#multi-backend

Keras 3 is an adaptable deep learning framework compatible with JAX, TensorFlow, and PyTorch. It enhances model building across fields like computer vision and NLP with superior flexibility and performance. This framework ensures effortless scaling from personal setups to datacenter environments. Users benefit from easy installation via pip and improved GPU performance through CUDA integration. Keras 3 supports existing tf.keras code and extends custom components, leveraging each backend's distinct capabilities for robust machine learning solutions.

The transition from Keras Core to Keras 3 marks a significant step in AI development, consolidating all development in the keras-team/keras repository. This move ensures the preservation and acknowledgment of initial contributions, facilitating seamless multi-backend support. The legacy tf.keras codebase now resides in keras-team/tf-keras. Stay informed on the latest in multi-backend machine learning as Keras 3 embarks on its new era, honoring past work while exploring new frontiers in AI.

Bitsandbytes library provides an efficient Python interface for CUDA functions, featuring 8-bit optimizers, matrix multiplication, and quantization for 8-bit and 4-bit operations. It extends support to multiple backends like AMD GPUs and Intel processors, improving cross-platform functionality. The recent alpha release showcases its commitment to expanding hardware compatibility, with ongoing efforts for Windows and future Apple Silicon support, inviting constructive community feedback for continual enhancement.

Discover TensorLayerX, a dynamic AI framework that seamlessly integrates with major platforms such as TensorFlow and PyTorch, ensuring compatibility with a variety of hardware, including Nvidia-GPU and Huawei-Ascend. This framework facilitates easy model deployment across multiple platforms and offers a rich Model Zoo, featuring both classic and cutting-edge models in fields like CV, NLP, and RL. With support for the ONNX protocol, TensorLayerX ensures straightforward model export and deployment. Benefit from comprehensive documentation and educational materials designed to enhance deep learning skills.