TensorFlow Serving 2 on Ubuntu 24.04

## TensorFlow Serving 2 on Ubuntu 24.04 by cloudimg

TensorFlow Serving 2 on Ubuntu 24.04 LTS (Noble Numbat), purpose built for Microsoft Azure and maintained by cloudimg. TensorFlow Serving is Google's high performance, production ready ML model server. It loads TensorFlow SavedModel artefacts from disk and exposes them over gRPC and REST APIs with automatic versioning, dynamic batching, and graceful model reloading. Apache 2.0 licensed and used at Google scale.

Why Choose cloudimg?

* 24/7 Expert Support with guaranteed 24 hour response for all requests and one hour average for critical issues. Contact support@cloudimg.co.uk

* Production Ready from Launch Pre configured, security patched, validated before publication

* Azure Native Integration Built with Azure Linux Agent, cloud init, and Gen2 Hyper V support

* Sample Model Pre Loaded The official half_plus_two model (y = 0.5x + 2) is staged at /data/models/half_plus_two/1 so you can prove inference works in seconds without uploading anything

What is Included

* TensorFlow Serving 2.16 via the official tensorflow/serving Docker image (Google maintained)

* Docker CE and Docker Compose v2 plugin from the official Docker APT repository

* gRPC API on TCP 8500 and REST API on TCP 8501

* Sample SavedModel half_plus_two pre staged for instant POST /v1/models/half_plus_two:predict validation

* /data/models bind mount with Linux directory layout model_name/version/saved_model.pb so customers can drop in their own models without restarting the container

* tfserving.service systemd oneshot wrapper around docker compose for clean systemctl restart and ExecStop docker compose down

* tfserving-firstboot.service systemd oneshot that generates a per VM API key placeholder for use with optional nginx reverse proxy auth

* Ubuntu 24.04 LTS base with latest security patches applied at build time

* Azure Linux Agent for seamless cloud integration and SSH key injection

Use Cases

* Real time inference for image classification, NLP, recommendation, fraud detection, and forecasting models

* A/B testing and canary deployment of multiple model versions side by side

* Model warm pool serving with dynamic batching for cost effective GPU or CPU utilisation

* Backend for production ML pipelines built with TensorFlow, Keras, or TF Hub

* Microservice in a Kubernetes or Service Mesh architecture (gRPC native)

* On premise sovereign or air gapped ML serving for regulated industries

Technical Specifications

* Operating System: Ubuntu 24.04 LTS (Noble Numbat)

* TensorFlow Serving Version: 2.16.0 (Google official Docker image)

* Docker: Docker CE 29 from download.docker.com noble repository

* Sample Model: half_plus_two (TensorFlow Serving testdata) staged at /data/models/half_plus_two/1

* Ports: 8500 (gRPC), 8501 (REST)

* Default User: azureuser (sudo enabled)

* Service Management: systemd (tfserving.service, tfserving-firstboot.service)

* Recommended Size: Standard_B2s for the bundled half_plus_two demo and lightweight models; D2s_v5 or larger for production with multi gigabyte models, GPU SKUs for accelerated inference

* VM Generation: Hyper V Gen2 with UEFI boot

Security Notes

TensorFlow Serving has no built in authentication. For production deployments place a reverse proxy (nginx, Envoy, or an Azure Application Gateway) in front and enforce authentication, TLS, and rate limiting at that layer. Do not expose ports 8500 or 8501 directly to the public internet without first protecting them.

Support

cloudimg provides 24/7/365 expert technical support. Contact support@cloudimg.co.uk or visit www.cloudimg.co.uk for the latest documentation and deployment guides.