Principle:Tensorflow Serving Batch Scheduling Configuration

Knowledge Sources	TF Serving Performance
Domains	Performance, Scheduling
Last Updated	2026-02-13 17:00 GMT

Overview

A session-wrapping mechanism that transparently intercepts individual TensorFlow Session::Run() calls and groups them into batches for efficient execution.

Description

Batch scheduling wraps a TensorFlow Session with a BatchingSession that provides the same interface but internally batches requests. When a client calls Run(), the request is:

1. Converted into a BatchingSessionTask with the input tensors
2. Enqueued into a BasicBatchScheduler which groups tasks into batches
3. Blocked until the batch is processed

When a batch is ready (full or timed out), ProcessBatch():

1. Merges all input tensors by concatenating along the 0th (batch) dimension via MergeInputTensors()
2. Executes a single session->Run() on the merged batch
3. Splits output tensors back into individual results via SplitOutputTensors()

Usage

This is the core batching mechanism. It is created automatically when --enable_batching is set. Users control behavior through scheduling parameters (max_batch_size, timeout, thread count).

Theoretical Basis

# Abstract batch scheduling (NOT real implementation)
def batching_session_run(inputs):
    task = BatchingSessionTask(inputs, zeroth_dim_size=batch_dim(inputs))
    scheduler.enqueue(task)
    task.wait()  # Block until batch is processed
    return task.outputs

def process_batch(batch_of_tasks):
    merged_inputs = concatenate([t.inputs for t in batch_of_tasks], axis=0)
    merged_outputs = original_session.run(merged_inputs)
    for i, task in enumerate(batch_of_tasks):
        task.outputs = slice(merged_outputs, start=offset[i], size=task.zeroth_dim_size)
        task.notify_done()

Related Pages

Implemented By

• Implementation:Tensorflow_Serving_CreateBasicBatchingSession

Uses Heuristic

• Heuristic:Tensorflow_Serving_Batching_Thread_Tuning