Merge branch 'main' into add-datasets-ci-tests

baselines/doc/source/_templates/base.html

@@ -5,6 +5,7 @@
     <meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark">
+    <link rel="canonical" href="https://flower.dev/docs/baselines/{{ pagename }}.html">
 
     {%- if metatags %}{{ metatags }}{% endif -%}
 

datasets/flwr_datasets/partitioner/iid_partitioner_test.py

@@ -56,8 +56,8 @@ def test_load_partition_size(self, num_partitions: int, num_rows: int) -> None:
         Only the correct data is tested in this method.
 
         In case the dataset is dividable among `num_partitions` the size of each
-        partition should be the same. This checks if the randomly chosen partition
-        has size as expected.
+        partition should be the same. This checks if the randomly chosen partition has
+        size as expected.
         """
         _, partitioner = _dummy_setup(num_partitions, num_rows)
         partition_size = num_rows // num_partitions

doc/locales/fr/LC_MESSAGES/framework-docs.po

@@ -5050,15 +5050,15 @@ msgstr ""
 msgid ""
 "This can be achieved by customizing an existing strategy or by "
 "`implementing a custom strategy from scratch <https://flower.dev/docs"
-"/implementing-strategies.html>`_. Here's a nonsensical example that "
+"/how-to-implement-strategies.html>`_. Here's a nonsensical example that "
 "customizes :code:`FedAvg` by adding a custom ``\"hello\": \"world\"`` "
 "configuration key/value pair to the config dict of a *single client* "
 "(only the first client in the list, the other clients in this round to "
 "not receive this \"special\" config value):"
 msgstr ""
 "Ceci peut être réalisé en personnalisant une stratégie existante ou en "
 "`mettant en œuvre une stratégie personnalisée à partir de zéro "
-"<https://flower.dev/docs/implementing-strategies.html>`_. Voici un "
+"<https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. Voici un "
 "exemple absurde qui personnalise :code:`FedAvg` en ajoutant une paire "
 "clé/valeur de configuration personnalisée ``\"hello\" : \"world\"`` au "
 "config dict d'un *seul client* (uniquement le premier client de la liste,"
@@ -7087,7 +7087,7 @@ msgid ""
 msgstr ""
 "L'écriture d'une stratégie entièrement personnalisée est un peu plus "
 "complexe, mais c'est celle qui offre le plus de souplesse. Lis le guide "
-"`Implémentation des stratégies <implementing-strategies.html>`_ pour en "
+"`Implémentation des stratégies <how-to-implement-strategies.html>`_ pour en "
 "savoir plus."
 
 #: ../../source/index.rst:31
@@ -10068,12 +10068,12 @@ msgstr ""
 #: ../../source/ref-changelog.md:517
 msgid ""
 "New documentation for [implementing strategies](https://flower.dev/docs"
-"/implementing-strategies.html) "
+"/how-to-implement-strategies.html) "
 "([#1097](https://github.com/adap/flower/pull/1097), "
 "[#1175](https://github.com/adap/flower/pull/1175))"
 msgstr ""
 "Nouvelle documentation pour [mettre en œuvre des "
-"stratégies](https://flower.dev/docs/implementing-strategies.html) "
+"stratégies](https://flower.dev/docs/framework/how-to-implement-strategies.html) "
 "([#1097](https://github.com/adap/flower/pull/1097), "
 "[#1175](https://github.com/adap/flower/pull/1175))"
 

doc/source/conf.py

@@ -122,18 +122,18 @@
     # Renamed pages
     "installation": "how-to-install-flower.html",
     "configuring-clients.html": "how-to-configure-clients.html",
-    "quickstart_mxnet": "quickstart-mxnet.html",
-    "quickstart_pytorch_lightning": "quickstart-pytorch-lightning.html",
-    "quickstart_huggingface": "quickstart-huggingface.html",
-    "quickstart_pytorch": "quickstart-pytorch.html",
-    "quickstart_tensorflow": "quickstart-tensorflow.html",
-    "quickstart_scikitlearn": "quickstart-scikitlearn.html",
-    "quickstart_xgboost": "quickstart-xgboost.html",
+    "quickstart_mxnet": "tutorial-quickstart-mxnet.html",
+    "quickstart_pytorch_lightning": "tutorial-quickstart-pytorch-lightning.html",
+    "quickstart_huggingface": "tutorial-quickstart-huggingface.html",
+    "quickstart_pytorch": "tutorial-quickstart-pytorch.html",
+    "quickstart_tensorflow": "tutorial-quickstart-tensorflow.html",
+    "quickstart_scikitlearn": "tutorial-quickstart-scikitlearn.html",
+    "quickstart_xgboost": "tutorial-quickstart-xgboost.html",
     "example_walkthrough_pytorch_mnist": "example-walkthrough-pytorch-mnist.html",
-    "release_process": "release-process.html",
+    "release_process": "contributor-how-to-release-flower.html",
     "saving-progress": "how-to-save-and-load-model-checkpoints.html",
-    "writing-documentation": "write-documentation.html",
-    "apiref-binaries": "apiref-cli.html",
+    "writing-documentation": "contributor-how-to-write-documentation.html",
+    "apiref-binaries": "ref-api-cli.html",
     "fedbn-example-pytorch-from-centralized-to-federated": "example-fedbn-pytorch-from-centralized-to-federated.html",
     # Restructuring: tutorials
     "tutorial/Flower-0-What-is-FL": "tutorial-series-what-is-federated-learning.html",

