Combining Machine Learning frameworks with Apache Spark

1. Combining Machine
Learning frameworks with
Apache Spark
Tim Hunter
Hadoop Summit
June 2016

2. About me
Apache Spark contributor (since Spark 0.6)
Software Engineer @ Databricks
Ph.D. in Machine Learning @ UC Berkeley
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3. Founded by the team who
created Apache Spark
Offers a hosted service:
- Apache Spark in the cloud
- Notebooks
- Cluster management
- Production environment
About Databricks
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4. Apache Spark
The most active open-source project in big data

5. Large-scale machine learning on Apache Spark
Spark MLlib

6. MLlib’s Mission
MLlib’s mission is to make practical machine
learning easy and scalable.
• Easy to build machine learning applications
• Capable of learning from large-scale datasets
• Easy to integrate into existing workflows
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7. Algorithm Coverage
• Classification
• Logistic regression
• Naive Bayes
• Streaming logistic regression
• Linear SVMs
• Decision trees
• Random forests
• Gradient-boosted trees
• Multilayer perceptron
• Regression
• Ordinary least squares
• Ridge regression
• Lasso
• Isotonic regression
• Decision trees
• Random forests
• Gradient-boosted trees
• Streaming linear methods
• Generalized Linear Models
• Frequent itemsets
• FP-growth
• PrefixSpan
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Clustering
• Gaussian mixture models
• K-Means
• Streaming K-Means
• Latent Dirichlet Allocation
• Power Iteration Clustering
• Bisecting K-Means
Statistics
• Pearson correlation
• Spearman correlation
• Online summarization
• Chi-squared test
• Kernel density estimation
• Kolmogorov–Smirnov test
• Online hypothesis testing
• Survival analysis
Linear algebra
• Local dense & sparse vectors & matrices
• Normal equation for least squares
• Distributed matrices
• Block-partitioned matrix
• Row matrix
• Indexed row matrix
• Coordinate matrix
• Matrix decompositions
Recommendation
• Alternating Least Squares
Feature extraction & selection
• Word2Vec
• Chi-Squared selection
• Hashing term frequency
• Inverse document frequency
• Normalizer
• Standard scaler
• Tokenizer
• One-Hot Encoder
• StringIndexer
• VectorIndexer
• VectorAssembler
• Binarizer
• Bucketizer
• ElementwiseProduct
• PolynomialExpansion
• Quantile discretizer
• SQL transformer
Model import/export
Pipelines
List based on Spark 2.0

8. Outline
• ML workflows are complex
• Spark as a scheduler
• Integration within single-machine frameworks
• Unified cross-languages ML pipelines with MLlib
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9. ML workflows are complex
• Specify the pipeline
• Re-run on new data
• Inspect the results
• Tune the parameters
• Usually, each step of a pipeline is easier with one
framework
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10. ML Workflows are Complex
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Train model 1
Evaluate
Datasource 1
Datasource 2
Datasource 3
Extract featuresExtract features
Feature transform 1
Feature transform 2
Feature transform 3
Train model 2
Ensemble

11. Existing tools
• Scikit-learn
– Excellent documentation
– Standard for Python
• R
– Lots of packages available
• Pandas
– Very easy to use
• A lot of investment in tooling and education
– How to integrate big data with these tools?
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12. Common misconceptions
• Spark is for big data only
• Spark can only work with dedicated, distributed
libraries
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13. Spark as a scheduler
• A lot of tasks in ML are ”embarrassingly parallel”
• Use Spark for data management and for
scheduling
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14. One example: learning digits
• Learning tasks: given set of images, recognized
digits
• Standard benchmark dataset in computer vision
built by NIST:
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15. Training Deep Learning algorithms
• Training a neural network is hard:
• It is a sequential procedure (present one image after the
other to learn from)
• It can be sensitive to noise and order of images:
robustness analysis is critical
• Tuning the training parameters (descent rate, batch sizes,
etc.) is very important. Otherwise, learning is too slow or
gets stuck in a local minima. A lot of heuristics are used in
practice.
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16. TensorFlow as a training library
• A lot of algorithms have been presented for this
task, we will choose TensorFlow, from Google:
• Popular choice for neural network training and
deep learning
• Competitive performance
• Easy to experiment with
• Python interface makes it easy to integrate with
Spark
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17. Distributing TensorFlow computations
• Even if TF is used as a single-machine library, we
get speedups from Spark
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Distributed Cross Validation
...
Best
Model
Model #1
Training
Model #2
Training
Model #3
Training

18. Distributing TensorFlow computations
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Distributed Cross Validation
...
Best Model
Model #4
Training
Model #6
Training
Model #3
Training
Model #1
Training
Model #5
Training
Model #2
Training

