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A Tale of Three Deep
Learning Frameworks:
TensorFlow, Keras, and Deep
Learning Pipelines
Brooke Wenig
Jules S. Damji
Spark + AI Summit, SF 6/5/2018
About Us . . .
Databricks Machine LearningInstructor
Data ScienceSolution Consultant@ Databricks
Software Engineering @Splunk & MyFitnessPal
MS Machine Learning(UCLA)
Fluentin Chinese
https://www.linkedin.com/in/brookewenig/
Brooke WenigJules S. Damji
Apache Spark Developer& Community
Advocate @Databricks
Program Chair Spark + AI Summit
Software engineering @Sun Microsystems,
Netscape, @Home, VeriSign, Scalix, Centrify,
LoudCloud/Opsware, ProQuest
https://www.linkedin.com/in/dmatrix
@2twitme
Agenda for Today’s Talk
• Impact of Big Data
• Why Apache Spark?
• Short Survey of 3 DL Frameworks
• TensorFlow
• Keras
• Deep Learning Pipelines
• Demo
• Q&A
What has Big Data Done to Us?
Permeated our livesSource : MIT
Hardest Part of AI isn’t AI, it’s Data
ML
Code
Configuration
Data	Collection
Data	
Verification
Feature	
Extraction
Machine	
Resource	
Management
Analysis	Tools
Process
Management	Tools
Serving
Infrastructure
Monitoring
“Hidden Technical Debt in Machine Learning Systems,” Google NIPS 2015
Figure 1: Onlya small fraction of real-world ML systems is composed of the ML
code. The required surrounding infrastructure is vast and complex.
What’s Apache Spark
& Why
Apache Spark: The First Unified Analytics Engine
Runtime	
Delta
Spark	Core	Engine
Big Data Processing
ETL + SQL + Streaming
Machine Learning
MLlib + SparkR
Uniquelycombines Data & AI technologies
Survey of Three Deep
Learning Frameworks
What’s TensorFlow?
• Open source from Google, 2015
• Current v1.8 API
• Fast: Backend C/C++
• Data flow graphs
• Nodes are functions/operators
• Edges are input or data (tensors)
• Lazy execution
• Eager execution (1.7)
TensorFlow Programming Stack
CPU GPU Android iOS …TPU
Use canned estimators
Build models
Keras	
Models
Why TensorFlow: Community
AF
AF
• 100K+ stars!
• 11M downloads
• Popular open-source code
• TensorFlow Hub & Blog
○ Code Examples &
Tutorials!
○ Learn + share from
others
Why TensorFlow: Tools
AF
AF
• Deploy + Serve Models• TensorBoard
• Visualize Tensors flow
TensorFlow: We Get it … So What?
• Steep learning curve, but powerful!!
• Low-level APIs, butoffers control!!
• Expert in Machine Learning, justlearn!!
• Yet, high-level Estimators help, you bet!!
• Better, Keras integration helps, indeed!!
What’s Keras?
• Open source Python Library APIs for Deep Learning
• Current v2.1.6 APIs François Chollet (Google)
• API spec: TensorFlow, CNTKand Theano
• Easy to UseHigh-Level DeclarativeAPIs!
• Build layers
– Great for Neural Network Applications
• Fast Experimentation,Modular & Extensible!
Keras Programming Stack
CPU GPU Android iOS …TPU
Use canned estimators
Specific Impl
models
Keras	API	Specification	
TF-Keras Theano-Keras CNTK
TensorFlow	
Workflow
.....
Why Keras?
• Focuses on Developer Experience
• Popular & Broader Community
• Supports multiple backends
• Modularity
• Sequential Layers
• Multi-layer input networks
model = Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))
model.add(Dense, 32, activation=’softmax’)
...
Transfer Learning &
Deep Learning Pipelines
What’s Transfer Learning?
• Training from scratch requires
• Enormousamounts of data
• A lot of compute resources & time
Intermediate
representations learned
for one task may be
useful for other related
tasks
IDEA
Trained Model
SoftMax
GIANT PANDA 0.9
RACCOON 0.05
RED PANDA 0.01
…
Transfer Learning as a Pipeline
Classifier
Dog/Cat?
When to use Transfer Learning?
• Dataset is small & similar
• Dataset is large & similar
• Dataset is small but different
• Dataset is large and different
Source: Andrej Karpathy’s Transfer Learning
What & Why Deep Learning Pipelines (DLP)?
• Open source from Databricks, 2017
• Current v1.0 APIs w/ Apache Spark 2.3
• Primarily in Python
• Ease of Use & Integration
• Spark MLlibPipelines & DataFrames
• TensorFlow & Keras
• SQL
– Deploying & Evaluating
• Distributed Hyperparameter Tuning
• Easy for Transfer Learning
DEMO
https://dbricks.co/dlf_sai_2018
Takeaways: Which One & What Language?
TensorFlow Keras
Takeaways: When to Use TF, Keras or DLP
Deep Learning Pipelines
• Low-level APIs & Control
• Visualize with
TensorBoard
• Train Models or Transfer
Learning
• Model Serving
• High-level APIs
• TensorFlowBackend
• LovePython
• Train models or
transfer learning
• Integration with Spark
MLlib Pipelines &
DataFrames
• Integrated with TF &
Keras
• Transfer Learning
Resources
Blogposts Talk, & webinars (http://databricks.com/blog)
• Deep Learning Pipelines
• GPU acceleration in Databricks
• Deep Learning and ApacheSpark
• Build Scalable Deep LearningPipelines
• Deep Learning course:fast.ai
• TensorFlowTutorials
• TensorFlowDev Summit
• Keras/TensorFlowTutorials
• MLFlow.org
Docs for Deep Learning on Databricks (http://docs.databricks.com)
• Deep Learning Pipelines Example
• ApacheSpark integration
Thank You!
