That is why Meta started developing PyTorch as a means to offer pretty much the same functionalities as TensorFlow, but making it easier to use. The people behind TensorFlow soon took note of this ...

Is PyTorch better than TensorFlow for general use cases? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world ...

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 has a rapidly growing community, especially in the research sector, and is gaining on TensorFlow. Debugging PyTorch allows for straightforward debugging using standard Python tools.

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.

As Spisak told me, one of the most important new features in PyTorch 1.1 is support for TensorBoard, Google’s visualization tool for TensorFlow that helps developers evaluate and inspect models.

PyTorch vs TensorFlow. While TensorFlow is the workhorse of Google’s ML efforts, it’s not the only open-source ML training library. In recent years the open-source PyTorch framework, ...

Facebook wants to make sure the open-source PyTorch machine-learning framework supports the needs of developers who want to use its AI models in production systems, not just research projects, it ...

Available today, PyTorch 1.3 comes with the ability to quantize a model for inference on to either server or mobile devices. Quantization is a way to perform computation at reduced precision.