Merge branch 'main' into add-flwr-log-functions

.github/workflows/_docker-build.yml

@@ -101,6 +101,7 @@ jobs:
           attempt_delay: 9000 # 9 secs
           with: |
             pull: true
+            sbom: true
             platforms: ${{ matrix.platform.docker }}
             context: "{{defaultContext}}:${{ inputs.file-dir }}"
             outputs: type=image,name=${{ inputs.namespace-repository }},push-by-digest=true,name-canonical=true,push=true

README.md

@@ -18,6 +18,7 @@
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)
 ![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg)
 [![Downloads](https://static.pepy.tech/badge/flwr)](https://pepy.tech/project/flwr)
+[![Docker Hub](https://img.shields.io/badge/Docker%20Hub-flwr-blue)](https://hub.docker.com/u/flwr)
 [![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.ai/join-slack)
 
 Flower (`flwr`) is a framework for building federated learning systems. The

benchmarks/flowertune-llm/README.md

@@ -13,13 +13,13 @@ As the first step, please register for a Flower account on [flower.ai/login](htt
 Then, create a new Python environment and install Flower. 
 
 > [!TIP]
-> We recommend using `pyenv` and the `virtualenv` plugin to create your environment. Other manager such as Conda would likely work too. Check the [documentation](https://flower.ai/docs/framework/how-to-install-flower.html) for alternative ways of installing Flower.
+> We recommend using `pyenv` with the `virtualenv` plugin to create your environment. Other managers, such as Conda, will likely work as well. Check the [documentation](https://flower.ai/docs/framework/how-to-install-flower.html) for alternative ways to install Flower.
 
 ```shell
 pip install flwr
 ```
 
-On the new environment, create a new Flower project using the `FlowerTune` template. You will be prompted for a name to give to your project, your username, and for your choice of LLM challenge:
+In the new environment, create a new Flower project using the `FlowerTune` template. You will be prompted for a name to give to your project, your username, and for your choice of LLM challenge:
 ```shell
 flwr new --framework=FlowerTune
 ```

benchmarks/flowertune-llm/evaluation/README.md

@@ -39,6 +39,9 @@ The default template generated by `flwr new` (see the [Project Creation Instruct
 |:----------:|:-----:|:---------:|:--------------:|:---------------:|:-----:|
 | Pass@1 (%) | 31.60 |   23.78   |     28.57      |      25.47      | 27.36 |
 
+> [!NOTE]  
+> In the LLM Leaderboard, we rank the submissions based on the **average** value derived from different evaluation datasets for each challenge.
+
 
 ## Make submission on FlowerTune LLM Leaderboard
 

datasets/doc/source/index.rst

@@ -3,6 +3,15 @@ Flower Datasets
 
 Flower Datasets (``flwr-datasets``) is a library that enables the quick and easy creation of datasets for federated learning/analytics/evaluation. It enables heterogeneity (non-iidness) simulation and division of datasets with the preexisting notion of IDs. The library was created by the ``Flower Labs`` team that also created `Flower <https://flower.ai>`_ : A Friendly Federated Learning Framework.
 
+.. raw:: html
+
+  <script
+    type="module"
+    src="https://gradio.s3-us-west-2.amazonaws.com/4.44.0/gradio.js"
+  ></script>
+
+  <gradio-app src="https://flwrlabs-federated-learning-datasets-by-flwr-datasets.hf.space"></gradio-app>
+
 Flower Datasets Framework
 -------------------------
 

dev/build-docker-image-matrix.py

@@ -134,7 +134,7 @@ def tag_latest_ubuntu_with_flwr_version(image: BaseImage) -> List[str]:
     ubuntu_base_images = generate_base_images(
         flwr_version,
         SUPPORTED_PYTHON_VERSIONS,
-        [Distro(DistroName.UBUNTU, "22.04")],
+        [Distro(DistroName.UBUNTU, "24.04")],
     )
     # alpine base images for the latest supported python version
     alpine_base_images = generate_base_images(

dev/format.sh

@@ -36,3 +36,6 @@ python -m nbstripout examples/*/*.ipynb --extra-keys "$KEYS"
 
 # Markdown
 python -m mdformat --number doc/source examples
+
+# RST
+docstrfmt doc/source

dev/test.sh

@@ -56,6 +56,14 @@ echo "- mdformat: done"
 
 echo "- All Markdown checks passed"
 
+echo "- Start rST checks"
+
+echo "- docstrfmt: start"
+docstrfmt --check doc/source
+echo "- docstrfmt: done"
+
+echo "- All rST checks passed"
+
 echo "- Start license checks"
 
 echo "- copyright: start"