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

@@ -3,11 +3,11 @@ Example: FedBN in PyTorch - From Centralized To Federated
 
 This tutorial will show you how to use Flower to build a federated version of an existing machine learning workload with `FedBN <https://github.com/med-air/FedBN>`_, a federated training strategy designed for non-iid data.
 We are using PyTorch to train a Convolutional Neural Network(with Batch Normalization layers) on the CIFAR-10 dataset.
-When applying FedBN, only few changes needed compared to `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/example-pytorch-from-centralized-to-federated.html>`_.
+When applying FedBN, only few changes needed compared to `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_.
 
 Centralized Training
 --------------------
-All files are revised based on `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/example-pytorch-from-centralized-to-federated.html>`_.
+All files are revised based on `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_.
 The only thing to do is modifying the file called :code:`cifar.py`, revised part is shown below:
 
 The model architecture defined in class Net() is added with Batch Normalization layers accordingly.
@@ -50,8 +50,8 @@ Let's take the next step and use what we've built to create a federated learning
 Federated Training
 ------------------
 
-If you have read `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/example-pytorch-from-centralized-to-federated.html>`_, the following parts are easy to follow, onyl :code:`get_parameters` and :code:`set_parameters` function in :code:`client.py` needed to revise.
-If not, please read the `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/example-pytorch-from-centralized-to-federated.html>`_. first.
+If you have read `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_, the following parts are easy to follow, onyl :code:`get_parameters` and :code:`set_parameters` function in :code:`client.py` needed to revise.
+If not, please read the `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_. first.
 
 Our example consists of one *server* and two *clients*. In FedBN, :code:`server.py` keeps unchanged, we can start the server directly.
 

doc/source/how-to-configure-clients.rst

@@ -86,7 +86,7 @@ Configuring individual clients
 
 In some cases, it is necessary to send different configuration values to different clients.
 
-This can be achieved by customizing an existing strategy or by `implementing a custom strategy from scratch <https://flower.dev/docs/implementing-strategies.html>`_. Here's a nonsensical example that customizes :code:`FedAvg` by adding a custom ``"hello": "world"`` configuration key/value pair to the config dict of a *single client* (only the first client in the list, the other clients in this round to not receive this "special" config value):
+This can be achieved by customizing an existing strategy or by `implementing a custom strategy from scratch <https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. Here's a nonsensical example that customizes :code:`FedAvg` by adding a custom ``"hello": "world"`` configuration key/value pair to the config dict of a *single client* (only the first client in the list, the other clients in this round to not receive this "special" config value):
 
 .. code-block:: python
 

doc/source/how-to-use-strategies.rst

@@ -86,4 +86,4 @@ Server-side evaluation can be enabled by passing an evaluation function to :code
 Implement a novel strategy
 --------------------------
 
-Writing a fully custom strategy is a bit more involved, but it provides the most flexibility. Read the `Implementing Strategies <implementing-strategies.html>`_ guide to learn more.
+Writing a fully custom strategy is a bit more involved, but it provides the most flexibility. Read the `Implementing Strategies <how-to-implement-strategies.html>`_ guide to learn more.

doc/source/index.rst

@@ -1,6 +1,9 @@
 Flower Framework Documentation
 ==============================
 
+.. meta::
+   :description: Check out the documentation of the main Flower Framework enabling easy Python development for Federated Learning.
+
 Welcome to Flower's documentation. `Flower <https://flower.dev>`_ is a friendly federated learning framework.
 
