Optimizing terascale machine learning pipelines with KeystoneML
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Spark Summit 2016 talk by Evan Sparks
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Optimizing terascale machine learning pipelines with KeystoneML
- 1. OPTIMIZINGTERASCALE MACHINE LEARNING PIPELINES WITH Evan R. Sparks, UC Berkeley AMPLab with ShivaramVenkataraman,Tomer Kaftan, Michael Franklin, Benjamin Recht MLKeystone Apache
- 2. WHAT’S A MACHINE LEARNING PIPELINE?
- 3. A STANDARD MACHINE LEARNING PIPELINE Right? Data Train Classifier Model
- 4. A STANDARD MACHINE LEARNING PIPELINE That’s more like it! Data Train Linear Classifier Model Feature Extraction Test Data Predictions
- 5. A REAL PIPELINE FOR IMAGE CLASSIFICATION Inspired by Coates & Ng, 2012 Data Image Parser Normalizer Convolver sqrt,mean Zipper Linear Solver Symmetric Rectifier ident,abs ident,mean Global Pooling Pooler Patch Extractor Patch Whitener KMeans Clusterer Feature Extractor Label Extractor ModelLinear Mapper Test Data Label Extractor Feature Extractor Test Error Error Computer
- 6. Data Image Parser Normalizer Convolver sqrt,mean Zipper Linear Solver Symmetric Rectifier ident,abs ident,mean Global Pooling Pooler Patch Extractor Patch Whitener KMeans Clusterer Feature Extractor Label Extractor Linear Mapper Model Test Data Label Extractor Feature Extractor Test Error Error Computer Embarrassingly Parallel Requires Coordination Tricky to Scale
- 7. ABOUT KEYSTONEML • Software framework for building scalable end-to-end machine learning pipelines on Apache Spark. • Helps us understand what it means to build systems for robust, scalable, end-to-end advanced analytics workloads and the patterns that emerge. • Example pipelines that achieve state-of-the-art results on large scale datasets in computer vision, NLP, and speech - fast. • Open source software, available at: http://keystone-ml.org/
- 8. SIMPLE EXAMPLE: TEXT CLASSIFICATION 20 Newsgroups .fit( ) Trim Tokenize Bigrams Top Features Naive Bayes Max Classifier Trim Tokenize Bigrams Max Classifier Top Features Transformer Naive Bayes Model Once estimated - apply these steps to your production data in an online or batch fashion.
- 9. NOT SO SIMPLE EXAMPLE: IMAGE CLASSIFICATION Images (VOC2007) .fit( ) Resize Grayscale SIFT PCA FisherVector MaxClassifier Linear Regression Resize Grayscale SIFT MaxClassifier PCA Map Fisher Encoder Linear Model Achieves performance of Chatfield et. al., 2011 Pleasantly parallel featurization and evaluation. 7 minutes on a modest cluster. 5,000 examples, 40,000 features, 20 classes
- 10. EVEN LESS SIMPLE: IMAGENET Color Edges Resize Grayscale SIFT PCA FisherVector Top 5 Classifier LCS PCA FisherVector Block Linear Solver <100 SLOC Upgrading the solver for higher precision means changing 1 LOC. Weighted Block Linear Solver Adding 100,000 more texture features is easy. Texture Gabor Wavelets PCA FisherVector 1000 class classification. 1,200,000 examples 64,000 features. 90 minutes on 100 nodes.
- 11. OPTIMIZING KEYSTONEML PIPELINES High-level API enables rich space of optimizations Automated ML operator selection. Linear Solver L-BFGS Iterative SGD Direct Solver Training Data Grayscaler SIFT Extractor Reduce Dimensions Fisher Vector Normalize Column Sampler Linear Map Distributed PCA Column Sampler Local GMM Least Sq. L-BFGS Predictions Training Labels Auto-caching for iterative workloads.
