
-* [Quickstart (Pandas)](https://github.com/adap/flower/tree/main/examples/quickstart_pandas)
-* [Quickstart (MXNet)](https://github.com/adap/flower/tree/main/examples/quickstart_mxnet)
-* [Quickstart (JAX)](https://github.com/adap/flower/tree/main/examples/quickstart_jax)
+* [Quickstart (TensorFlow)](https://github.com/adap/flower/tree/main/examples/quickstart-tensorflow)
+* [Quickstart (PyTorch)](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch)
+* [Quickstart (Hugging Face)](https://github.com/adap/flower/tree/main/examples/quickstart-huggingface)
+* [Quickstart (PyTorch Lightning)](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch_lightning)
+* [Quickstart (fastai)](https://github.com/adap/flower/tree/main/examples/quickstart-fastai)
+* [Quickstart (Pandas)](https://github.com/adap/flower/tree/main/examples/quickstart-pandas)
+* [Quickstart (MXNet)](https://github.com/adap/flower/tree/main/examples/quickstart-mxnet)
+* [Quickstart (JAX)](https://github.com/adap/flower/tree/main/examples/quickstart-jax)
 * [Quickstart (scikit-learn)](https://github.com/adap/flower/tree/main/examples/sklearn-logreg-mnist)
 * [Quickstart (Android [TFLite])](https://github.com/adap/flower/tree/main/examples/android)
 * [Quickstart (iOS [CoreML])](https://github.com/adap/flower/tree/main/examples/ios)
 
 Other [examples](https://github.com/adap/flower/tree/main/examples):
 
-* [Raspberry Pi & Nvidia Jetson Tutorial](https://github.com/adap/flower/tree/main/examples/embedded_devices)
-* [PyTorch: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/pytorch_from_centralized_to_federated)
-* [MXNet: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/mxnet_from_centralized_to_federated)
-* [Advanced Flower with TensorFlow/Keras](https://github.com/adap/flower/tree/main/examples/advanced_tensorflow)
-* [Advanced Flower with PyTorch](https://github.com/adap/flower/tree/main/examples/advanced_pytorch)
+* [Raspberry Pi & Nvidia Jetson Tutorial](https://github.com/adap/flower/tree/main/examples/embedded-devices)
+* [PyTorch: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/pytorch-from-centralized-to-federated)
+* [MXNet: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/mxnet-from-centralized-to-federated)
+* [Advanced Flower with TensorFlow/Keras](https://github.com/adap/flower/tree/main/examples/advanced-tensorflow)
+* [Advanced Flower with PyTorch](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)
 * Single-Machine Simulation of Federated Learning Systems ([PyTorch](https://github.com/adap/flower/tree/main/examples/simulation_pytorch)) ([Tensorflow](https://github.com/adap/flower/tree/main/examples/simulation_tensorflow))
 
 ## Community

doc/source/evaluation.rst

@@ -174,4 +174,4 @@ Model parameters can also be evaluated during training. :code:`Client.fit` can r
 Full Code Example
 -----------------
 
-For a full code example that uses both centralized and federated evaluation, see the *Advanced TensorFlow Example* (the same approach can be applied to workloads implemented in any other framework): https://github.com/adap/flower/tree/main/examples/advanced_tensorflow
+For a full code example that uses both centralized and federated evaluation, see the *Advanced TensorFlow Example* (the same approach can be applied to workloads implemented in any other framework): https://github.com/adap/flower/tree/main/examples/advanced-tensorflow

doc/source/example-jax-from-centralized-to-federated.rst

@@ -3,7 +3,7 @@ Example: JAX - Run JAX Federated
 
 This tutorial will show you how to use Flower to build a federated version of an existing JAX workload.
 We are using JAX to train a linear regression model on a scikit-learn dataset.
-We will structure the example similar to our `PyTorch - From Centralized To Federated <https://github.com/adap/flower/blob/main/examples/pytorch_from_centralized_to_federated>`_ walkthrough.
+We will structure the example similar to our `PyTorch - From Centralized To Federated <https://github.com/adap/flower/blob/main/examples/pytorch-from-centralized-to-federated>`_ walkthrough.
 First, we build a centralized training approach based on the `Linear Regression with JAX <https://coax.readthedocs.io/en/latest/examples/linear_regression/jax.html>`_ tutorial`.
 Then, we build upon the centralized training code to run the training in a federated fashion.
 
@@ -276,7 +276,7 @@ in each window (make sure that the server is still running before you do so) and
 Next Steps
 ----------
 
-The source code of this example was improved over time and can be found here: `Quickstart JAX <https://github.com/adap/flower/blob/main/examples/quickstart_jax>`_.
+The source code of this example was improved over time and can be found here: `Quickstart JAX <https://github.com/adap/flower/blob/main/examples/quickstart-jax>`_.
 Our example is somewhat over-simplified because both clients load the same dataset.
 
