PyTorch 1.0 Goes Live
Last week Facebook announced the 1.0 release of PyTorch, it's Python-first open source deep learning platform.
Facebook announced that 1.0 was coming back in May, stating that the release would offer artificial intelligence (AI) developers "a fast, seamless path from research prototyping to production deployment for a broad range of AI projects" via a "hybrid front end that seamlessly transitions between imperative and declarative execution modes," with the 1.0 version finally offering "performance at production-scale."
Previous versions have been available on GitHub for a while, but according to the PyTorch Web site, 1.0 is a vast improvement for developers. As part of this, the company introduced Torch.jit into PyTorch 1.0, a new just-in-time (JIT) compiler for models (reviewed in detail here) that essentially allows you much more flexibility on how to export and use the code you create an PyTorch.
The 1.0 version also offers a new hybrid front end for "seamless transitions between eager mode and graph mode to provide both flexibility and speed" in C++ runtimes.
As with previous versions, if desired, PyTorch can be set up in the cloud via partnerships with machine learning services from Amazon, Google and Microsoft.
The Github page for PyTorch is here. The PyTorch Web site is here.
Becky Nagel is the vice president of Web & Digital Strategy for 1105's Converge360 Group, where she oversees the front-end Web team and deals with all aspects of digital strategy. She also serves as executive editor of the group's media Web sites, and you'll even find her byline on PureAI.com, the group's newest site for enterprise developers working with AI. She recently gave a talk at a leading technical publishers conference about how changes in Web technology may impact publishers' bottom lines. Follow her on twitter @beckynagel.