Questions?
brooke@databricks.com
jules@databricks.com (@2twitme)

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A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, and Deep Learning Pipelines with Brooke Wenig and Jules Damji

  • 1. A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, and Deep Learning Pipelines Brooke Wenig Jules S. Damji Spark + AI Summit, SF 6/5/2018
  • 2. About Us . . . Databricks Machine LearningInstructor Data ScienceSolution Consultant@ Databricks Software Engineering @Splunk & MyFitnessPal MS Machine Learning(UCLA) Fluentin Chinese https://www.linkedin.com/in/brookewenig/ Brooke WenigJules S. Damji Apache Spark Developer& Community Advocate @Databricks Program Chair Spark + AI Summit Software engineering @Sun Microsystems, Netscape, @Home, VeriSign, Scalix, Centrify, LoudCloud/Opsware, ProQuest https://www.linkedin.com/in/dmatrix @2twitme
  • 3. Agenda for Today’s Talk • Impact of Big Data • Why Apache Spark? • Short Survey of 3 DL Frameworks • TensorFlow • Keras • Deep Learning Pipelines • Demo • Q&A
  • 4. What has Big Data Done to Us? Permeated our livesSource : MIT
  • 5. Hardest Part of AI isn’t AI, it’s Data ML Code Configuration Data Collection Data Verification Feature Extraction Machine Resource Management Analysis Tools Process Management Tools Serving Infrastructure Monitoring “Hidden Technical Debt in Machine Learning Systems,” Google NIPS 2015 Figure 1: Onlya small fraction of real-world ML systems is composed of the ML code. The required surrounding infrastructure is vast and complex.
  • 7. Apache Spark: The First Unified Analytics Engine Runtime Delta Spark Core Engine Big Data Processing ETL + SQL + Streaming Machine Learning MLlib + SparkR Uniquelycombines Data & AI technologies
  • 8. Survey of Three Deep Learning Frameworks
  • 9. What’s TensorFlow? • Open source from Google, 2015 • Current v1.8 API • Fast: Backend C/C++ • Data flow graphs • Nodes are functions/operators • Edges are input or data (tensors) • Lazy execution • Eager execution (1.7)
  • 10. TensorFlow Programming Stack CPU GPU Android iOS …TPU Use canned estimators Build models Keras Models
  • 11. Why TensorFlow: Community AF AF • 100K+ stars! • 11M downloads • Popular open-source code • TensorFlow Hub & Blog ○ Code Examples & Tutorials! ○ Learn + share from others
  • 12. Why TensorFlow: Tools AF AF • Deploy + Serve Models• TensorBoard • Visualize Tensors flow
  • 13. TensorFlow: We Get it … So What? • Steep learning curve, but powerful!! • Low-level APIs, butoffers control!! • Expert in Machine Learning, justlearn!! • Yet, high-level Estimators help, you bet!! • Better, Keras integration helps, indeed!!
  • 14. What’s Keras? • Open source Python Library APIs for Deep Learning • Current v2.1.6 APIs François Chollet (Google) • API spec: TensorFlow, CNTKand Theano • Easy to UseHigh-Level DeclarativeAPIs! • Build layers – Great for Neural Network Applications • Fast Experimentation,Modular & Extensible!
  • 15. Keras Programming Stack CPU GPU Android iOS …TPU Use canned estimators Specific Impl models Keras API Specification TF-Keras Theano-Keras CNTK TensorFlow Workflow .....
  • 16. Why Keras? • Focuses on Developer Experience • Popular & Broader Community • Supports multiple backends • Modularity • Sequential Layers • Multi-layer input networks model = Sequential() model.add(Dense(32, input_dim=784)) model.add(Activation('relu')) model.add(Dense, 32, activation=’softmax’) ...
  • 17. Transfer Learning & Deep Learning Pipelines
  • 18. What’s Transfer Learning? • Training from scratch requires • Enormousamounts of data • A lot of compute resources & time Intermediate representations learned for one task may be useful for other related tasks IDEA
  • 19. Trained Model SoftMax GIANT PANDA 0.9 RACCOON 0.05 RED PANDA 0.01 …
  • 20. Transfer Learning as a Pipeline Classifier Dog/Cat?
  • 21. When to use Transfer Learning? • Dataset is small & similar • Dataset is large & similar • Dataset is small but different • Dataset is large and different Source: Andrej Karpathy’s Transfer Learning
  • 22. What & Why Deep Learning Pipelines (DLP)? • Open source from Databricks, 2017 • Current v1.0 APIs w/ Apache Spark 2.3 • Primarily in Python • Ease of Use & Integration • Spark MLlibPipelines & DataFrames • TensorFlow & Keras • SQL – Deploying & Evaluating • Distributed Hyperparameter Tuning • Easy for Transfer Learning
  • 24. Takeaways: Which One & What Language?
  • 25. TensorFlow Keras Takeaways: When to Use TF, Keras or DLP Deep Learning Pipelines • Low-level APIs & Control • Visualize with TensorBoard • Train Models or Transfer Learning • Model Serving • High-level APIs • TensorFlowBackend • LovePython • Train models or transfer learning • Integration with Spark MLlib Pipelines & DataFrames • Integrated with TF & Keras • Transfer Learning
  • 26. Resources Blogposts Talk, & webinars (http://databricks.com/blog) • Deep Learning Pipelines • GPU acceleration in Databricks • Deep Learning and ApacheSpark • Build Scalable Deep LearningPipelines • Deep Learning course:fast.ai • TensorFlowTutorials • TensorFlowDev Summit • Keras/TensorFlowTutorials • MLFlow.org Docs for Deep Learning on Databricks (http://docs.databricks.com) • Deep Learning Pipelines Example • ApacheSpark integration