 You're now prepared to explore this topic further. How about using a more sophisticated model or using a different dataset? How about adding more clients?

doc/source/example-mxnet-walk-through.rst

@@ -3,7 +3,7 @@ Example: MXNet - Run MXNet Federated
 
 This tutorial will show you how to use Flower to build a federated version of an existing MXNet workload.
 We are using MXNet to train a Sequential model on the MNIST dataset.
-We will structure the example similar to our `PyTorch - From Centralized To Federated <https://github.com/adap/flower/blob/main/examples/pytorch_from_centralized_to_federated>`_ walkthrough. MXNet and PyTorch are very similar and a very good comparison between MXNet and PyTorch is given `here <https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials/getting-started/to-mxnet/pytorch.html>`_.
+We will structure the example similar to our `PyTorch - From Centralized To Federated <https://github.com/adap/flower/blob/main/examples/pytorch-from-centralized-to-federated>`_ walkthrough. MXNet and PyTorch are very similar and a very good comparison between MXNet and PyTorch is given `here <https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials/getting-started/to-mxnet/pytorch.html>`_.
 First, we build a centralized training approach based on the `Handwritten Digit Recognition <https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_ tutorial.
 Then, we build upon the centralized training code to run the training in a federated fashion.
 
@@ -355,6 +355,6 @@ in each window (make sure that the server is still running before you do so) and
 Next Steps
 ----------
 
-The full source code for this example: `MXNet: From Centralized To Federated (Code) <https://github.com/adap/flower/blob/main/examples/mxnet_from_centralized_to_federated>`_.
+The full source code for this example: `MXNet: From Centralized To Federated (Code) <https://github.com/adap/flower/blob/main/examples/mxnet-from-centralized-to-federated>`_.
 Our example is of course somewhat over-simplified because both clients load the exact same dataset, which isn't realistic.
 You're now prepared to explore this topic further. How about using a CNN or using a different dataset? How about adding more clients?

doc/source/example-pytorch-from-centralized-to-federated.rst

@@ -309,6 +309,6 @@ in each window (make sure that the server is running before you do so) and see y
 Next Steps
 ----------
 
-The full source code for this example: `PyTorch: From Centralized To Federated (Code) <https://github.com/adap/flower/blob/main/examples/pytorch_from_centralized_to_federated>`_.
+The full source code for this example: `PyTorch: From Centralized To Federated (Code) <https://github.com/adap/flower/blob/main/examples/pytorch-from-centralized-to-federated>`_.
 Our example is, of course, somewhat over-simplified because both clients load the exact same dataset, which isn't realistic.
 You're now prepared to explore this topic further. How about using different subsets of CIFAR-10 on each client? How about adding more clients?

doc/source/example-walkthrough-pytorch-mnist.rst

@@ -72,7 +72,7 @@ Inside the server helper script *run-server.sh* you will find the following code
 
 .. code-block:: bash 
 
-    python -m flwr_example.quickstart_pytorch.server
+    python -m flwr_example.quickstart-pytorch.server
 
 
 We can go a bit deeper and see that :code:`server.py` simply launches a server that will coordinate three rounds of training.
@@ -92,7 +92,7 @@ Next, let's take a look at the *run-clients.sh* file. You will see that it conta
 
 .. code-block:: bash 
 
-    python -m flwr_example.quickstart_pytorch.client \
+    python -m flwr_example.quickstart-pytorch.client \
       --cid=$i \
       --server_address=$SERVER_ADDRESS \
       --nb_clients=$NUM_CLIENTS 
@@ -101,7 +101,7 @@ Next, let's take a look at the *run-clients.sh* file. You will see that it conta
 * **sever_address**: String that identifies IP and port of the server. 
 * **nb_clients**: This defines the number of clients being created. This piece of information is not required by the client, but it helps us partition the original MNIST dataset to make sure that every client is working on unique subsets of both *training* and *test* sets.
 
-Again, we can go deeper and look inside :code:`flwr_example/quickstart_pytorch/client.py`. 
+Again, we can go deeper and look inside :code:`flwr_example/quickstart-pytorch/client.py`. 
 After going through the argument parsing code at the beginning of our :code:`main` function, you will find a call to :code:`mnist.load_data`. This function is responsible for partitioning the original MNIST datasets (*training* and *test*) and returning a :code:`torch.utils.data.DataLoader` s for each of them.
 We then instantiate a :code:`PytorchMNISTClient` object with our client ID, our DataLoaders, the number of epochs in each round, and which device we want to use for training (CPU or GPU).
 
