Blog
The latest from Google Research
Running your models in production with TensorFlow Serving
Tuesday, February 16, 2016
Posted by Noah Fiedel, Software Engineer
Machine learning powers many Google product features, from
speech recognition in the Google app
to
Smart Reply in Inbox
to
search in Google Photos
. While decades of experience have enabled the software industry to establish best practices for building and supporting products, doing so for services based upon machine learning introduces
new and interesting challenges
.
Today, we announce the release of
TensorFlow Serving
, designed to address some of these challenges. TensorFlow Serving is a high performance, open source serving system for machine learning models, designed for production environments and optimized for
TensorFlow
.
TensorFlow Serving
is ideal for running multiple models, at large scale, that change over time based on real-world data, enabling:
model lifecycle management
experiments with multiple algorithms
efficient use of GPU resources
TensorFlow Serving makes the process of taking a model into production easier and faster. It allows you to safely deploy new models and
run experiments
while keeping the same server architecture and APIs. Out of the box it provides integration with TensorFlow, but it can be extended to serve other types of models.
Here’s how it works. In the simplified, supervised training pipeline shown below, training data is fed to the learner, which outputs a model:
Once a new model version becomes available, upon
validation
, it is ready to be deployed to the serving system, as shown below.
TensorFlow Serving uses the (previously trained) model to perform inference - predictions based on new data presented by its clients. Since clients typically communicate with the serving system using a
remote procedure call
(RPC) interface, TensorFlow Serving comes with a reference front-end implementation based on
gRPC
, a high performance, open source RPC framework from Google.
It is quite common to launch and iterate on your model over time, as new data becomes available, or as you improve the model. In fact, at Google, many pipelines run continuously, producing new model versions as new data becomes available.
TensorFlow Serving is written in C++ and it supports Linux. TensorFlow Serving introduces minimal overhead. In our benchmarks we recoded ~100,000 queries per second (QPS) per core on a 16 vCPU Intel Xeon E5 2.6 GHz
machine
, excluding gRPC and the TensorFlow inference processing time.
We are excited to share this important component of TensorFlow today under the Apache 2.0 open source license. We would love to hear your
questions
and
feature requests
on Stack Overflow and GitHub respectively. To get started quickly, clone the code from
github.com/tensorflow/serving
and check out this
tutorial
.
You can expect to keep hearing more about TensorFlow as we continue to develop what we believe to be one of the best machine learning toolboxes in the world. If you'd like to stay up to date, follow
@googleresearch
or
+ResearchatGoogle
, and keep an eye out for
Jeff Dean
's keynote address at
GCP Next 2016
in March.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
materials science
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2021
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
