Principle:Tensorflow Serving Batching Enablement
| Knowledge Sources | |
|---|---|
| Domains | Performance, Configuration |
| Last Updated | 2026-02-13 17:00 GMT |
Overview
A server configuration pattern that activates request batching via command-line flags, enabling TensorFlow Serving to aggregate individual inference requests into efficient batches.
Description
Batching enablement is the first step in configuring TensorFlow Serving for high-throughput inference. By default, each inference request executes independently, which underutilizes GPU parallelism. Enabling batching allows the server to transparently combine multiple requests into a single batch, improving throughput at the cost of slightly increased per-request latency.
The batching system wraps the TensorFlow session with a BatchingSession that intercepts Run() calls, queues them, and dispatches batched executions when either the batch is full or a timeout expires.
Batching is configured through:
- --enable_batching CLI flag to activate the system
- --batching_parameters_file to specify scheduling parameters
- --enable_per_model_batching_parameters to read model-specific params
Usage
Enable batching when serving models on GPUs where batch execution is significantly faster than individual execution. Particularly important for models with high compute-to-IO ratios (large neural networks, transformers).
Theoretical Basis
# Abstract batching configuration (NOT real implementation)
# Without batching: each request runs independently
for request in incoming_requests:
result = session.run(request) # GPU underutilized
# With batching: requests are grouped
batch = collect_requests(max_size=32, timeout_ms=10)
results = session.run(batch) # GPU fully utilized
split_and_return(results, batch)
The throughput improvement depends on batch size and hardware:
- GPU: 2-10x throughput improvement with batching
- CPU: Moderate improvement from better cache utilization