December 02, 2022

Accelerating Hugging Face and TIMM models with PyTorch 2.0

torch.compile() makes it easy to experiment with different compiler backends to make PyTorch code faster with a single line decorator torch.compile(). It works either directly over an nn.Module as a drop-in replacement for torch.jit.script() but without requiring you to make any source code changes. We expect this one lin...

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November 17, 2022

Introducing TorchMultimodal - a library for accelerating exploration in Multimodal AI

We are announcing TorchMultimodal Beta, a PyTorch domain library for training SoTA multi-task multimodal models at scale. The library provides composable building blocks (modules, transforms, loss functions) to accelerate model development, SoTA model architectures (FLAVA, MDETR, Omnivore) from published research, training and evaluation scripts, as well as notebooks for exploring these models. The library is under active development, and we’d love to hear your feedback! You can find more det...

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November 10, 2022

PyTorch Enterprise Support Program Update

On May 25, 2021, we announced the PyTorch Enterprise Support Program (ESP) that enabled providers to develop and offer tailored enterprise-grade support to their customers.

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November 03, 2022

Extending TorchVision’s Transforms to Object Detection, Segmentation & Video tasks

TorchVision is extending its Transforms API! Here is what’s new:

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