TensorFlow is your ally for scalability and production. PyTorch is your friend for research flexibility and ease of use. The choice depends on your project needs, expertise, and long-term goals.

PyTorch recreates the graph on the fly at each iteration step. In contrast, TensorFlow by default creates a single data flow graph, optimizes the graph code for performance, and then trains the model.

PyTorch recreates the graph on the fly at each iteration step. In contrast, TensorFlow by default creates a single data flow graph, optimizes the graph code for performance, and then trains the model.

If this is what matters most for you, then your choice is probably TensorFlow. A network written in PyTorch is a Dynamic Computational Graph (DCG). It allows you to do any crazy thing you want to do.

TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, Theano and Apache MXNet are the seven most popular frameworks for developing AI applications.

Google enhances TensorFlow with deep learning capabilities and parallelism techniques for developer choice in machine language tooling.

AI Platform Notebooks are configured with the core packages needed for TensorFlow and PyTorch environments. They also have the packages with the latest Nvidia driver for GPU-enabled instances.

TensorFlow is an open-source machine learning and deep learning framework created by Google Brain in 2015. It provides a flexible and efficient ecosystem for building and training AI models ...

PyTorch is an open-source machine learning library developed by Facebook’s AI Research lab (FAIR). It’s known for its flexibility, ease of use, and as a powerful tool for deep learning ...