Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Making Deployments Easy with TF Serving | TF Ev...
Search
Rishit Dagli
May 11, 2021
Programming
1
160
Making Deployments Easy with TF Serving | TF Everywhere India
My talk at TensorFlow Everywhere India
Rishit Dagli
May 11, 2021
Tweet
Share
More Decks by Rishit Dagli
See All by Rishit Dagli
Fantastic Models and Where to Find Them
rishitdagli
0
75
Plant AI: Project Showcase
rishitdagli
0
120
Deploying an ML Model as an API | Postman Student Summit
rishitdagli
0
90
APIs 101 with Postman
rishitdagli
0
80
Deploying Models to production with Azure ML | Scottish Summit
rishitdagli
1
87
Computer Vision with TensorFlow, Getting Started
rishitdagli
0
290
Teaching Your Models to Play Fair | Global AI Student Conf
rishitdagli
1
170
Deploying Models to Production with TF Serving
rishitdagli
1
200
Superpower Your Android apps with ML: Android 11 | Devfest 2020
rishitdagli
1
81
Other Decks in Programming
See All in Programming
코딩 에이전트 체크리스트: Claude Code ver.
nacyot
0
930
システム成長を止めない!本番無停止テーブル移行の全貌
sakawe_ee
1
360
チームで開発し事業を加速するための"良い"設計の考え方 @ サポーターズCoLab 2025-07-08
agatan
1
470
「App Intent」よくわからんけどすごい!
rinngo0302
1
100
状態遷移図を書こう / Sequence Chart vs State Diagram
orgachem
PRO
2
200
“いい感じ“な定量評価を求めて - Four Keysとアウトカムの間の探求 -
nealle
2
12k
AI駆動のマルチエージェントによる業務フロー自動化の設計と実践
h_okkah
0
230
型で語るカタ
irof
0
700
Hack Claude Code with Claude Code
choplin
7
2.6k
Goで作る、開発・CI環境
sin392
0
260
ご注文の差分はこちらですか? 〜 AWS CDK のいろいろな差分検出と安全なデプロイ
konokenj
3
580
明示と暗黙 ー PHPとGoの インターフェイスの違いを知る
shimabox
2
620
Featured
See All Featured
Six Lessons from altMBA
skipperchong
28
3.9k
Building a Modern Day E-commerce SEO Strategy
aleyda
42
7.4k
Sharpening the Axe: The Primacy of Toolmaking
bcantrill
44
2.4k
Fantastic passwords and where to find them - at NoRuKo
philnash
51
3.3k
Raft: Consensus for Rubyists
vanstee
140
7k
How To Stay Up To Date on Web Technology
chriscoyier
790
250k
Side Projects
sachag
455
42k
ReactJS: Keep Simple. Everything can be a component!
pedronauck
667
120k
実際に使うSQLの書き方 徹底解説 / pgcon21j-tutorial
soudai
PRO
181
54k
Bootstrapping a Software Product
garrettdimon
PRO
307
110k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
53k
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
48
2.9k
Transcript
Making Deployments Easy with TF Serving Rishit Dagli High School
TEDx, TED-Ed Speaker rishit_dagli Rishit-dagli
“Most models don’t get deployed.”
of models don’t get deployed. 90%
Source: Laurence Moroney
Source: Laurence Moroney
• High School Student • TEDx and Ted-Ed Speaker •
♡ Hackathons and competitions • ♡ Research • My coordinates - www.rishit.tech $whoami rishit_dagli Rishit-dagli
• Devs who have worked on Deep Learning Models (Keras)
• Devs looking for ways to put their model into production ready manner Ideal Audience
Why care about ML deployments? Source: memegenerator.net
None
• Package the model What things to take care of?
• Package the model • Post the model on Server
What things to take care of?
• Package the model • Post the model on Server
• Maintain the server What things to take care of?
• Package the model • Post the model on Server
• Maintain the server Auto-scale What things to take care of?
• Package the model • Post the model on Server
• Maintain the server Auto-scale What things to take care of?
• Package the model • Post the model on Server
• Maintain the server Auto-scale Global availability What things to take care of?
• Package the model • Post the model on Server
• Maintain the server Auto-scale Global availability Latency What things to take care of?
• Package the model • Post the model on Server
• Maintain the server • API What things to take care of?
• Package the model • Post the model on Server
• Maintain the server • API • Model Versioning What things to take care of?
Simple Deployments Why are they inefficient?
None
Simple Deployments Why are they inefficient? • No consistent API
• No model versioning • No mini-batching • Inefficient for large models Source: Hannes Hapke
TensorFlow Serving
TensorFlow Serving TensorFlow Data validation TensorFlow Transform TensorFlow Model Analysis
TensorFlow Serving TensorFlow Extended
• Part of TensorFlow Extended TensorFlow Serving
• Part of TensorFlow Extended • Used Internally at Google
TensorFlow Serving
• Part of TensorFlow Extended • Used Internally at Google
• Makes deployment a lot easier TensorFlow Serving
The Process
• The SavedModel format • Graph definitions as protocol buffer
Export Model
SavedModel Directory
auxiliary files e.g. vocabularies SavedModel Directory
auxiliary files e.g. vocabularies SavedModel Directory Variables
auxiliary files e.g. vocabularies SavedModel Directory Variables Graph definitions
TensorFlow Serving
TensorFlow Serving
TensorFlow Serving Also supports gRPC
TensorFlow Serving
TensorFlow Serving
TensorFlow Serving
TensorFlow Serving
Inference
• Consistent APIs • Supports simultaneously gRPC: 8500 REST: 8501
• No lists but lists of lists Inference
• No lists but lists of lists Inference
• JSON response • Can specify a particular version Inference
with REST Default URL http://{HOST}:8501/v1/ models/test Model Version http://{HOST}:8501/v1/ models/test/versions/ {MODEL_VERSION}: predict
• JSON response • Can specify a particular version Inference
with REST Default URL http://{HOST}:8501/v1/ models/test Model Version http://{HOST}:8501/v1/ models/test/versions/ {MODEL_VERSION}: predict Port Model name
Inference with REST
• Better connections • Data converted to protocol buffer •
Request types have designated type • Payload converted to base64 • Use gRPC stubs Inference with gRPC
Model Meta Information
• You have an API to get meta info •
Useful for model tracking in telementry systems • Provides model input/ outputs, signatures Model Meta Information
Model Meta Information http://{HOST}:8501/ v1/models/{MODEL_NAME} /versions/{MODEL_VERSION} /metadata
Batch Inferences
• Use hardware efficiently • Save costs and compute resources
• Take multiple requests process them together • Super cool😎 for large models Batch inferences
• max_batch_size • batch_timeout_micros • num_batch_threads • max_enqueued_batches • file_system_poll_wait
_seconds • tensorflow_session _paralellism • tensorflow_intra_op _parallelism Batch Inference Highly customizable
• Load configuration file on startup • Change parameters according
to use cases Batch Inference
Also take a look at...
• Kubeflow deployments • Data pre-processing on server🚅 • AI
Platform Predictions • Deployment on edge devices • Federated learning Also take a look at...
bit.ly/tf-everywhere-ind Demos!
bit.ly/serving-deck Slides
Thank You rishit_dagli Rishit-dagli