| CPC G06N 20/00 (2019.01) [G06F 9/4881 (2013.01)] | 20 Claims |
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1. An autotuning computer system for performing stateless autotuning tasks with respect to a machine learning (ML) model, the autotuning computer system comprising:
a memory storing a scheduler circuit, wherein the scheduler circuit operates in accordance with machine executable instructions; and
one or more processors configured to access the memory and execute the machine executable instructions stored to:
receive, from the scheduler circuit, a scheduling request for an autotuning task, wherein the scheduling request comprises an autotuning stage, an autotuning task ID, and parameters;
receive a set of states of the autotuning task from a key-value store by using the autotuning task ID as a key, wherein the set of states of the autotuning task comprise a current stage and an average runtime per stage;
based at least on the average runtime per stage, determine a hardware resource, the hardware resource configured to generate at least one of the states of the autotuning task through execution of the autotuning task by the hardware resource;
load the states of the autotuning task at the hardware resource;
execute, by the hardware resource, the current stage with the loaded states, which generates new states to be used for executing a next stage; and
store the generated new states as values in the key-value store by using the autotuning task ID as the key, wherein decoupling of the states from computation to the key-value store forms the autotuning computer system as stateless.
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