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Queueing Requests in Continuous Batching
In continuous batching, if new user requests arrive when the inference engine is operating at full capacity, the scheduler does not add them to the active batch immediately. Instead, these requests are placed in a queue and must wait until resources are freed up, for instance, after an existing sequence in the batch completes its generation.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Queueing Requests in Continuous Batching
Dynamic Request Scheduling Scenario
An inference engine using a continuous batching strategy is actively processing a set of user requests. In the brief interval between two processing iterations, the scheduler successfully incorporates a newly arrived request into the active batch. What is the most critical condition that must have been met for the scheduler to make this decision?
In a system using continuous batching, a new user request that arrives while an existing batch is being processed must wait until all requests in that current batch are fully completed before it can be considered for processing.
Learn After
An inference engine using a continuous batching scheduler is operating at maximum capacity, meaning it cannot immediately process any more sequences. When new user requests arrive under these conditions, they are placed in a waiting queue. What is the primary trade-off the system is making by implementing this queueing mechanism?
An inference engine using a continuous batching strategy is currently processing a set of text generation requests that fully utilizes its processing capacity. At this point, a new, additional request arrives. What is the most likely immediate action the system's scheduler will take regarding this new request?
Continuous Batching Scheduler Behavior