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: Platform for Complete
Machine Learning Lifecycle
Mani Parkhe & Tomas Nykodym
Oct 4, 2018
Outline
Overview of ML development challenges
How MLflow tackles these
MLflow components
How to get started
Machine Learning
Development is Complex
ML Lifecycle
Delta
Data Prep
Training
Deploy
Raw Data
μ
λ θ Tuning
Scale
μ
λ θ Tuning
Scale
Scale
Scale
Model
Exchange
Governance
“I build 100s of models/day to lift revenue, using any library:
MLlib, PyTorch, R, etc. There’s no easy way to see what data
went in a model from a week ago, tune it and rebuild it.”
-- Chief scientist at ad tech firm
Example
Example
“Our company has 100 teams using ML worldwide. We can’t
share work across them: when a new team tries to run some
code, it often doesn’t even give the same result.”
-- Large consumer electronics firm
Custom ML Platforms
Facebook FBLearner, Uber Michelangelo, Google TFX
+ Standardize the data prep / training / deploy loop:
if you work with the platform, you get these!
– Limited to a few algorithms or frameworks
– Tied to one company’s infrastructure
Can we provide similar benefits in an open manner?
Introducing
Open machine learning platform
• Works with any ML library & language
• Runs the same way anywhere (e.g. any cloud)
• Designed to be useful for 1 or 1000+ person orgs
MLflow Components
Tracking
Record and query
experiments: code,
configs, results,
…etc
Projects
Packaging format
for reproducible
runs
on any platform
Models
General model format
that supports diverse
deployment tools
MLflow Tracking
Tracking
Record and query
experiments: code,
configs, results,
…etc
import mlflow
with mlflow.start_run():
mlflow.log_param("layers", layers)
mlflow.log_param("alpha", alpha)
# train model
mlflow.log_metric("mse", model.mse())
mlflow.log_artifact("plot", model.plot(test_df))
mlflow.tensorflow.log_model(model)
Demo
bathrooms: 1
bedrooms: 2
accommodates: 4
total_reviews: 45
cleanliness_rating: 9
location_rating: 10
checkin_rating: 10
zip_code: 94105
price: 150
f (x)
Model
listing attributes
based on data from insideairbnb.com
Goal: Predict Price of Airbnb Listings
Notebooks
Local Apps
Cloud Jobs
Tracking Server
UI
API
Python or
REST API
MLflow backend stores
1. Entity Store
• FileStore (local and REST)
• Database backed (coming soon)
2. Artifact Repository
• S3 backed store
• Azure Blob storage
• Google Cloud storage
• DBFS artifact repo
Learning More About MLflow
pip install mlflow to get started
Find docs & examples at mlflow.org
tinyurl.com/mlflow-slack
16
Break
MLflow Deep Dive Part #2
Tracking
Record and query
experiments: code,
data, config, results
Projects
Packaging format
for reproducible runs
on any platform
Models
General model format
that supports diverse
deployment tools
Closer look at MLflow Projects and Models
MLflow Deep Dive Part #2
2.1 MLflow Projects
• Motivation
• Quick Overview
• Examples
• Demo
2.2 MLflow Models
• Motivation
• Quick Overview
• About Flavors
• Examples
• Demo
Outline
2.1 MLflow Projects
• Motivation
• Quick Overview
• Examples
• Demo
MLflow Projects Motivation
Diverse set of tools
Diverse set of environments
Result: It is difficult to productionalize and share.
Project Spec
Code DataConfig
Local Execution
Remote Execution
MLflow Projects
Packaging format for reproducible ML runs
- Any code folder or Github repository
- Optional MLproject file with project configuration
Defines dependencies for reproducibility
- Conda (+ R, Docker, …) dependencies can be specified in MLproject
- Reproducible in (almost) any environment
Execution API for running projects locally or remote
- CLI / Python / R / Java
- mlflow run . -e train …
- mlflow.projects.run(., “train”, ...)
Example MLflow Project
my_project/
├── MLproject
│
│
│
│
│
├── conda.yaml
├── main.py
└── model.py
...
conda_env: conda.yaml
entry_points:
main:
parameters:
training_data: path
lambda: {type: float, default: 0.1}
command: python main.py {training_data} {lambda}
$ mlflow run git://<my_project>
mlflow.run(“git://<my_project>”, ...)
