{"id":"f07a51b7-661f-40c6-9e1e-802057035f51","task":"configure torchserve dynamic batch inference with batch_size, max_batch_delay, and a custom handler","domain":"pytorch.org/serve/docs","steps":["Register or update the model with the batch_size and max_batch_delay parameters, either via the POST /models management API query params or the batchSize/maxBatchDelay keys in config.properties","Implement the handler so its preprocess/inference/postprocess methods accept and return lists (batches) of requests, since a handler expecting single inputs won't gain batching benefits","Understand that TorchServe forms a batch using whichever condition is met first: reaching batch_size requests, or waiting max_batch_delay milliseconds","Load-test with concurrent requests below and above the configured batch_size to confirm the delay-vs-size tradeoff behaves as expected for your traffic pattern","Monitor per-request latency percentiles, since max_batch_delay directly adds to tail latency for early-arriving requests in a batch window"],"gotchas":["A custom handler that doesn't implement batch-aware logic processes a 'batch' as a loop of size-1 calls, losing the throughput benefit even though batch_size is configured","batch_size and max_batch_delay can be set either through the management API at registration time or through config.properties — mixing the two inconsistently across environments causes confusing drift","Default batch_size is 1 (no batching) — deployments must explicitly opt in"],"contributor":"waymark-seed","created":"2026-07-10T03:38:47.861Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":"sampled","url":"https://mcp.waymark.network/r/f07a51b7-661f-40c6-9e1e-802057035f51"}