Introduction

TensorFlow Serving is a flexible, high-performance serving system for machine learning models designed specifically for production environments. It provides easy integration with TensorFlow models and can handle multiple models and versions, allowing developers to focus on model deployment rather than the intricacies of infrastructure management. A crucial part of deploying models using TensorFlow Serving involves ensuring that data is properly processed both before and after inference—these stages are commonly known as pre-processing and post-processing.

In this article, we'll explore where pre-processing and post-processing steps should be executed when a TensorFlow (TF) model is served using TensorFlow Serving, along with technical explanations and examples. We'll also provide a summary table for quick reference.

Pre-Processing Steps

Pre-processing refers to the manipulation of raw input data to make it suitable for a machine learning model. This may involve normalization, formatting, feature extraction, or other data transformations necessary for the model to understand the input.

Where Should Pre-Processing be Executed?

1. Client-Side Pre-Processing:
   • Executing pre-processing on the client-side involves manipulating data before sending it over the network to the TensorFlow Serving endpoint. This approach can reduce server overhead and decreases latency, as the server has less data processing workload.
   • Considerations: Ensures that the client environment is consistent with the training environment to avoid data discrepancies.
2. Server-Side (Inline) Pre-Processing:
   • When pre-processing is done server-side, the model server itself manages the transformation of raw input data. This can be implemented by including pre-processing logic within the model graph or creating a custom TensorFlow Serving model server with pre-processing steps baked in.
   • Considerations: This approach centralizes pre-processing logic, ensuring consistency across all clients.

Here's a simple example of pre-processing in Python before invoking a TensorFlow Serving model:

1import numpy as np
2import requests
3
4# Pre-process data: Normalize inputs
5def normalize(input_data):
6    return (input_data - np.mean(input_data)) / np.std(input_data)
7
8# Prepare data
9data = np.array([10, 20, 30, 40, 50])
10normalized_data = normalize(data)
11
12# Serve data to TensorFlow Serving
13payload = {"signature_name": "serving_default", "instances": normalized_data.tolist()}
14response = requests.post('http://localhost:8501/v1/models/your_model:predict', json=payload)

Post-Processing Steps

Post-processing is the process of transforming the model's output into a meaningful format for end-users. This might involve decoding, formatting, and applying business logic to the model's prediction.

Where Should Post-Processing be Executed?

1. Client-Side Post-Processing:
   • Here, the final transformation and interpretation of the model's output are done after the response has been received from TensorFlow Serving. This is often simpler if client-sided logic needs customization or user-specific handling.
   • Considerations: It places more responsibility on the distributed client-side environment, but allows greater flexibility for custom user interactions.
2. Server-Side (Inline) Post-Processing:
   • Implementing post-processing on the server ensures that the data returned to clients is immediately usable and often application-ready. It may be embedded as part of the response middleware or directly incorporated into the TensorFlow graph.
   • Considerations: Centralizes response transformation, ensuring all clients receive standardized output.

Here's an example of client-side post-processing in Python:

1import requests
2
3response = requests.get('http://localhost:8501/v1/models/your_model:predict')
4predictions = response.json()['predictions']
5
6# Post-process by selecting top 1 prediction (e.g., in a classification task)
7top_prediction = max(predictions)
8print("Predicted label:", top_prediction)

Summary Table

Here's a quick overview of where pre-processing and post-processing should be executed along with some key considerations:

Conclusion

Deciding where to execute pre-processing and post-processing depends on several factors, including the location of computation resources, latency requirements, and the need for consistency and flexibility. Client-side processing often offers flexibility and reduces server load, while server-side processing provides centralized, consistent execution of processing logic. Ultimately, the choice should align with your application requirements and infrastructure constraints to ensure efficient model deployment with TensorFlow Serving.