Keras Expands Infrastructure Reach With Distributed Training and Multi-Backend Async Support
François Chollet, Keras creator and deep learning researcher, announced that Keras now supports distributed training, asynchronous jobs, and compatibility across all Keras backends, which include TensorFlow, JAX, and PyTorch.
8. Keras Expands Infrastructure Reach With Distributed Training and Multi-Backend Async Support
François Chollet, Keras creator and deep learning researcher, announced that Keras now supports distributed training, asynchronous jobs, and compatibility across all Keras backends, which include TensorFlow, JAX, and PyTorch. The announcement, shared via Chollet's personal Twitter account, points to a linked resource detailing the implementation. While the snippet is sparse on version specifics, the combination of these three capabilities signals a deliberate push to make Keras viable for production-scale training pipelines, not just research prototyping.
The stakes here are real for teams choosing between frameworks. Distributed training support puts Keras in more direct competition with PyTorch Lightning and Hugging Face Accelerate, both of which have built significant developer loyalty precisely by abstracting away multi-GPU and multi-node complexity. The async jobs capability is particularly notable: it suggests Keras is targeting cloud-native and MLOps workflows where training runs are managed asynchronously through orchestration layers like Kubernetes or managed services from Google Cloud, AWS, and Azure. Keras's multi-backend flexibility remains its sharpest differentiator, and adding production infrastructure features on top of that means organizations already invested in JAX for TPU performance or PyTorch for ecosystem breadth no longer have to abandon Keras-level ergonomics to scale up.
This move fits a broader pattern of ML frameworks converging upward into infrastructure. What began as differentiation at the model-definition layer has shifted to differentiation at the training orchestration layer. Chollet's Keras, now developed under Google DeepMind's umbrella, is clearly positioning itself not as a beginner-friendly wrapper but as a serious end-to-end training stack. That repositioning has direct implications for Google's ability to keep Keras relevant as JAX adoption grows internally and externally.
Source: https://twitter.com/fchollet/status/2040116224718450970