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
150
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
65
Plant AI: Project Showcase
rishitdagli
0
110
Deploying an ML Model as an API | Postman Student Summit
rishitdagli
0
84
APIs 101 with Postman
rishitdagli
0
70
Deploying Models to production with Azure ML | Scottish Summit
rishitdagli
1
79
Computer Vision with TensorFlow, Getting Started
rishitdagli
0
280
Teaching Your Models to Play Fair | Global AI Student Conf
rishitdagli
1
150
Deploying Models to Production with TF Serving
rishitdagli
1
180
Superpower Your Android apps with ML: Android 11 | Devfest 2020
rishitdagli
1
77
Other Decks in Programming
See All in Programming
Grafana Loki によるサーバログのコスト削減
mot_techtalk
1
110
データの整合性を保つ非同期処理アーキテクチャパターン / Async Architecture Patterns
mokuo
41
15k
パスキーのすべて ── 導入・UX設計・実装の紹介 / 20250213 パスキー開発者の集い
kuralab
3
670
CloudNativePGがCNCF Sandboxプロジェクトになったぞ! 〜CloudNativePGの仕組みの紹介〜
nnaka2992
0
220
Ruby on cygwin 2025-02
fd0
0
140
AWS Organizations で実現する、 マルチ AWS アカウントのルートユーザー管理からの脱却
atpons
0
130
いりゃあせ、PHPカンファレンス名古屋2025 / Welcome to PHP Conference Nagoya 2025
ttskch
1
270
ペアーズでの、Langfuseを中心とした評価ドリブンなリリースサイクルのご紹介
fukubaka0825
2
300
ASP. NET CoreにおけるWebAPIの最新情報
tomokusaba
0
360
なぜイベント駆動が必要なのか - CQRS/ESで解く複雑系システムの課題 -
j5ik2o
7
2.5k
動作確認やテストで漏れがちな観点3選
starfish719
6
1k
Introduction to kotlinx.rpc
arawn
0
630
Featured
See All Featured
Side Projects
sachag
452
42k
Visualizing Your Data: Incorporating Mongo into Loggly Infrastructure
mongodb
44
9.4k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
53k
Java REST API Framework Comparison - PWX 2021
mraible
28
8.4k
個人開発の失敗を避けるイケてる考え方 / tips for indie hackers
panda_program
99
18k
4 Signs Your Business is Dying
shpigford
182
22k
Done Done
chrislema
182
16k
Speed Design
sergeychernyshev
25
780
The Myth of the Modular Monolith - Day 2 Keynote - Rails World 2024
eileencodes
20
2.4k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
47
5.2k
RailsConf & Balkan Ruby 2019: The Past, Present, and Future of Rails at GitHub
eileencodes
132
33k
The Cost Of JavaScript in 2023
addyosmani
47
7.3k
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