The PyTorch Virtual Developer Day is now available online with technical talks and version 1.7 release deep dives. The team also announced updates to PyTorch.

PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. It aims to offer a replacement for NumPy that makes use of the power of GPUs, while providing a deep learning research platform that provides maximum flexibility and speed.

Sessions now online from the Developer Day, which took place on November 12th cover a variety of topics, including updates to the core framework and new tools and libraries to support development across a variety of domains.

The announcements at the virtual event started with four PyTorch prototype features, three designed to let mobile machine-learning developers execute models on the full set of hardware engines making up a system-on-chip (SOC). The developers say this gives developers options to optimize their model execution for unique performance, power, and system-level concurrency.

The hardware engines supported by the new features are DSP and NPUs using the Android Neural Networks API (NNAPI), and GPU execution on Android and on iOS.

The developers also announced a number of technical contributions to enable end-to-end support for MLflow usage with PyTorch, including support for auto logging via PyTorch Lightning; TorchServe integration through a new deployment plug-in; and a sample end-to-end workflow targeting HuggingFace Transformers. This work, done in collaboration between members of the technical teams at Databricks/MLflow core maintainers and the PyTorch core development team within Facebook, is described as being just the beginning, with more updates expected in the coming months.

Another announcement was support for PyTorch Neural Networks API, meaning developers will be able to use hardware accelerated inference with PyTorch.

More Info

PyTorch Website

PyTorch On GitHub

PyTorch Developer Day Registration

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Today, we are announcing a number of technical contributions to enable end-to-end support for MLflow usage with PyTorch, including support for: autologging via PyTorch Lightning; TorchServe integration through a new deployment plug-in; and a sample end-to-end workflow targeting HuggingFace Transformers. This work, done in collaboration between members of the technical teams at Databricks/MLflow core maintainers and the PyTorch core development team within Facebook, is just the beginning. We expect more technical contributions in the coming months.