Which Python tool provides a frontend environment for the TensorFlow library?

Keras is a high-level neural networks API that provides an easy-to-use frontend interface for building and training deep learning models, and it is specifically designed to work with TensorFlow as its backend engine. By acting as a user-friendly layer on top of TensorFlow, Keras simplifies many tasks associated with developing and experimenting with deep learning models, allowing practitioners to focus on designing and refining their architecture without getting bogged down by the complex details of TensorFlow.

Keras supports various layers, optimizers, and loss functions and offers a straightforward syntax for model building, which is particularly beneficial for those new to deep learning or for rapid prototyping. The integration with TensorFlow enhances Keras's capabilities, combining ease of use with the power and scalability of TensorFlow's ecosystem.

Other options, while being valuable tools in the data science and machine learning landscape, serve different purposes. For instance, SciPy is more oriented toward scientific and mathematical computations, Apache Spark MLlib focuses on scalable machine learning in big data contexts, and PyTorch is a separate deep learning framework with its own frontend capabilities but does not work as a frontend for TensorFlow.