{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/11"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"A multi-backend deep learning framework that lets you build and train neural networks using JAX, TensorFlow, PyTorch, or OpenVINO as the compute engine, without rewriting your model code.","skillfed_tags":["multi-backend","neural-networks","framework-agnostic"],"use_cases":["Train a model on JAX for development speed, then switch to TensorFlow for production deployment without rewriting code.","Build computer vision models (image classification, detection) that run on any of the four supported backends.","Develop NLP or time-series models that can be deployed via OpenVINO for inference-only optimization.","Convert existing tf.keras code to run on PyTorch or JAX by changing the backend environment variable.","Write custom layers or training loops that work across multiple frameworks without framework-specific code.","Scale model training from a laptop to GPU or TPU clusters while keeping the same high-level API."],"what_it_does":"keras-nightly is a high-level deep learning framework designed to work with multiple compute backends\u2014JAX, TensorFlow, PyTorch, and OpenVINO\u2014so you can write model code once and run it on whichever backend suits your needs. It provides a unified API for building neural networks across computer vision, natural language processing, audio, time-series, and recommender systems, with the goal of letting you switch backends without rewriting your models.\n\nThe package includes 8 runtime dependencies (absl-py, numpy, rich, namex, h5py, optree, ml-dtypes, packaging) and is actively maintained with recent releases. It requires Python 3.11 or later and one or more backend packages installed separately. The framework is positioned as a drop-in replacement for tf.keras when using the TensorFlow backend, and supports consuming datasets from tf.data or PyTorch DataLoaders regardless of which backend you choose.","worth_installing":"Yes, if you need a unified deep learning API across multiple backends or want to avoid framework lock-in. The active maintenance, low install friction, permissive license, and zero known vulnerabilities support adoption. Install it alongside your chosen backend (TensorFlow, JAX, PyTorch, or OpenVINO) and set KERAS_BACKEND before importing. Not suitable if you require Python versions below 3.11 or need a single-backend framework."},"id":"keras-nightly","links":{"html":"https://skillfed.io/packages/keras-nightly","md":"https://skillfed.io/packages/keras-nightly.md","pypi":"https://pypi.org/project/keras-nightly/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"keras-nightly","python_support":"supports_current","summary":"Multi-backend Keras"},"popularity":{"monthly_downloads":256557,"position":8463,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.16.0.dev2026081404"}