Blog
The latest news from Google AI
Announcing TensorFlow 0.8 – now with distributed computing support!
Wednesday, April 13, 2016
Posted by Derek Murray, Software Engineer
Google uses machine learning across a wide range of its products. In order to continually improve our models, it's crucial that the training process be as fast as possible. One way to do this is to run
TensorFlow
across hundreds of machines, which shortens the training process for some models from weeks to hours, and allows us to experiment with models of increasing size and sophistication. Ever since we released TensorFlow as an open-source project, distributed training support has been one of the most requested features. Now the wait is over.
Today, we're excited to release TensorFlow 0.8 with distributed computing support, including everything you need to train distributed models on your own infrastructure. Distributed TensorFlow is powered by the high-performance
gRPC
library, which supports training on hundreds of machines in parallel. It complements our recent announcement of
Google Cloud Machine Learning
, which enables you to train and serve your TensorFlow models using the power of the Google Cloud Platform.
To coincide with the TensorFlow 0.8 release, we have published a
distributed trainer
for the I
nception image classification
neural network in the TensorFlow models repository. Using the distributed trainer, we trained the Inception network to 78% accuracy in less than 65 hours using 100 GPUs. Even small clusters—or a couple of machines under your desk—can benefit from distributed TensorFlow, since adding more GPUs improves the overall throughput, and produces accurate results sooner.
TensorFlow can speed up Inception training by a factor of 56, using 100 GPUs.
The distributed trainer also enables you to scale out training using a cluster management system like
Kubernetes
. Furthermore, once you have trained your model, you can deploy to production and
speed up inference using TensorFlow Serving on Kubernetes
.
Beyond distributed Inception, the 0.8 release includes
new libraries
for defining your own distributed models. TensorFlow's distributed architecture permits a great deal of flexibility in defining your model, because every process in the cluster can perform general-purpose computation. Our previous system
DistBelief
(like many systems that have followed it) used special "parameter servers" to manage the shared model parameters, where the parameter servers had a simple read/write interface for fetching and updating shared parameters. In TensorFlow, all computation—including parameter management—is represented in the dataflow graph, and the system maps the graph onto heterogeneous devices (like multi-core CPUs, general-purpose GPUs, and mobile processors) in the available processes. To make TensorFlow easier to use, we have included Python libraries that make it easy to write a model that runs on a single process and scales to use multiple replicas for training.
This architecture makes it easier to scale a single-process job up to use a cluster, and also to experiment with novel architectures for distributed training. As an example, my colleagues have recently shown that
synchronous SGD with backup workers
, implemented in the TensorFlow graph, achieves improved time-to-accuracy for image model training.
The current version of distributed computing support in TensorFlow is just the start. We are continuing to research ways of improving the performance of distributed training—both through engineering and algorithmic improvements—and will share these improvements with the community
on GitHub
. However, getting to this point would not have been possible without help from the following people:
TensorFlow training libraries
- Jianmin Chen, Matthieu Devin, Sherry Moore and Sergio Guadarrama
TensorFlow core
- Zhifeng Chen, Manjunath Kudlur and Vijay Vasudevan
Testing
- Shanqing Cai
Inception model architecture
- Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Jonathon Shlens and Zbigniew Wojna
Project management
- Amy McDonald Sandjideh
Engineering leadership
- Jeff Dean and Rajat Monga
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
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
2020
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
.
