December 2025 monthly summary for keras-team/keras focused on stabilizing model startup memory usage and improving cross-backend initialization reliability. Delivered a critical memory-safety bug fix to Keras initialization to prevent Out Of Memory (OOM) across backends, accompanied by foundational improvements to initialization workflows, error handling, and cross-backend consistency. The work reduces memory-related incidents during model startup, enhances user experience for large models, and strengthens maintainability and test coverage.

April 2025 monthly summary focused on expanding Keras backend support to PyTorch with CuDNN-optimized LSTM. Delivered groundwork for a PyTorch LSTM backend, including mask validation helpers, weight preparation utilities, and a core LSTM implementation leveraging PyTorch's optimized LSTM module. This work enhances cross-framework interoperability and provides performance benefits for users running Keras models on PyTorch backends. The work is in a WIP state with initial changes committed; next steps include broader integration, tests, and documentation. Commit reference: 128e28018753c5bd8638b4bb60c100b8749134b6 (WIP: #21135).

In March 2025, focused on stabilizing the PyTorch-backed stateful RNN/LSTM workflow in keras, delivering a correctness fix that prevents autograd-related gradient issues during training. The change ensures new tensor copies are created for internal states, avoiding in-place modifications that disrupted backward passes. This work strengthens cross-backend consistency and training reliability for stateful sequence models.