Airbnb Example Revisited
We have 2 other data scientists working on the project
• Aaron used XGBoost in python
• Amy used Keras in R
• They provided their code as MLflow project on github
Let’s test their code!
2.2 MLflow Models
• Motivation
• Quick Overview
• About Flavors
• Examples
• Demo
Inference Code
Batch & Stream Scoring
Serving ToolsML Frameworks
Model Format
Flavor 2Flavor 1
ML Frameworks
Inference Code
Batch & Stream Scoring
Serving Tools
Standard for ML models
MLflow Models
Packaging format for ML Models
- Any directory with MLmodel file
- Different flavors for different models
Defines dependencies for reproducibility
- Conda (+ R, Docker, … ?) dependencies can be specified in MLproject
- Deployable (almost) anywhere
Deployment APIs
- CLI / Python / R / Java
- mlflow pyfunc serve …
- mlflow.pyfunc.load(...).predict()
Example MLflow Model
my_model/
├── MLmodel
│
│
│
│
│
└── estimator/
├── saved_model.pb
└── variables/
...
Usable by tools that understand
TensorFlow model format
Usable by any tool that can run
Python (Docker, Spark, etc!)
run_id: 769915006efd4c4bbd662461
time_created: 2018-06-28T12:34
flavors:
tensorflow:
saved_model_dir: estimator
signature_def_key: predict
python_function:
loader_module: mlflow.tensorflow
>>> mlflow.tensorflow.log_model(...)
About Those Flavors
Models can generate several flavors
• Spark ML model generates spark, mleap and pyfunc flavors
Generic flavors provide abstraction layer
• pyfunc can be served locally, as spark_udf, on Azure ML, …
• By generating pyfunc flavor we get all above
Different Flavors Can be Loaded By Different Languages
• pyfunc in python, mleap in Java, crate in R, Keras in python and R
PyFunc - Generic Python Model
PyFunc is saved as “just a directory”:
./model_dir/
./MLmodel: configuration
<code>: code packaged with the model (specified in the MLmodel file)
<data>: data packaged with the model (specified in the MLmodel file)
<env>: Conda environment definition (specified in the MLmodel file)
Model loader specified in MLmodel file:
Arbitrary python code loaded dynamically at runtime
Loaded Pyfunc is “just an object” with a predict method:
predict(pandas.DataFrame) -> pandas.DataFrame | numpy.array
Airbnb Example Revisited (Again)
Let’s put our models to production!
• Predict from cli
• Set up a real time REST API prediction server
• Score big data in Spark
Advanced MLFlow - HyperParameters
34
HyperParam
Search Run
Train Model
Run
mlflow.log_metric()mlflow.get_metric()
Projects
Tracking
Models
mlflow run ... Logged Model
mlflow.log_artifact
Advanced MLFlow - Multistep Workflow
Get started with MLflow
pip install mlflow to get started
Find docs & examples at mlflow.org
tinyurl.com/mlflow-slack
36
Ongoing MLflow Roadmap
•TensorFlow, Keras, PyTorch, H2O, MLlib integrations ✔
•Java and R language APIs ✔
•Multi-step workflows
•Hyperparameter tuning
•Data source API based on Spark data sources
•Model metadata & management
Conclusion
Workflow tools can greatly simplify the ML lifecycle
• Improve usability for both data scientists and engineers
• Same way software dev lifecycle tools simplify development
Learn more about MLflow at mlflow.org
Thank you!