@@ -122,7 +122,7 @@ The :code:`PytorchMNISTClient` object when finally passed to :code:`fl.client.st
 A Closer Look
 -------------
 
-Now, let's look closely into the :code:`PytorchMNISTClient` inside :code:`flwr_example.quickstart_pytorch.mnist` and see what it is doing:
+Now, let's look closely into the :code:`PytorchMNISTClient` inside :code:`flwr_example.quickstart-pytorch.mnist` and see what it is doing:
 
 .. code-block:: python
 
@@ -241,7 +241,7 @@ The first thing to notice is that :code:`PytorchMNISTClient` instantiates a CNN
         self.model = MNISTNet().to(device)
     ...
 
-The code for the CNN is available under :code:`quickstart_pytorch.mnist` and it is reproduced below. It is the same network found in `Basic MNIST Example <https://github.com/pytorch/examples/tree/master/mnist>`_.
+The code for the CNN is available under :code:`quickstart-pytorch.mnist` and it is reproduced below. It is the same network found in `Basic MNIST Example <https://github.com/pytorch/examples/tree/master/mnist>`_.
 
 .. code-block:: python
 
@@ -314,7 +314,7 @@ The second thing to notice is that :code:`PytorchMNISTClient` class inherits fro
 
 When comparing the abstract class to its derived class :code:`PytorchMNISTClient` you will notice that :code:`fit` calls a :code:`train` function and that :code:`evaluate` calls a :code:`test`: function. 
 
-These functions can both be found inside the same :code:`quickstart_pytorch.mnist` module:
+These functions can both be found inside the same :code:`quickstart-pytorch.mnist` module:
 
 .. code-block:: python
 

doc/source/examples.rst

@@ -22,7 +22,7 @@ Quickstart TensorFlow/Keras
 The TensorFlow/Keras quickstart example shows CIFAR-10 image classification
 with MobileNetV2:
 
-- `Quickstart TensorFlow (Code) <https://github.com/adap/flower/tree/main/examples/quickstart_tensorflow>`_
+- `Quickstart TensorFlow (Code) <https://github.com/adap/flower/tree/main/examples/quickstart-tensorflow>`_
 - `Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/quickstart-tensorflow.html>`_
 - `Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11-federated-learning-in-less-than-20-lines-of-code>`_
 
@@ -33,7 +33,7 @@ Quickstart PyTorch
 The PyTorch quickstart example shows CIFAR-10 image classification
 with a simple Convolutional Neural Network:
 
-- `Quickstart PyTorch (Code) <https://github.com/adap/flower/tree/main/examples/quickstart_pytorch>`_
+- `Quickstart PyTorch (Code) <https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_
 - `Quickstart PyTorch (Tutorial) <https://flower.dev/docs/quickstart-pytorch.html>`_
 
 
@@ -42,7 +42,7 @@ PyTorch: From Centralized To Federated
 
 This example shows how a regular PyTorch project can be federated using Flower:
 
-- `PyTorch: From Centralized To Federated (Code) <https://github.com/adap/flower/tree/main/examples/pytorch_from_centralized_to_federated>`_
+- `PyTorch: From Centralized To Federated (Code) <https://github.com/adap/flower/tree/main/examples/pytorch-from-centralized-to-federated>`_
 - `PyTorch: From Centralized To Federated (Tutorial) <https://flower.dev/docs/example-pytorch-from-centralized-to-federated.html>`_
 
 
@@ -51,8 +51,8 @@ Federated Learning on Raspberry Pi and Nvidia Jetson
 
 This example shows how Flower can be used to build a federated learning system that run across Raspberry Pi and Nvidia Jetson:
 
-- `Federated Learning on Raspberry Pi and Nvidia Jetson (Code) <https://github.com/adap/flower/tree/main/examples/embedded_devices>`_
-- `Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) <https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_
+- `Federated Learning on Raspberry Pi and Nvidia Jetson (Code) <https://github.com/adap/flower/tree/main/examples/embedded-devices>`_
+- `Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) <https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded-devices_with_flower>`_
 
 
 

doc/source/faq.rst

@@ -12,7 +12,7 @@ This page collects answers to commonly asked questions about Federated Learning
 
 .. dropdown:: :fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?
 
-    Find the `blog post about federated learning on embedded device here <https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_ and the corresponding `GitHub code example <https://github.com/adap/flower/tree/main/examples/embedded_devices>`_. 
+    Find the `blog post about federated learning on embedded device here <https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_ and the corresponding `GitHub code example <https://github.com/adap/flower/tree/main/examples/embedded-devices>`_. 
 
 .. dropdown:: :fa:`eye,mr-1` Does Flower support federated learning on Android devices?
 

doc/source/fedbn-example-pytorch-from-centralized-to-federated.rst

@@ -93,6 +93,6 @@ in each window (make sure that the server is still running before you do so) and
 Next Steps
 ----------
 
-The full source code for this example can be found `here <https://github.com/adap/flower/blob/main/examples/pytorch_from_centralized_to_federated>`_.
+The full source code for this example can be found `here <https://github.com/adap/flower/blob/main/examples/pytorch-from-centralized-to-federated>`_.
 Our example is of course somewhat over-simplified because both clients load the exact same dataset, which isn't realistic.
 You're now prepared to explore this topic further. How about using different subsets of CIFAR-10 on each client? How about adding more clients?

doc/source/quickstart-fastai.rst

@@ -6,4 +6,4 @@ Quickstart fastai
 
 Let's build a federated learning system using fastai and Flower!
 
