TensorFlow Serving

E457353

TensorFlow Serving is a flexible, high-performance system for deploying and serving machine learning models in production, particularly those built with TensorFlow.

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Statements (51)

Predicate Object
instanceOf TensorFlow ecosystem component
machine learning infrastructure software
model serving system
open-source software
architecture servable-based modular architecture
component Loader
Manager
ModelServer NERFINISHED
Servable
Source
deploymentModel Docker container
Kubernetes NERFINISHED
cloud virtual machines
on-premises servers
developer Google
documentation https://www.tensorflow.org/tfx/guide/serving
feature A/B testing support via multiple model versions
CPU-only serving support
GPU acceleration support
batching of inference requests
canary model deployment
dynamic model configuration
high-performance inference serving
hot model swapping without downtime
model lifecycle management
model rollback
monitoring hooks via custom servables
multi-model serving
production model deployment
versioned model management
goal provide flexible, high-performance serving of machine learning models in production
license Apache License 2.0
optimizedFor TensorFlow models in SavedModel format
partOf TensorFlow NERFINISHED
programmingLanguage C++
Python
repository https://github.com/tensorflow/serving
supportsFramework Keras NERFINISHED
TensorFlow NERFINISHED
supportsLanguageBinding REST
gRPC NERFINISHED
supportsModelFormat SavedModel NERFINISHED
TensorFlow Hub module (via SavedModel) NERFINISHED
supportsPlatform Docker-compatible platforms
Linux
supportsProtocol HTTP/JSON
gRPC binary protocol
useCase large-scale production ML systems
microservice-based ML APIs
online prediction
real-time inference

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Full triples — surface form annotated when it differs from this entity's canonical label.

TensorFlow Extended usesLibrary TensorFlow Serving