MLflow Design Philosophy
1. “API-first”, open platform
• Allow submitting runs, models, etc from any library & language
• Example: a “model” can just be a lambda function that MLflow can
then deploy in many places (Docker, Azure ML, Spark UDF, …)
Key enabler: built around REST APIs and CLI
MLflow Design Philosophy
2. Modular design
• Let people use different components individually (e.g., use
MLflow’s project format but not its deployment tools)
• Easy to integrate into existing ML platforms & workflows
Key enabler: distinct components (Tracking/Projects/Models)

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MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani Parkhe and Tomas Nykodym

  • 1. : Platform for Complete Machine Learning Lifecycle Mani Parkhe & Tomas Nykodym Oct 4, 2018
  • 2. Outline Overview of ML development challenges How MLflow tackles these MLflow components How to get started
  • 4. ML Lifecycle Delta Data Prep Training Deploy Raw Data μ λ θ Tuning Scale μ λ θ Tuning Scale Scale Scale Model Exchange Governance
  • 5. “I build 100s of models/day to lift revenue, using any library: MLlib, PyTorch, R, etc. There’s no easy way to see what data went in a model from a week ago, tune it and rebuild it.” -- Chief scientist at ad tech firm Example
  • 6. Example “Our company has 100 teams using ML worldwide. We can’t share work across them: when a new team tries to run some code, it often doesn’t even give the same result.” -- Large consumer electronics firm
  • 7. Custom ML Platforms Facebook FBLearner, Uber Michelangelo, Google TFX + Standardize the data prep / training / deploy loop: if you work with the platform, you get these! – Limited to a few algorithms or frameworks – Tied to one company’s infrastructure Can we provide similar benefits in an open manner?
  • 8. Introducing Open machine learning platform • Works with any ML library & language • Runs the same way anywhere (e.g. any cloud) • Designed to be useful for 1 or 1000+ person orgs
  • 9. MLflow Components Tracking Record and query experiments: code, configs, results, …etc Projects Packaging format for reproducible runs on any platform Models General model format that supports diverse deployment tools
  • 10. MLflow Tracking Tracking Record and query experiments: code, configs, results, …etc import mlflow with mlflow.start_run(): mlflow.log_param("layers", layers) mlflow.log_param("alpha", alpha) # train model mlflow.log_metric("mse", model.mse()) mlflow.log_artifact("plot", model.plot(test_df)) mlflow.tensorflow.log_model(model)
  • 11. Demo
  • 12. bathrooms: 1 bedrooms: 2 accommodates: 4 total_reviews: 45 cleanliness_rating: 9 location_rating: 10 checkin_rating: 10 zip_code: 94105 price: 150 f (x) Model listing attributes based on data from insideairbnb.com Goal: Predict Price of Airbnb Listings
  • 13. Notebooks Local Apps Cloud Jobs Tracking Server UI API Python or REST API
  • 14.
  • 15. MLflow backend stores 1. Entity Store • FileStore (local and REST) • Database backed (coming soon) 2. Artifact Repository • S3 backed store • Azure Blob storage • Google Cloud storage • DBFS artifact repo
  • 16. Learning More About MLflow pip install mlflow to get started Find docs & examples at mlflow.org tinyurl.com/mlflow-slack 16
  • 17. Break
  • 18. MLflow Deep Dive Part #2 Tracking Record and query experiments: code, data, config, results Projects Packaging format for reproducible runs on any platform Models General model format that supports diverse deployment tools Closer look at MLflow Projects and Models
  • 19. MLflow Deep Dive Part #2 2.1 MLflow Projects • Motivation • Quick Overview • Examples • Demo 2.2 MLflow Models • Motivation • Quick Overview • About Flavors • Examples • Demo Outline
  • 20. 2.1 MLflow Projects • Motivation • Quick Overview • Examples • Demo
  • 21. MLflow Projects Motivation Diverse set of tools Diverse set of environments Result: It is difficult to productionalize and share.
  • 22. Project Spec Code DataConfig Local Execution Remote Execution
  • 23. MLflow Projects Packaging format for reproducible ML runs - Any code folder or Github repository - Optional MLproject file with project configuration Defines dependencies for reproducibility - Conda (+ R, Docker, …) dependencies can be specified in MLproject - Reproducible in (almost) any environment Execution API for running projects locally or remote - CLI / Python / R / Java - mlflow run . -e train … - mlflow.projects.run(., “train”, ...)
  • 24. Example MLflow Project my_project/ ├── MLproject │ │ │ │ │ ├── conda.yaml ├── main.py └── model.py ... conda_env: conda.yaml entry_points: main: parameters: training_data: path lambda: {type: float, default: 0.1} command: python main.py {training_data} {lambda} $ mlflow run git://<my_project> mlflow.run(“git://<my_project>”, ...)
