TensorFlow Serving AMI With Nginx Auth | cloudimg Support

This is a repackaged open source software product wherein additional charges apply for cloudimg support services.

Why This AMI Instead of Manual Setup

Launching TensorFlow Serving from a raw Docker image means configuring authentication, writing systemd units, setting up reverse proxies, and hardening credentials - hours of work before your first prediction. This AMI eliminates that overhead. You get a secured, production-grade TF Serving stack that is ready to score inputs on first boot, with per-instance credentials automatically rotated so no two instances share secrets. Compared to an unprotected default TF Serving container, this image ensures your model endpoints are never exposed unauthenticated on the public internet.

Application Stack

The official tensorflow/serving CPU image runs as a Docker container managed by Docker Compose v2 and supervised by systemd. Two endpoints are exposed: a gRPC predict endpoint on port 8500 and a REST predict endpoint on port 8501. An nginx reverse proxy on port 80 fronts the REST API and enforces HTTP Basic authentication, protecting your model server from unauthorized access.

Secure First Boot

On first boot, a one-shot systemd unit generates a high-entropy password using OpenSSL, writes /etc/nginx/.htpasswd, and saves the credentials along with a sample curl command to /root/tensorflow-serving-credentials.txt (readable only by root). Every instance gets a unique password - no shared secrets across your fleet.

Sample Model and Model Replacement

Google's canonical half_plus_two SavedModel is bundled at /var/lib/tfserving/models/half_plus_two/1/ so the server has a working model on first boot. To deploy your own model, drop a new versioned directory under /var/lib/tfserving/models/ and restart the service. Multi-model serving is supported by adding additional model directories.

Getting Started

1. Launch the AMI on your preferred EC2 instance type.
2. SSH into the instance and retrieve credentials from /root/tensorflow-serving-credentials.txt.
3. Browse to http:///v1/models/half_plus_two with user "cloudimg" and the per-instance password to confirm the model status is AVAILABLE.
4. POST inference requests to /v1/models/half_plus_two:predict to score inputs.
5. Replace the sample model with your own TensorFlow SavedModel to begin production serving.

Use Case: E-Commerce Recommendation Scoring

An e-commerce team deploys two model versions under /var/lib/tfserving/models/ - a production model serving 90% of traffic and a challenger model serving 10%. By comparing conversion rates across versions through their application layer, the team validates model improvements before full rollout. The nginx auth layer ensures only authorized backend services can reach the scoring endpoints, while gRPC support keeps latency low for real-time product recommendations at scale.

Additional Use Cases

• Low-latency online inference for TensorFlow SavedModels via REST or gRPC
• A/B testing model versions with traffic splitting at the application layer
• Edge or regional model hosting for latency-sensitive workloads
• Multi-model serving from a single container for resource efficiency

cloudimg Support

24/7 technical support by email and live chat. Our engineers assist with TensorFlow Serving deployment, model upgrades, gRPC and REST integration, nginx hardening, and TLS termination. To schedule a guided deployment walkthrough or get help with your specific use case, contact our support team.

TensorFlow and the TensorFlow logo are trademarks of Google LLC. All other product and company names are trademarks or registered trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.