
PyTorch
An open-source machine learning framework that accelerates the path from research prototyping to production deployment.

The high-level deep learning API for JAX, PyTorch, and TensorFlow.

Keras is a high-level neural networks API, written in Python and capable of running on top of JAX, PyTorch, or TensorFlow. As of 2026, Keras 3 has established itself as the premier multi-backend deep learning framework, allowing developers to write code once and execute it across the industry's most powerful engines. This 'write once, run anywhere' philosophy solves a critical fragmentation issue in the AI ecosystem. Keras emphasizes developer experience, offering a consistent, simple API that reduces cognitive load during model design. Its architecture is built for speed of experimentation—enabling users to go from idea to result with the least possible delay. Keras is extensively used in both industry and academia, supporting everything from simple convolutional networks to complex generative models like Stable Diffusion and Large Language Models. By leveraging XLA compilation via JAX and TensorFlow, or the native performance of PyTorch, Keras ensures that high-level abstractions do not come at the cost of execution speed. Its ecosystem includes KerasCV and KerasNLP, which provide production-grade, pre-trained components for computer vision and natural language processing tasks, making it a cornerstone for enterprise AI pipelines in 2026.
Keras is a high-level neural networks API, written in Python and capable of running on top of JAX, PyTorch, or TensorFlow.
Explore all tools that specialize in image classification. This domain focus ensures Keras delivers optimized results for this specific requirement.
Explore all tools that specialize in train deep learning models. This domain focus ensures Keras delivers optimized results for this specific requirement.
Explore all tools that specialize in generate synthetic data. This domain focus ensures Keras delivers optimized results for this specific requirement.
Enables the exact same Keras code to run on JAX, PyTorch, or TensorFlow backends.
Leverages Accelerated Linear Algebra to optimize GPU/TPU operations via Just-In-Time (JIT) compilation.
Built-in API for multi-worker and multi-GPU training strategies using simple distribution scopes.
Automated use of float16 and bfloat16 for computation while maintaining float32 for weights.
A way to create models that are more flexible than the Sequential API, handling non-linear topologies and shared layers.
Vertical-specific libraries containing state-of-the-art pre-trained weights and modular components.
Object-oriented approach to defining custom layers with full control over the forward pass.
Install Python 3.10+ environment.
Install Keras via 'pip install keras'.
Select and install a backend framework (JAX, PyTorch, or TensorFlow).
Configure the 'KERAS_BACKEND' environment variable.
Load datasets using keras.utils.get_file or directory_to_dataset.
Define model architecture using the Sequential or Functional API.
Compile the model specifying the optimizer (e.g., AdamW) and loss function.
Implement Keras Callbacks for monitoring and model checkpointing.
Train the model using the model.fit() method.
Export the model to the .keras format for framework-agnostic deployment.
All Set
Ready to go
Verified feedback from other users.
"Highly praised for its simplicity and the massive improvement in Keras 3's multi-backend support."
Post questions, share tips, and help other users.

An open-source machine learning framework that accelerates the path from research prototyping to production deployment.

The high-performance deep learning framework for flexible and efficient distributed training.

Criss-Cross Network for Semantic Segmentation using attention mechanisms.

A suite of tools for deploying and training deep learning models using the JVM.

The industry-standard open-source object detection toolbox for academic research and industrial deployment.

The high-performance sequence modeling toolkit for researchers and production-grade NLP engineering.

A library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

The engineer's choice for developing, testing, and deploying high-performance AI models.