Implementation:Tensorflow Serving SavedModel Download

Knowledge Sources	TensorFlow Serving TF Serving Kubernetes
Domains	Data_Preparation, Deployment
Last Updated	2026-02-13 17:00 GMT

Overview

Concrete tool for downloading and extracting pre-trained SavedModels from TensorFlow Hub or other sources for Kubernetes deployment.

Description

The TensorFlow Serving Kubernetes tutorial demonstrates downloading a ResNet model from TensorFlow Hub using wget and tar. The model archive is extracted into a versioned directory (/tmp/resnet/123/) where 123 is an arbitrary version number.

This is an external tool documentation — the implementation uses standard Unix commands rather than a repository API.

Usage

Use this when deploying a pre-trained model from TensorFlow Hub. For custom models, use SavedModelBuilder export instead.

Code Reference

Source Location

• Repository: tensorflow/serving
• File: tensorflow_serving/g3doc/serving_kubernetes.md (L27-43)

Signature

# Download model from TF Hub
wget "https://tfhub.dev/tensorflow/resnet_50/classification/1?tf-hub-format=compressed" \
    -O /tmp/resnet.tar.gz

# Extract into versioned directory
mkdir -p /tmp/resnet/123
tar xvfz /tmp/resnet.tar.gz -C /tmp/resnet/123/

Import

# Shell commands — no code import needed
# Requires: wget, tar, mkdir

I/O Contract

Inputs

Name	Type	Required	Description
Model URL	string	Yes	TF Hub URL or other model download URL
Version number	integer	Yes	Version directory name (e.g., 123)
Base path	string	Yes	Local directory for model storage

Outputs

Name	Type	Description
SavedModel directory	Directory	<base_path>/<version>/ containing saved_model.pb and variables/

Usage Examples

Download ResNet from TF Hub

# Download and extract
mkdir -p /tmp/resnet/123
wget "https://tfhub.dev/tensorflow/resnet_50/classification/1?tf-hub-format=compressed" \
    -O /tmp/resnet.tar.gz
tar xvfz /tmp/resnet.tar.gz -C /tmp/resnet/123/

# Verify structure
ls /tmp/resnet/123/
# Output: saved_model.pb  variables/

Related Pages

Implements Principle

• Principle:Tensorflow_Serving_SavedModel_Preparation

Requires Environment

• Environment:Tensorflow_Serving_Kubernetes_Deployment_Environment