dhi.io/tensorflow-serving
Tensorflow Serving
A flexible, high-performance serving system for machine learning models designed for production environments
pushed 1 day ago
linux/amd64, linux/arm64
136.01 MB
for linux/amd64
28
for linux/amd64
Active support
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Build Level 3
Tamper-evident proof of how and from what sources this image was built. Build Level 3 is the highest the SLSA build track defines.
CycloneDX SBOM + SPDX SBOM
A software bill of materials in both CycloneDX and SPDX formats so it drops straight into your existing tooling. Audit exactly what you're shipping.
A Vulnerability Exploitability exchange document, Docker's assessment of which CVEs actually affect this image and which don't apply, so you can focus on real risk instead of chasing false positives.
Available
A link to everything used to build the image, package source code, Git repos, and build files, so you can audit or reproduce the build and stay compliant with open source licenses.
Every known vulnerability in this image, shown in full rather than hidden. The VEX data flags which ones actually apply, so you can tell real exposure from noise before you ship.
Exactly what changed in this build, down to the package bumps and fixes behind the version you're pulling.
Verifies no keys, tokens, or credentials were accidentally baked into the image.
The image layers were scanned for known malware signatures before publishing.
About TensorFlow Serving
TensorFlow Serving is a flexible, high-performance serving system for machine learning models designed for production environments. It is part of the TensorFlow Extended (TFX) platform and provides a robust infrastructure for deploying machine learning models at scale.
This Docker Hardened Image provides the CPU variant of TensorFlow Serving, optimized for inference workloads on CPU-based infrastructure.
TensorFlow Serving supports model versioning, allowing you to deploy new model versions without taking down the service. It automatically manages model lifecycle, loading new versions and unloading old ones based on configurable policies. The system is optimized for low latency and high throughput, making it suitable for production workloads.
TensorFlow Serving provides both REST and gRPC APIs for making predictions. The REST API is convenient for development and testing, while the gRPC API offers better performance for production deployments. The system supports batching multiple requests together to improve CPU utilization and overall throughput.
TensorFlow Serving is widely used in production systems for serving recommendations, image classification, natural language processing, and other machine learning applications at scale.
For more details, visit https://www.tensorflow.org/tfx/guide/serving.
About Docker Hardened Images
Docker Hardened Images are built to meet the highest security and compliance standards. They provide a trusted foundation for containerized workloads by incorporating security best practices from the start.
Why use Docker Hardened Images?
These images are published with zero-known CVEs, include signed provenance, and come with a complete Software Bill of Materials (SBOM) and VEX metadata. They're designed to secure your software supply chain while fitting seamlessly into existing Docker workflows.
Trademarks
TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc. All rights in the marks are reserved to Google Inc. Any use by Docker is for referential purposes only and does not indicate sponsorship, endorsement, or affiliation.