US 12,353,909 B2
Orchestration of machine learning (ML) workloads
Ryan Nicholas Comer, Round Rock, TX (US); Daniel L. Hamlin, Round Rock, TX (US); and Vivek Viswanathan Iyer, Austin, TX (US)
Assigned to Dell Products, L.P., Round Rock, TX (US)
Filed by Dell Products, L.P., Round Rock, TX (US)
Filed on Sep. 9, 2021, as Appl. No. 17/470,848.
Prior Publication US 2023/0074802 A1, Mar. 9, 2023
Int. Cl. G06F 9/48 (2006.01); G06F 9/50 (2006.01)
CPC G06F 9/4881 (2013.01) [G06F 9/505 (2013.01)] 17 Claims
OG exemplary drawing
 
1. An Information Handling System (IHS), comprising:
a processor; and
a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to:
receive an indication of a Machine Learning (ML) workload to be executed by the IHS;
orchestrate execution of the ML workload with respect to a plurality of ML resources coupled to the IHS, wherein orchestrate execution comprises discover a plurality of ML capabilities associated with the plurality of ML resources;
determine a selected subset of the plurality of discovered ML capabilities associated with the plurality of ML resources, wherein the subset of the plurality of discovered ML capabilities is selected based at least in part on sensor information captured by the IHS;
advertise the selected subset of the plurality of ML capabilities associated with the plurality of ML resources, wherein the indication of the ML workload is received from an application in response to the advertisement; and
modify a second ML workload into two or more ML workloads in response to a determination that the second ML workload has an ML requirement that does not match the selected subset of the plurality of ML capabilities, and wherein each of the two or more ML workloads has an ML requirement that matches the selected subset of the plurality of ML capabilities.