[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-479":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":33,"readmeContent":34,"aiSummary":35,"trendingCount":16,"starSnapshotCount":16,"syncStatus":36,"lastSyncTime":37,"discoverSource":38},479,"keras","keras-team\u002Fkeras","keras-team","Deep Learning for humans","http:\u002F\u002Fkeras.io\u002F",null,"Python",64076,19733,1855,144,0,5,67,45,"Apache License 2.0",false,"master",true,[25,26,27,28,29,30,31,32],"data-science","deep-learning","jax","machine-learning","neural-networks","python","pytorch","tensorflow","2026-06-12 02:00:14","# Keras 3: Deep Learning for Humans\n\nKeras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only).\nEffortlessly build and train models for computer vision, natural language processing, audio processing,\ntimeseries forecasting, recommender systems, etc.\n\n- **Accelerated model development**: Ship deep learning solutions faster thanks to the high-level UX of Keras\nand the availability of easy-to-debug runtimes like PyTorch or JAX eager execution.\n- **State-of-the-art performance**: By picking the backend that is the fastest for your model architecture (often JAX!),\nleverage speedups ranging from 20% to 350% compared to other frameworks. [Benchmark here](https:\u002F\u002Fkeras.io\u002Fgetting_started\u002Fbenchmarks\u002F).\n- **Datacenter-scale training**: Scale confidently from your laptop to large clusters of GPUs or TPUs.\n\nJoin nearly three million developers, from burgeoning startups to global enterprises, in harnessing the power of Keras 3.\n\n\n## Installation\n\n### Install with pip\n\nKeras 3 is available on PyPI as `keras`. Note that Keras 2 remains available as the `tf-keras` package.\n\n1. Install `keras`:\n\n```\npip install keras --upgrade\n```\n\n2. Install backend package(s).\n\nTo use `keras`, you should also install the backend of choice: `tensorflow`, `jax`, or `torch`. Additionally,\nThe `openvino` backend is available with support for model inference only.\n\n### Local installation\n\n#### Minimal installation\n\nKeras 3 is compatible with Linux and macOS systems. For Windows users, we recommend using WSL2 to run Keras.\nTo install a local development version:\n\n1. Install dependencies:\n\n```\npip install -r requirements.txt\n```\n\n2. Run installation command from the root directory.\n\n```\npython pip_build.py --install\n```\n\n3. Run API generation script when creating PRs that update `keras_export` public APIs:\n\n```\n.\u002Fshell\u002Fapi_gen.sh\n```\n\n## Backend Compatibility Table\n\nThe following table lists the minimum supported versions of each backend for the latest stable release of Keras (v3.x):\n\n| Backend    | Minimum Supported Version |\n|------------|---------------------------|\n| TensorFlow | 2.16.1                    |\n| JAX        | 0.4.20                    |\n| PyTorch    | 2.1.0                     |\n| OpenVINO   | 2025.3.0                  |\n\n#### Adding GPU support\n\nThe `requirements.txt` file will install a CPU-only version of TensorFlow, JAX, and PyTorch. For GPU support, we also\nprovide a separate `requirements-{backend}-cuda.txt` for TensorFlow, JAX, and PyTorch. These install all CUDA\ndependencies via `pip` and expect a NVIDIA driver to be pre-installed. We recommend a clean Python environment for each\nbackend to avoid CUDA version mismatches. As an example, here is how to create a JAX GPU environment with `conda`:\n\n```shell\nconda create -y -n keras-jax python=3.10\nconda activate keras-jax\npip install -r requirements-jax-cuda.txt\npython pip_build.py --install\n```\n\n## Configuring your backend\n\nYou can export the environment variable `KERAS_BACKEND` or you can edit your local config file at `~\u002F.keras\u002Fkeras.json`\nto configure your backend. Available backend options are: `\"tensorflow\"`, `\"jax\"`, `\"torch\"`, `\"openvino\"`. Example:\n\n```\nexport KERAS_BACKEND=\"jax\"\n```\n\nIn Colab, you can do:\n\n```python\nimport os\nos.environ[\"KERAS_BACKEND\"] = \"jax\"\n\nimport keras\n```\n\n**Note:** The backend must be configured before importing `keras`, and the backend cannot be changed after\nthe package has been imported.\n\n**Note:** The OpenVINO backend is an inference-only backend, meaning it is designed only for running model\npredictions using `model.predict()` method.\n\n## Backwards compatibility\n\nKeras 3 is intended to work as a drop-in replacement for `tf.keras` (when using the TensorFlow backend). Just take your\nexisting `tf.keras` code, make sure that your calls to `model.save()` are using the up-to-date `.keras` format, and you're\ndone.\n\nIf your `tf.keras` model does not include custom components, you can start running it on top of JAX or PyTorch immediately.\n\nIf it does include custom components (e.g. custom layers or a custom `train_step()`), it is usually possible to convert it\nto a backend-agnostic implementation in just a few minutes.\n\nIn addition, Keras models can consume datasets in any format, regardless of the backend you're using:\nyou can train your models with your existing `tf.data.Dataset` pipelines or PyTorch `DataLoaders`.\n\n## Why use Keras 3?\n\n- Run your high-level Keras workflows on top of any framework -- benefiting at will from the advantages of each framework,\ne.g. the scalability and performance of JAX or the production ecosystem options of TensorFlow.\n- Write custom components (e.g. layers, models, metrics) that you can use in low-level workflows in any framework.\n    - You can take a Keras model and train it in a training loop written from scratch in native TF, JAX, or PyTorch.\n    - You can take a Keras model and use it as part of a PyTorch-native `Module` or as part of a JAX-native model function.\n- Make your ML code future-proof by avoiding framework lock-in.\n- As a PyTorch user: get access to power and usability of Keras, at last!\n- As a JAX user: get access to a fully-featured, battle-tested, well-documented modeling and training library.\n\n\nRead more in the [Keras 3 release announcement](https:\u002F\u002Fkeras.io\u002Fkeras_3\u002F).\n","Keras 3 是一个面向人类的深度学习框架，支持 JAX、TensorFlow、PyTorch 和 OpenVINO（仅用于推理）。它提供了加速模型开发的功能，通过其高级用户体验和易于调试的运行时环境（如 PyTorch 或 JAX 的即时执行模式），使开发者能够更快地交付深度学习解决方案。Keras 3 还能根据模型架构选择最快的后端，从而实现高达 20% 至 350% 的性能提升。此外，该框架支持从笔记本电脑到大规模 GPU 或 TPU 集群的数据中心级训练扩展。适用于需要快速构建和训练计算机视觉、自然语言处理、音频处理、时间序列预测及推荐系统等模型的场景。",2,"2026-06-11 02:36:20","top_all"]