US 12,112,148 B2
Configuring machine learning models for training and deployment using graphical components
Shaunak Gupte, Maharashtra (IN); Prashant Gaikwad, Cupertino, CA (US); Chandrahas Jagadish Ramalad, Milpitas, CA (US); and Bhushan Rupde, Maharashtra (IN)
Assigned to NVIDIA CORPORATION, Santa Clara, CA (US)
Filed by NVIDIA Corporation, Santa Clara, CA (US)
Filed on Jun. 8, 2022, as Appl. No. 17/806,056.
Claims priority of provisional application 63/208,934, filed on Jun. 9, 2021.
Claims priority of provisional application 63/208,483, filed on Jun. 8, 2021.
Prior Publication US 2022/0391176 A1, Dec. 8, 2022
Int. Cl. G06F 8/36 (2018.01); G06F 8/34 (2018.01); G06F 8/60 (2018.01); G06N 20/00 (2019.01)
CPC G06F 8/34 (2013.01) [G06F 8/36 (2013.01); G06F 8/60 (2013.01); G06N 20/00 (2019.01)] 18 Claims
OG exemplary drawing
 
1. A processor comprising:
one or more processing units to:
determine, based at least in part on one or more inputs to a first graphical user interface (GUI), one or more machine learning models and one or more processing operations corresponding to the one or more machine learning models;
determine, based at least in part on the one or more machine learning models and the one or more processing operations, one or more extension libraries;
generate a model component using the one or more machine learning models, the one or more processing operations, and the one or more extension libraries;
store the model component in a component library;
receive one or more selections of the model component for inclusion in a component pipeline corresponding to an application;
receive, using a second GUI, one or more selections of an inference component corresponding to the model component for inclusion in the component pipeline; and
compose the application based at least in part on the component pipeline, the application including the one or more machine learning models and the one or more processing operations.