OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Infrastructure & Serving

Simple Tensorflow Serving

Simple TensorFlow Serving is a self-hosted inference service that loads machine-learning models and exposes them through REST/HTTP APIs. It documents multi-model and version configuration, generated client code, raw-file image inference and GPU-accelerated deployment.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Simple Tensorflow Serving — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a self-hosted serving layer between trained models and HTTP clients, particularly when model configuration, generated clients or multiple versions are useful.

Who it is for

  • Teams that need to expose trained models to applications through HTTP inference requests.
  • Operators managing multiple model deployments or versions.
  • Developers who want generated client code or command-line access to model endpoints.

Documented uses

  • Host a TensorFlow SavedModel behind an inference endpoint.
  • Configure multiple models and select a model version for inference.
  • Generate client code for calling a served model.
  • Send raw image files to image models.
  • Configure GPU-accelerated inference for deployments with the required host setup.
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 license. The repository documents pip, source, Docker, Docker Compose and Kubernetes deployment paths.

  • The quick-start example loads a TensorFlow SavedModel from a local path.
  • The README lists support for multiple frameworks and formats, but the detailed configuration example shows TensorFlow and MXNet entries.
  • GPU use requires additional CUDA files, NVIDIA devices and container configuration.
  • Generated clients are documented for languages including Bash, Python, Go and JavaScript.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start a server with a TensorFlow SavedModel

Install the package, point the serving command at a SavedModel directory and open the documented local dashboard.

Before you start

  • A TensorFlow SavedModel at a local model path.
  • Python and pip for the documented package installation.

Steps

  1. Install the package with the command below.

    Documented command
    pip install simple_tensorflow_serving

  2. Start the service with simple_tensorflow_serving --model_base_path="./models/tensorflow_template_application_model", substituting the path to the SavedModel.

  3. Open http://127.0.0.1:8500 in a browser to view the dashboard.

Expected outcome

A serving process is started with the selected model, and the README identifies the local dashboard address.

Guide limitations

  • The guide documents server startup and dashboard access, not a successful inference request.
  • The example assumes a model directory at the supplied path.
Documentation 1 ↗
Source-based practical guide

Configure multiple models and versions

Define multiple models in a configuration file and start the serving process with that file.

Before you start

  • The serving package installed.
  • Model directories and paths to list in the configuration file.

Steps

  1. Create a JSON configuration with a model_config_list containing each model's name, base path and platform.

  2. Start the service with simple_tensorflow_serving --model_config_file="./examples/model_config_file.json", using the path to the configuration file.

Expected outcome

The service is configured to serve the models listed in the file; the README also describes automatic detection and reloading of added or removed model versions.

Guide limitations

  • The documented example includes TensorFlow and MXNet model entries; it does not establish compatibility for every listed framework in every environment.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Simple Tensorflow ServingHost a TensorFlow SavedModel behind an inference endpoint.Server, Cloud, Self-hostedThe project is open source under the Apache-2.0 license. The repository documents pip, source, Docker, Docker Compose and Kubernetes deployment paths.
MlserverServe trained models through REST or gRPC.ServerThe project is open source under the Apache License 2.0. Its README lists Python 3.9–3.12 as supported; it marks Python 3.7, 3.8, and 3.13 unsupported.
ZhilightRun inference for supported language models on local GPU infrastructure.Linux, Server, Self-hostedThe project is a public GitHub repository licensed under Apache-2.0. The README documents building or installing from source and provides a Docker image.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
7 November 2026
Original website title
tobegit3hub/simple_tensorflow_serving · Official homepage title · retained in its source language
How we review sourcesSuggest a correction