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.

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 ...

Google announced the release of the Quantization Aware Training (QAT) API for their TensorFlow Model Optimization Toolkit. QAT simulates low-precision hardware during the neural-network training proce ...

TensorFlow also includes quantization-aware training as part of its "contrib" library, which is "not officially supported, ... Facebook's PyTorch, another major deep-learning framework, ...

Unlike TensorFlow, PyTorch hasn’t experienced any major ruptures in the core code since the deprecation of the Variable API in version 0.4. (Previously, Variable was required to use autograd ...

PyTorch is still growing, while TensorFlow’s growth has stalled. Graph from StackOverflow trends . StackOverflow traffic for TensorFlow might not be declining at a rapid speed, but it’s ...

Facebook Inc. today updated its popular artificial intelligence software framework PyTorch with support for new features that enable a more seamless AI model deployment to mobile devices.PyTorch i.

For these cases, PyTorch and TensorFlow can be quite effective, especially if there is already a trained model similar to what you need in the framework’s model library. PyTorch.

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 ...

TensorFlow: Developed by Google. Strong in production capabilities and scalability. Extensive API offerings. PyTorch: Developed by Meta’s AI Research lab.