- 12. KEYSTONEML OPTIMIZER • Sampling-based cost model projects resource usage • CPU, Memory, Network • Utilization tracked through pipeline. • Decisions made to minimize total cost of execution. • Catalyst-based optimizer does the heavy lifting. Stage n d size (GB) Input 5000 1m pixel JPEG 0.4 Resize 5000 260k pixels 3.6 Grayscale 5000 260k pixels 1.2 SIFT 5000 65000x128 309 PCA 5000 65000x80 154 FV 5000 256x64x2 1.2 Linear Regression 5000 20 0.0007 Max Classifier 5000 1 0.00009
- 13. CHOOSING A SOLVER • Datasets have a number of interesting degrees of freedom. • Problem size (n, d, k) • sparsity (nnz) • condition number • Platform has degrees of freedom: • Memory, CPU, Network, Nodes • Solvers are predictable! 13 Where: A 2 Rn⇥d X 2 Rd⇥k B 2 Rn⇥k Objective: min X |AX B| 2 2 + |X|2 2
- 14. CHOOSING A SOLVER • Three Solvers • Exact, Block, LBFGS • Two datasets • Amazon - >99% sparse, n=65m • TIMIT - dense, n=2m • Exact solve works well for small # features. • Use LBFGS for sparse problems. • Block solver scales well to big dense problems. • Hundreds of thousands of features. ● ● ● ● ● ● Amazon TIMIT 100 1000 10000 10 100 1000 1024 2048 4096 8192 16384 1024 2048 4096 8192 16384 Number of Features Time(s) Solver ● Exact Block Solver LBFGS 14
- 15. SOLVER PERFORMANCE • Compared KeystoneML with: • VowpalWabbit - specialized system for large, sparse problems. • SystemML - general purpose, optimizing ML system. • Two problems: • Amazon - Sparse text features. • BinaryTIMIT - Dense phoneme data. • High Order Bit: • KeystoneML pipelines featurization and adapts to workload changes. Amazon 0 200 400 600 800 1024 2048 4096 8192 16384 Features Time(s) System KeystoneML SystemML Binary TIMIT 0 100 200 300 400 1024 2048 4096 8192 16384 Features Time(s) System KeystoneML SystemML Amazon 0 50 100 150 1024 2048 4096 8192 16384 Features Time(s) System KeystoneML Vowpal Wabbit Binary TIMIT 0 500 1000 1500 1024 2048 4096 8192 16384 Features Time(s) System KeystoneML Vowpal Wabbit
- 16. DECIDING WHATTO SAVE • Pipelines Generate Lots of intermediate state. • E.g. SIFT features blow up a 0.42GBVOC dataset to 300GB. • Iterative algorithms —> state needed many times. • How do we determine what to save for later and what to reuse, given fixed resource budget? • Can we adapt to workload changes? 16 Resize Grayscale SIFT PCA FisherVector MaxClassifier Linear Regression
- 17. CACHING PROBLEM • Output is computed via depth- first execution of DAG. • Caching “truncates” a path after first visit. • Want to minimize execution time. • Subject to memory constraints. • Picking optimal set is hard! 17 A B C D E 60s 50g 40s 200g 20s 40g 40g 15s 5s 10g Output Cache set Time Memory ABCDE 140s 340g B 140s 200g A 180s 50g {} 240s 0g
- 18. END-TO-END PERFORMANCE Dataset Training Examples Features Raw Size (GB) Feature Size (GB) Amazon 65 million 100k (sparse) 14 89 TIMIT 2.25 million 528k 7.5 8800 ImageNet 1.28 million 262k 74 2500 VOC 5000 40k 0.43 1.5
- 19. END-TO-END PERFORMANCE Dataset KeystoneML Accuracy Reported Accuracy KeystoneML Time (m) Reported Time (m) Speedup over Reported Amazon 91.6% N/A 3.3 N/A N/A TIMIT 66.1% 66.3% 138 120 0.87x ImageNet 67.4% 66.6% 270 5760 21x VOC 57.2% 59.2% 7 87 12x
- 20. END-TO-END PERFORMANCE Amazon TIMIT ImageNet 0 5 10 15 0 20 40 60 0 100 200 300 400 500 8 16 32 64 128 8 16 32 64 128 8 16 32 64 128 Cluster Size (# of nodes) Time(minutes) Stage Loading Train Data Featurization Model Solve Loading Test Data Model Eval ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Amazon TIMIT ImageNet 1 2 4 8 16 8 16 32 64 128 8 16 32 64 128 8 16 32 64 128 Cluster Size (# of nodes) Speedupover8nodes(x)
- 21. END-TO-END PERFORMANCE • Tested three levels of optimization • None • Auto-caching only • Auto-caching and operator-selection. • 7x to 15x speedup 0 5 10 15 Amazon TIMIT VOC Workload Speedup Optimization Level None Whole−Pipeline All
- 23. BACKUP SLIDES
- 24. SOFTWARE FEATURES • Data Loaders • CSV, CIFAR, ImageNet,VOC,TIMIT, 20 Newsgroups • Transformers • NLP -Tokenization, n-grams, term frequency, NER*, parsing* • Images - Convolution, Grayscaling, LCS, SIFT*, FisherVector*, Pooling,Windowing, HOG, Daisy • Speech - MFCCs* • Stats - Random Features, Normalization, Scaling*, Signed Hellinger Mapping, FFT • Utility/misc - Caching,Top-K classifier, indicator label mapping, sparse/dense encoding transformers. • Estimators • Learning - Block linear models, Linear Discriminant Analysis, PCA, ZCA Whitening, Naive Bayes*, GMM* • Example Pipelines • NLP - Amazon Product Review Classification, 20 Newsgroups,Wikipedia Language model • Images - MNIST, CIFAR,VOC, ImageNet • Speech -TIMIT • Evaluation Metrics • Binary Classification • Multiclass Classification • Multilabel Classification * - Links to external library Just 11k Lines of Code, 5k of which areTests or JavaDoc.
- 25. KEY API CONCEPTS
- 26. TRANSFORMERS TransformerInput Output abstract classTransformer[In, Out] { def apply(in: In): Out def apply(in: RDD[In]): RDD[Out] = in.map(apply) … } TYPE SAFETY HELPS ENSURE ROBUSTNESS
- 27. ESTIMATORS EstimatorRDD[Input] abstract class Estimator[In, Out] { def fit(in: RDD[In]):Transformer[In,Out] … } Transformer .fit()
- 28. CHAINING NGrams(2)String Vectorizer VectorBigrams val featurizer:Transformer[String,Vector] = NGrams(2) thenVectorizer featurizerString Vector =
- 29. COMPLEX PIPELINES .fit(data, labels) pipelineString Prediction = val pipeline = (featurizer thenLabelEstimator LinearModel).fit(data, labels) featurizerString Vector Linear Model Prediction featurizerString Vector Linear Map Prediction