Keras
Open-source high-level neural networks API that supports multiple backends. Designed for fast prototyping, readability, and production deployment.
Keras is a deep learning API designed for human beings, not machines. It prioritizes debugging speed, code elegance, maintainability, and deployability. With Keras 3, you can write code once and run it on JAX, TensorFlow, or PyTorch backends.
The framework provides three ways to build models: the Functional API for complex architectures, the Sequential model for simple layer stacks, and subclassing for complete customization. KerasHub extends this with pretrained models including Gemma, LLaMA, Stable Diffusion, and Mistral.
Key capabilities
Keras handles the full deep learning workflow from model definition through training to deployment. The built-in training loop supports callbacks, metrics, and validation out of the box. For advanced use cases, you can customize every aspect of the training step.
Quantization features support 8-bit and 4-bit integer quantization as well as GPTQ for deploying large models with reduced memory footprint. Distributed training works across multiple GPUs and TPUs with minimal code changes.
Limitations
- Requires a separate backend installation (JAX, TensorFlow, or PyTorch) which adds complexity to environment setup
- Multi-backend abstraction can obscure performance characteristics specific to each backend
- Smaller ecosystem of third-party extensions compared to native PyTorch
- Some advanced features from individual backends may not be exposed through the Keras API
- Debugging across backend boundaries can be challenging when issues arise in framework-specific code
Keras
TensorFlowKeras 3 integrates with TensorFlow as one of its three supported backends. When using the TensorFlow backend, Keras models compile to TensorFlow operations and can leverage the full TensorFlow ecosystem including TensorBoard, TensorFlow Serving, and TensorFlow Lite for deployment.
Keras
PyTorchKeras 3 integrates with PyTorch as one of its three supported backends. When using the PyTorch backend, Keras models use PyTorch tensors and autograd, enabling access to the PyTorch ecosystem including torch.compile, PyTorch Lightning, and native PyTorch deployment options.
KerasKeras models can be exported to ONNX format for deployment across different inference engines and hardware accelerators. This enables Keras-trained models to run on ONNX Runtime, TensorRT, OpenVINO, and other ONNX-compatible execution providers.
Similar to Keras