 

doc/source/ref-changelog.md

@@ -2,6 +2,8 @@
 
 ## Unreleased
 
+- **Support custom** `ClientManager` **in** `start_driver()` ([#2292](https://github.com/adap/flower/pull/2292))
+
 - **Update REST API to support create and delete nodes** ([#2283](https://github.com/adap/flower/pull/2283))
 
 ### What's new?
@@ -536,7 +538,7 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
   - New option to keep Ray running if Ray was already initialized in `start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))
   - Add support for custom `ClientManager` as a `start_simulation` parameter ([#1171](https://github.com/adap/flower/pull/1171))
-  - New documentation for [implementing strategies](https://flower.dev/docs/implementing-strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), [#1175](https://github.com/adap/flower/pull/1175))
+  - New documentation for [implementing strategies](https://flower.dev/docs/framework/how-to-implement-strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), [#1175](https://github.com/adap/flower/pull/1175))
   - New mobile-friendly documentation theme ([#1174](https://github.com/adap/flower/pull/1174))
   - Limit version range for (optional) `ray` dependency to include only compatible releases (`>=1.9.2,<1.12.0`) ([#1205](https://github.com/adap/flower/pull/1205))
 

doc/source/ref-example-projects.rst

@@ -23,7 +23,7 @@ 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 (Tutorial) <https://flower.dev/docs/quickstart-tensorflow.html>`_
+- `Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/framework/tutorial-quickstart-tensorflow.html>`_
 - `Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11-federated-learning-in-less-than-20-lines-of-code>`_
 
 
@@ -34,7 +34,7 @@ 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 (Tutorial) <https://flower.dev/docs/quickstart-pytorch.html>`_
+- `Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework/tutorial-quickstart-pytorch.html>`_
 
 
 PyTorch: From Centralized To Federated
@@ -43,7 +43,7 @@ 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 (Tutorial) <https://flower.dev/docs/example-pytorch-from-centralized-to-federated.html>`_
+- `PyTorch: From Centralized To Federated (Tutorial) <https://flower.dev/docs/framework/example-pytorch-from-centralized-to-federated.html>`_
 
 
 Federated Learning on Raspberry Pi and Nvidia Jetson

doc/source/tutorial-quickstart-android.rst

@@ -4,6 +4,9 @@
 Quickstart Android
 ==================
 
+.. meta::
+   :description: Read this Federated Learning quickstart tutorial for creating an Android app using Flower.
+
 Let's build a federated learning system using TFLite and Flower on Android!
 
 Please refer to the `full code example <https://github.com/adap/flower/tree/main/examples/android>`_ to learn more.

doc/source/tutorial-quickstart-fastai.rst

@@ -4,6 +4,9 @@
 Quickstart fastai
 =================
 
+.. meta::
+   :description: Check out this Federated Learning quickstart tutorial for using Flower with FastAI to train a vision model on CIFAR-10.
+
 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.

doc/source/tutorial-quickstart-huggingface.rst

@@ -4,6 +4,9 @@
 Quickstart 🤗 Transformers
 ==========================
 
+.. meta::
+   :description: Check out this Federating Learning quickstart tutorial for using Flower with HuggingFace Transformers in order to fine-tune an LLM.
+
 Let's build a federated learning system using Hugging Face Transformers and Flower!
 
 We will leverage Hugging Face to federate the training of language models over multiple clients using Flower. 

doc/source/tutorial-quickstart-ios.rst

@@ -4,6 +4,9 @@
 Quickstart iOS
 ==============
 
+.. meta::
+   :description: Read this Federated Learning quickstart tutorial for creating an iOS app using Flower to train a neural network on MNIST.
+
 In this tutorial we will learn how to train a Neural Network on MNIST using Flower and CoreML on iOS devices. 
 
 First of all, for running the Flower Python server, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_.

doc/source/tutorial-quickstart-jax.rst

@@ -4,6 +4,9 @@
 Quickstart JAX
 ==============
 
+.. meta::
+   :description: Check out this Federated Learning quickstart tutorial for using Flower with Jax to train a linear regression model on a scikit-learn dataset.
+
 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.

doc/source/tutorial-quickstart-mxnet.rst

@@ -4,6 +4,9 @@
 Quickstart MXNet
 ================
 
+.. meta::
+   :description: Check out this Federated Learning quickstart tutorial for using Flower with MXNet to train a Sequential model on MNIST.
+
 In this tutorial, we will learn how to train a :code:`Sequential` model on MNIST using Flower and MXNet. 
 
