Contents

flwr 1.9.0

0

Flower: A Friendly Federated Learning Framework

Flower: A Friendly Federated Learning Framework

Stars: 4521, Watchers: 4521, Forks: 794, Open Issues: 526

The adap/flower repo was created 4 years ago and the last code push was 37 minutes ago.
The project is very popular with an impressive 4521 github stars!

How to Install flwr

You can install flwr using pip

pip install flwr

or add it to a project with poetry

poetry add flwr

Package Details

Author
The Flower Authors
License
Apache-2.0
Homepage
https://flower.ai
PyPi:
https://pypi.org/project/flwr/
Documentation:
https://flower.ai
GitHub Repo:
https://github.com/adap/flower

Classifiers

  • Scientific/Engineering
  • Scientific/Engineering/Artificial Intelligence
  • Scientific/Engineering/Mathematics
  • Software Development
  • Software Development/Libraries
  • Software Development/Libraries/Python Modules
No  flwr  pypi packages just yet.

Errors

A list of common flwr errors.

Code Examples

Here are some flwr code examples and snippets.

GitHub Issues

The flwr package has 526 open issues on GitHub

  • Restructure Baselines docs
  • Add check wheel contents
  • Update the installing dependencies for MAC user
  • Implementation of FedDF
  • Connection overriding problem
  • Add HuggingFace E2E test
  • Add PyTorch-Lightning E2E test
  • Fixes: Broken links in Flower Baseline section
  • Broken links in README for Flower Baseline section
  • Update tensorflow-cpu requirement from ^2.9.1, !=2.11.1 to ^2.11.1 in /e2e/tensorflow
  • Update numpy requirement from 1.23.1 to 1.24.4 in /e2e/mxnet
  • FedNTD
  • Use latest versions of frameworks for E2E testing
  • Check if requirements.txt is synced with pyproject.toml
  • New Android Example with Kotlin and TensorFlow Lite 2022

See more issues on GitHub

Related Packages & Articles

thinc 9.0.0

A refreshing functional take on deep learning, compatible with your favorite libraries

deeplake 3.9.14

Deep Lake is a Database for AI powered by a unique storage format optimized for deep-learning and Large Language Model (LLM) based applications. It simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage for all workloads, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more.

pytorch-lightning 2.3.3

PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.

onnx 1.16.1

Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring).

vosk 0.3.45

Offline open source speech recognition API based on Kaldi and Vosk

jina 3.27.2

Multimodal AI services & pipelines with cloud-native stack: gRPC, Kubernetes, Docker, OpenTelemetry, Prometheus, Jaeger, etc.