-Please refer to the `full code example <https://github.com/adap/flower/tree/main/examples/quickstart_fastai>`_ to learn more.
+Please refer to the `full code example <https://github.com/adap/flower/tree/main/examples/quickstart-fastai>`_ to learn more.

doc/source/quickstart-huggingface.rst

@@ -219,7 +219,7 @@ And they will be able to connect to the server and start the federated training.
 
 If you want to check out everything put together, 
 you should check out the full code example: 
-[https://github.com/adap/flower/tree/main/examples/quickstart_huggingface](https://github.com/adap/flower/tree/main/examples/quickstart_huggingface). 
+[https://github.com/adap/flower/tree/main/examples/quickstart-huggingface](https://github.com/adap/flower/tree/main/examples/quickstart-huggingface). 
 
 Of course, this is a very basic example, and a lot can be added or modified, 
 it was just to showcase how simply we could federate a Hugging Face workflow using Flower.

doc/source/quickstart-jax.rst

@@ -6,7 +6,7 @@ Quickstart JAX
 
 This tutorial will show you how to use Flower to build a federated version of an existing JAX workload.
 We are using JAX to train a linear regression model on a scikit-learn dataset.
-We will structure the example similar to our `PyTorch - From Centralized To Federated <https://github.com/adap/flower/blob/main/examples/pytorch_from_centralized_to_federated>`_ walkthrough.
+We will structure the example similar to our `PyTorch - From Centralized To Federated <https://github.com/adap/flower/blob/main/examples/pytorch-from-centralized-to-federated>`_ walkthrough.
 First, we build a centralized training approach based on the `Linear Regression with JAX <https://coax.readthedocs.io/en/latest/examples/linear_regression/jax.html>`_ tutorial`.
 Then, we build upon the centralized training code to run the training in a federated fashion.
 
@@ -279,7 +279,7 @@ in each window (make sure that the server is still running before you do so) and
 Next Steps
 ----------
 
-The source code of this example was improved over time and can be found here: `Quickstart JAX <https://github.com/adap/flower/blob/main/examples/quickstart_jax>`_.
+The source code of this example was improved over time and can be found here: `Quickstart JAX <https://github.com/adap/flower/blob/main/examples/quickstart-jax>`_.
 Our example is somewhat over-simplified because both clients load the same dataset.
 
 You're now prepared to explore this topic further. How about using a more sophisticated model or using a different dataset? How about adding more clients?

doc/source/quickstart-mxnet.rst

@@ -288,4 +288,4 @@ You should now see how the training does in the very first terminal (the one tha
 
 Congratulations!
 You've successfully built and run your first federated learning system.
-The full `source code <https://github.com/adap/flower/blob/main/examples/quickstart_mxnet/client.py>`_ for this example can be found in :code:`examples/quickstart_mxnet`.
+The full `source code <https://github.com/adap/flower/blob/main/examples/quickstart-mxnet/client.py>`_ for this example can be found in :code:`examples/quickstart-mxnet`.

doc/source/quickstart-pandas.rst

@@ -6,4 +6,4 @@ Quickstart Pandas
 
 Let's build a federated analytics system using Pandas and Flower!
 
-Please refer to the `full code example <https://github.com/adap/flower/tree/main/examples/quickstart_pandas>`_ to learn more.
+Please refer to the `full code example <https://github.com/adap/flower/tree/main/examples/quickstart-pandas>`_ to learn more.

doc/source/quickstart-pytorch-lightning.rst

@@ -6,4 +6,4 @@ Quickstart PyTorch Lightning
 
 Let's build a federated learning system using PyTorch Lightning and Flower!
 
-Please refer to the `full code example <https://github.com/adap/flower/tree/main/examples/quickstart_pytorch_lightning>`_ to learn more.
+Please refer to the `full code example <https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-lightning>`_ to learn more.

doc/source/quickstart-pytorch.rst

@@ -264,4 +264,4 @@ You should now see how the training does in the very first terminal (the one tha
 
 Congratulations!
 You've successfully built and run your first federated learning system.
-The full `source code <https://github.com/adap/flower/blob/main/examples/quickstart_pytorch/client.py>`_ for this example can be found in :code:`examples/quickstart_pytorch`.
+The full `source code <https://github.com/adap/flower/blob/main/examples/quickstart-pytorch/client.py>`_ for this example can be found in :code:`examples/quickstart-pytorch`.