19. Results
• Running a 2-layer neural network, and testing for
different update rates and different layer sizes
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0
3000
6000
9000
12000
1 node 2 nodes 13 nodes

20. Embedding deep learning in Spark
• Best known algorithms are essentially sequential
during training
• Careful selection of training parameters is critical
• Spark can help for fast iterations and find a good
set of parameters
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21. A data scientist’s wish list:
• Run original code on a production environment
• Use distributed data sources
• Use familiar APIs and libraries
• Distribute ML workload piece by piece
• Only distribute as needed
• Easily switch between local & distributed settings
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22. Example: sentiment analysis
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Given a review (text), predict the user’s rating.
Data from https://snap.stanford.edu/data/web-Amazon.html

23. ML Workflow
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Train model
Evaluate
Load data
Extract features
Review: This product doesn't seem to be made to last… Rating: 2
feature_vector: [0.1 -1.3 0.23 … -0.74] rating: 2.0
Regression: (review: String) => Double

24. Load Data
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built-in external
{ JSON }
JDBC
and more …
Data sources for DataFrames
LIBSVM
Train model
Evaluate
Load data
Extract features

25. Extract Features
words: [this, product, doesn't, seem, to, …]
feature_vector: [0.1 -1.3 0.23 … -0.74]
Review: This product doesn't seem to be made to last… Rating: 2
Prediction: 3.0
Train model
Evaluate
Load data
Tokenizer
Hashed Term Frequ.

26. Extract Features
words: [this, product, doesn't, seem, to, …]
feature_vector: [0.1 -1.3 0.23 … -0.74]
Review: This product doesn't seem to be made to last… Rating: 2
Prediction: 3.0
Linear regression
Evaluate
Load data
Tokenizer
Hashed Term Frequ.

27. Our ML workflow
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Cross Validation
Model
Training
Feature
Extraction
regularization
parameter:
{0.0, 0.1, ...}

28. Cross validation
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Cross Validation
...
Best Model
Model #1
Training
Model #2
Training
Feature
Extraction
Model #3
Training

29. Example
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30. A data scientist’s wish list:
• Run original code on a production environment
• Use distributed data sources
• Use familiar APIs and libraries
• Distribute ML workload piece by piece
• Only distribute as needed
• Easily switch between local & distributed settings
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31. DataFrame-based API for MLlib
a.k.a. “Pipelines” API, with utilities for constructing ML Pipelines
In 2.0, the DataFrame-based API will become the primary API for
MLlib.
• Voted by community
• org.apache.spark.ml, pyspark.ml
The RDD-based API will enter maintenance mode.
• Still maintained with bug fixes, but no new features
•org.apache.spark.mllib, pyspark.mllib
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32. Why ML persistence?
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Data Science Software Engineering
Prototype
(Python/R)
Create model
Re-implement model for
production (Java)
Deploy model

33. Why ML persistence?
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Data Science Software Engineering
Prototype
(Python/R)
Create Pipeline
• Extract raw features
• Transform features
• Select key features
• Fit multiple models
• Combine results to
make prediction
• Extra implementation work
• Different code paths
• Synchronization overhead
Re-implement Pipeline
for production (Java)
Deploy Pipeline

34. With ML persistence...
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Data Science Software Engineering
Prototype
(Python/R)
Create Pipeline
Persist model or Pipeline:
model.save(“s3n://...”)
Load Pipeline (Scala/Java)
Model.load(“s3n://…”)
Deploy in production

35. ML persistence status
Near-complete coverage in all Spark language APIs
• Scala & Java: complete (29 feature transformers, 21 models)
• Python: complete except for 2 algorithms
• R: complete for existing APIs
Single underlying implementation of models
Exchangeable data format
• JSON for metadata
• Parquet for model data (coefficients, etc.)
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36. A data scientist’s wish list:
• Run original code on a production environment
• Directly apply learned pipelines
• Use MLlib as export format
• Use distributed data sources
• Builtin Spark conversions
• Use familiar APIs and libraries
• Distribute ML workload piece by piece
• Easy to distribute the most common ML tasks
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37. What’s next?
Prioritized items on the 2.1 roadmap JIRA (SPARK-
15581):
• Critical feature completeness for the DataFrame-based API
– Multiclass logistic regression
– Statistics
• Python API parity & R API expansion
• Scaling & speed tuning for key algorithms: trees & ensembles
GraphFrames
• Release for Spark 2.0
• Speed improvements (join elimination, connected components)
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38. Get started
• Get involved: roadmap JIRA (SPARK-15581) +
mailing lists
• ML persistence blog post
http://databricks.com/blog/2016/05/31
• Try out the Apache Spark 2.0 preview release:
http://databricks.com/try
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39. Thank you!
spark.apache.org
spark-packages.org
databricks.com