A machine learning research team is developing a novel neural network architecture for a complex computer vision task. The project requirements prioritize rapid experimentation with unconventional layer designs and the ability to debug model components interactively using standard Python tools. The development workflow must be highly flexible, allowing for on-the-fly modifications to the model's structure based on intermediate results. Which of the following deep learning frameworks is BEST suited for this scenario?
The correct answer is PyTorch. PyTorch uses a dynamic computation graph (define-by-run), which builds the graph as the code is executed. This makes it exceptionally flexible for building complex and unconventional architectures. This 'Pythonic' nature allows for the use of standard Python debugging tools like pdb, which aligns perfectly with the team's requirement for interactive debugging.
TensorFlow with Keras is a plausible but less optimal choice. While Keras is excellent for rapid prototyping of well-established architectures and TensorFlow 2.x has adopted eager execution for more dynamic behavior, PyTorch is generally considered more flexible and intuitive for the kind of cutting-edge research and on-the-fly model modification described.
TensorFlow Serving is incorrect because it is a system for deploying and serving trained models in production, not a framework for developing and experimenting with them.
AutoML is also incorrect as it is a process for automating the selection and optimization of existing model architectures, which is the opposite of the team's goal to design a novel architecture from scratch.
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What is a dynamic computation graph in PyTorch?
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Why is TensorFlow with Keras less suited for rapid experimentation compared to PyTorch?