NCA-GENM Question 50
Single answerA team is developing a generative AI multimodal model that processes both text and image data. They need to use a deep learning framework that provides optimal support for dynamic computation graphs, GPU acceleration, and a Python-first API. Which framework should they choose?
- A
TensorFlow
- B
PyTorch
- C
Keras
- D
Theano
Show answer and explanation
Correct answer: B
Explanation
Dynamic computation graphs are particularly useful in generative AI tasks involving multimodal data, as they allow for more flexibility during runtime. PyTorch is widely recognized for its ease of use, strong support for dynamic computation graphs, and robust GPU acceleration. While TensorFlow has similar capabilities, PyTorch's dynamic graph implementation is more intuitive, making it the best option for this use case.
- A. Incorrect.
TensorFlow is a powerful framework, but it primarily emphasizes static computation graphs (although TensorFlow 2.0 supports eager execution). PyTorch, however, is more well-known for its dynamic computation graph capabilities.
- B. Correct.
PyTorch is specifically designed for dynamic computation graphs, GPU acceleration, and a Python-first approach, making it the ideal choice for this scenario.
- C. Incorrect.
Keras is a high-level API for building deep learning models but relies on a backend framework (like TensorFlow) for GPU acceleration and does not natively support dynamic graphs.
- D. Incorrect.
Theano is an older framework that is no longer actively maintained. While it supports GPU acceleration, it lacks dynamic graph support and a modern Python-centric design.