PyTorch vs. TensorFlow

Should you use PyTorch or TensorFlow? PyTorch, developed by Meta AI, dominates research, with 60% of published papers using it as of June of 2024. People love its dynamic computational graph, intuitive model building, and seamless integration with Python tools like NumPy. It’s also very user-friendly, making it great for rapid prototyping and experimentation. The fact that it's Pythonic also helps to make it more useful for a wider audience, and PyTorch also has a rich community, meaning that there’s an excellent ecosystem of models and libraries for PyTorch users to tap into. TensorFlow, on the other hand, developed by Google, is generally considered to be the industry standard for production environments. It offers robust deployment capabilities through TensorFlow Serving and strong cloud service integration too. TensorFlow's comprehensive tooling includes TensorBoard for visualization, TensorFlow Lite for mobile deployment, and TensorFlow.js for web deployment. It also supports hardware acceleration through GPUs and TPUs, ensuring high performance in large-scale environments. PyTorch used to have the edge when it came to computation graphs, but TensorFlow 2.x is offering it some pretty serious competition in that department, but at the same time, PyTorch is gaining ground in deployment capabilities, narrowing the gap with TensorFlow. In other words, PyTorch and TensorFlow are neck and neck and it’ll be interesting to see how things shake out.