{"href":"https://api.simplecast.com/oembed?url=https%3A%2F%2Fpytorch-dev-podcast.simplecast.com%2Fepisodes%2Faotautograd-qW76vMmk","width":444,"version":"1.0","type":"rich","title":"AOTAutograd","thumbnail_width":300,"thumbnail_url":"https://image.simplecastcdn.com/images/8cefde76-fb46-406a-8d87-ab0df67f3423/92f11400-2dad-49b4-8b14-cce35f5ab765/pytorch-symbol-02-orangeondark.jpg","thumbnail_height":300,"provider_url":"https://simplecast.com","provider_name":"Simplecast","html":"<iframe src=\"https://player.simplecast.com/25029844-d956-4a08-aa14-ea89d026d8d8\" height=\"200\" width=\"100%\" title=\"AOTAutograd\" frameborder=\"0\" scrolling=\"no\"></iframe>","height":200,"description":"AOTAutograd is a cool new feature in functorch for capturing both forward and backward traces of PyTorch operators, letting you run them through a compiler and then drop the compiled kernels back into a normal PyTorch eager program. Today, Horace joins me to tell me how it works, what it is good to use for, and what our future plans for it are."}