 It is recommended to create a virtual environment and run everything within this `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_. 

doc/source/tutorial-quickstart-pandas.rst

@@ -4,6 +4,9 @@
 Quickstart Pandas
 =================
 
+.. meta::
+   :description: Check out this Federated Learning quickstart tutorial for using Flower with Pandas to perform Federated Analytics.
+
 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.

doc/source/tutorial-quickstart-pytorch-lightning.rst

@@ -4,6 +4,9 @@
 Quickstart PyTorch Lightning
 ============================
 
+.. meta::
+   :description: Check out this Federated Learning quickstart tutorial for using Flower with PyTorch Lightning to train an Auto Encoder model on MNIST.
+
 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.

doc/source/tutorial-quickstart-pytorch.rst

@@ -4,6 +4,9 @@
 Quickstart PyTorch
 ==================
 
+.. meta::
+   :description: Check out this Federated Learning quickstart tutorial for using Flower with PyTorch to train a CNN model on MNIST.
+
 ..  youtube:: jOmmuzMIQ4c
    :width: 100%
 

doc/source/tutorial-quickstart-scikitlearn.rst

@@ -4,6 +4,9 @@
 Quickstart scikit-learn
 =======================
 
+.. meta::
+   :description: Check out this Federated Learning quickstart tutorial for using Flower with scikit-learn to train a linear regression model.
+
 In this tutorial, we will learn how to train a :code:`Logistic Regression` model on MNIST using Flower and scikit-learn. 
 
 It is recommended to create a virtual environment and run everything within this `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_. 

doc/source/tutorial-quickstart-tensorflow.rst

@@ -4,6 +4,9 @@
 Quickstart TensorFlow
 =====================
 
+.. meta::
+   :description: Check out this Federated Learning quickstart tutorial for using Flower with TensorFlow to train a MobilNetV2 model on CIFAR-10.
+
 ..  youtube:: FGTc2TQq7VM
    :width: 100%
 

doc/source/tutorial-quickstart-xgboost.rst

@@ -4,6 +4,9 @@
 Quickstart XGBoost
 ==================
 
+.. meta::
+   :description: Check out this Federated Learning quickstart tutorial for using Flower with XGBoost to train classification models on trees.
+
 Let's build a horizontal federated learning system using XGBoost and Flower!
 
 Please refer to the `full code example <https://github.com/adap/flower/tree/main/examples/quickstart-xgboost-horizontal>`_ to learn more.

examples/doc/source/_templates/base.html

@@ -5,6 +5,7 @@
     <meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark">
+    <link rel="canonical" href="https://flower.dev/docs/examples/{{ pagename }}.html">
 
     {%- if metatags %}{{ metatags }}{% endif -%}
 

examples/quickstart-fastai/README.md

@@ -71,4 +71,4 @@ Start client 2 in the second terminal:
 python3 client.py
 ```
 
-You will see that fastai is starting a federated training. Have a look to the [Flower Quickstarter documentation](https://flower.dev/docs/quickstart-fastai.html) for a detailed explanation.
+You will see that fastai is starting a federated training. For a more in-depth look, be sure to check out the code on our [repo](https://github.com/adap/flower/tree/main/examples/quickstart-fastai).

examples/quickstart-huggingface/README.md

@@ -1,6 +1,6 @@
 # Federated HuggingFace Transformers using Flower and PyTorch
 
-This introductory example to using [HuggingFace](https://huggingface.co) Transformers with Flower with PyTorch. This example has been extended from the [quickstart-pytorch](https://flower.dev/docs/quickstart-pytorch.html) example. The training script closely follows the [HuggingFace course](https://huggingface.co/course/chapter3?fw=pt), so you are encouraged to check that out for detailed explaination for the transformer pipeline.
+This introductory example to using [HuggingFace](https://huggingface.co) Transformers with Flower with PyTorch. This example has been extended from the [quickstart-pytorch](https://flower.dev/docs/examples/quickstart-pytorch.html) example. The training script closely follows the [HuggingFace course](https://huggingface.co/course/chapter3?fw=pt), so you are encouraged to check that out for detailed explaination for the transformer pipeline.
 
 Like `quickstart-pytorch`, running this example in itself is also meant to be quite easy.
 

examples/quickstart-pytorch/README.md

@@ -70,4 +70,4 @@ Start client 2 in the second terminal:
 python3 client.py
 ```
 
-You will see that PyTorch is starting a federated training. Have a look to the [Flower Quickstarter documentation](https://flower.dev/docs/quickstart-pytorch.html) for a detailed explanation.
+You will see that PyTorch is starting a federated training. Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch) for a detailed explanation.