Principle:Tensorflow Serving Hardware Tuning
| Knowledge Sources | |
|---|---|
| Domains | Performance, Hardware_Optimization |
| Last Updated | 2026-02-13 17:00 GMT |
Overview
A performance optimization process that tunes batch scheduling parameters to match the characteristics of the target hardware (GPU, CPU, memory constraints).
Description
Hardware tuning configures the batching system to maximize throughput while respecting latency constraints on specific hardware. Key tuning dimensions:
- Allowed batch sizes: Restricts batches to sizes that are efficient on the hardware. If the actual batch is smaller, inputs are padded up to the next allowed size.
- Variable-length input padding: For models with variable-length inputs (e.g., RNNs), pads tensors to uniform length within a batch.
- Batch timeout: Trades latency for throughput. Lower timeouts mean lower latency but smaller batches.
- Thread pool sizing: Matches compute threads to hardware parallelism.
The StreamingBatchScheduler provides an alternative to BasicBatchScheduler for latency-sensitive workloads, using per-task callbacks instead of blocking.
Usage
Tune batch parameters after establishing baseline performance. Start with large batch sizes and adjust down based on latency requirements. Use allowed_batch_sizes when the hardware has specific efficient execution sizes (e.g., powers of 2 on GPUs).
Theoretical Basis
# Abstract tuning tradeoff (NOT real implementation)
# Larger batch = higher throughput, higher latency
# Smaller batch = lower throughput, lower latency
# allowed_batch_sizes example: [8, 16, 32, 64, 128]
# If 20 requests arrive: batch padded to 32 (next allowed size)
# Wasted compute: 12/32 = 37.5% padding overhead
# Optimal tuning balances:
# throughput = batch_size / (compute_time + batch_wait_time)
# latency = batch_wait_time + compute_time(batch_size)
Related Pages
Implemented By
Uses Heuristic
Page Connections
Double-click a node to navigate. Hold to expand connections.
Principle
Implementation
Heuristic
Environment