  • 25. Airbnb Example Revisited We have 2 other data scientists working on the project • Aaron used XGBoost in python • Amy used Keras in R • They provided their code as MLflow project on github Let’s test their code!
  • 26. 2.2 MLflow Models • Motivation • Quick Overview • About Flavors • Examples • Demo
  • 27. Inference Code Batch & Stream Scoring Serving ToolsML Frameworks
  • 28. Model Format Flavor 2Flavor 1 ML Frameworks Inference Code Batch & Stream Scoring Serving Tools Standard for ML models
  • 29. MLflow Models Packaging format for ML Models - Any directory with MLmodel file - Different flavors for different models Defines dependencies for reproducibility - Conda (+ R, Docker, … ?) dependencies can be specified in MLproject - Deployable (almost) anywhere Deployment APIs - CLI / Python / R / Java - mlflow pyfunc serve … - mlflow.pyfunc.load(...).predict()
  • 30. Example MLflow Model my_model/ ├── MLmodel │ │ │ │ │ └── estimator/ ├── saved_model.pb └── variables/ ... Usable by tools that understand TensorFlow model format Usable by any tool that can run Python (Docker, Spark, etc!) run_id: 769915006efd4c4bbd662461 time_created: 2018-06-28T12:34 flavors: tensorflow: saved_model_dir: estimator signature_def_key: predict python_function: loader_module: mlflow.tensorflow >>> mlflow.tensorflow.log_model(...)
  • 31. About Those Flavors Models can generate several flavors • Spark ML model generates spark, mleap and pyfunc flavors Generic flavors provide abstraction layer • pyfunc can be served locally, as spark_udf, on Azure ML, … • By generating pyfunc flavor we get all above Different Flavors Can be Loaded By Different Languages • pyfunc in python, mleap in Java, crate in R, Keras in python and R
  • 32. PyFunc - Generic Python Model PyFunc is saved as “just a directory”: ./model_dir/ ./MLmodel: configuration <code>: code packaged with the model (specified in the MLmodel file) <data>: data packaged with the model (specified in the MLmodel file) <env>: Conda environment definition (specified in the MLmodel file) Model loader specified in MLmodel file: Arbitrary python code loaded dynamically at runtime Loaded Pyfunc is “just an object” with a predict method: predict(pandas.DataFrame) -> pandas.DataFrame | numpy.array
  • 33. Airbnb Example Revisited (Again) Let’s put our models to production! • Predict from cli • Set up a real time REST API prediction server • Score big data in Spark
  • 34. Advanced MLFlow - HyperParameters 34 HyperParam Search Run Train Model Run mlflow.log_metric()mlflow.get_metric() Projects Tracking Models mlflow run ... Logged Model mlflow.log_artifact
  • 35. Advanced MLFlow - Multistep Workflow
  • 36. Get started with MLflow pip install mlflow to get started Find docs & examples at mlflow.org tinyurl.com/mlflow-slack 36
  • 37. Ongoing MLflow Roadmap •TensorFlow, Keras, PyTorch, H2O, MLlib integrations ✔ •Java and R language APIs ✔ •Multi-step workflows •Hyperparameter tuning •Data source API based on Spark data sources •Model metadata & management
  • 38. Conclusion Workflow tools can greatly simplify the ML lifecycle • Improve usability for both data scientists and engineers • Same way software dev lifecycle tools simplify development Learn more about MLflow at mlflow.org
  • 40. MLflow Design Philosophy 1. “API-first”, open platform • Allow submitting runs, models, etc from any library & language • Example: a “model” can just be a lambda function that MLflow can then deploy in many places (Docker, Azure ML, Spark UDF, …) Key enabler: built around REST APIs and CLI
  • 41. MLflow Design Philosophy 2. Modular design • Let people use different components individually (e.g., use MLflow’s project format but not its deployment tools) • Easy to integrate into existing ML platforms & workflows Key enabler: distinct components (Tracking/Projects/Models)