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- 1. A Terascale Learning AlgorithmAlekh Agarwal, Olivier Chapelle, Miroslav Dudik, and John Langford ... And the Vowpal Wabbit project.
- 2. Applying for a fellowship in 1997
- 3. Applying for a fellowship in 1997 Interviewer: So, what do you want to do?
- 4. Applying for a fellowship in 1997 Interviewer: So, what do you want to do? John: I’d like to solve AI.
- 5. Applying for a fellowship in 1997 Interviewer: So, what do you want to do? John: I’d like to solve AI. I: How?
- 6. Applying for a fellowship in 1997 Interviewer: So, what do you want to do? John: I’d like to solve AI. I: How? J: I want to use parallel learning algorithms to create fantastic learning machines!
- 7. Applying for a fellowship in 1997 Interviewer: So, what do you want to do? John: I’d like to solve AI. I: How? J: I want to use parallel learning algorithms to create fantastic learning machines! I: You fool! The only thing parallel machines are good for is computational windtunnels!
- 8. Applying for a fellowship in 1997 Interviewer: So, what do you want to do? John: I’d like to solve AI. I: How? J: I want to use parallel learning algorithms to create fantastic learning machines! I: You fool! The only thing parallel machines are good for is computational windtunnels! The worst part: he had a point. At that time, smarter learning algorithms always won. To win, we must master the best single-machine learning algorithms, then clearly beat them with a parallel approach.
- 9. Demonstration
- 10. Terascale Linear Learning ACDL11 Given 2.1 Terafeatures of data, how can you learn a good linear predictor fw (x) = i wi xi ?
- 11. Terascale Linear Learning ACDL11 Given 2.1 Terafeatures of data, how can you learn a good linear predictor fw (x) = i wi xi ? 2.1T sparse features 17B Examples 16M parameters 1K nodes
- 12. Terascale Linear Learning ACDL11 Given 2.1 Terafeatures of data, how can you learn a good linear predictor fw (x) = i wi xi ? 2.1T sparse features 17B Examples 16M parameters 1K nodes 70 minutes = 500M features/second: faster than the IO bandwidth of a single machine⇒ we beat all possible single machine linear learning algorithms.
- 13. Terascale Linear Learning ACDL11 Given 2.1 Terafeatures of data, how can you learn a good linear predictor fw (x) = i wi xi ? 2.1T sparse features 17B Examples 16M parameters 1K nodes 70 minutes = 500M features/second: faster than the IO bandwidth of a single machine⇒ we beat all possible single machine linear learning algorithms. (Actually, we can do even better now.)
- 14. book Features/s 100 1000 10000 100000 1e+06 1e+07 1e+08 1e+09 RBF-SVM MPI?-500 RCV1 Ensemble Tree MPI-128 Synthetic single parallel RBF-SVM TCP-48 MNIST 220K Decision Tree MapRed-200 Ad-Bounce # Boosted DT MPI-32 Speed per method Ranking # Linear Threads-2 RCV1 LinearHadoop+TCP-1000 Ads *& Compare: Other Supervised Algorithms in Parallel Learning
- 15. The tricks we use First Vowpal Wabbit Newer Algorithmics Parallel Stuﬀ Feature Caching Adaptive Learning Parameter Averaging Feature Hashing Importance Updates Smart Averaging Online Learning Dimensional Correction Gradient Summing L-BFGS Hadoop AllReduce Hybrid Learning We’ll discuss Hashing, AllReduce, then how to learn.
- 16. String −> Index dictionary RAM Weights Weights Conventional VWMost algorithms use a hashmap to change a word into an index fora weight.VW uses a hash function which takes almost no RAM, is x10faster, and is easily parallelized.Empirically, radical state compression tricks possible with this.
- 17. MPI-style AllReduce Allreduce initial state 7 5 6 1 2 3 4 AllReduce = Reduce+Broadcast
- 18. MPI-style AllReduce Reducing, step 1 7 8 13 1 2 3 4 AllReduce = Reduce+Broadcast
- 19. MPI-style AllReduce Reducing, step 2 28 8 13 1 2 3 4 AllReduce = Reduce+Broadcast
- 20. MPI-style AllReduce Broadcast, step 1 28 28 28 1 2 3 4 AllReduce = Reduce+Broadcast
- 21. MPI-style AllReduce Allreduce final state 28 28 28 28 28 28 28 AllReduce = Reduce+Broadcast
- 22. MPI-style AllReduce Allreduce final state 28 28 28 28 28 28 28 AllReduce = Reduce+Broadcast Properties: 1 Easily pipelined so no latency concerns. 2 Bandwidth ≤ 6n. 3 No need to rewrite code!
- 23. An Example Algorithm: Weight averaging n = AllReduce(1) While (pass number < max) 1 While (examples left) 1 Do online update. 2 AllReduce(weights) 3 For each weight w ← w /n
- 24. An Example Algorithm: Weight averaging n = AllReduce(1) While (pass number < max) 1 While (examples left) 1 Do online update. 2 AllReduce(weights) 3 For each weight w ← w /n Other algorithms implemented: 1 Nonuniform averaging for online learning 2 Conjugate Gradient 3 LBFGS
- 25. What is Hadoop-Compatible AllReduce? Program Data 1 “Map” job moves program to data.
- 26. What is Hadoop-Compatible AllReduce? Program Data 1 “Map” job moves program to data. 2 Delayed initialization: Most failures are disk failures. First read (and cache) all data, before initializing allreduce. Failures autorestart on diﬀerent node with identical data.
- 27. What is Hadoop-Compatible AllReduce? Program Data 1 “Map” job moves program to data. 2 Delayed initialization: Most failures are disk failures. First read (and cache) all data, before initializing allreduce. Failures autorestart on diﬀerent node with identical data. 3 Speculative execution: In a busy cluster, one node is often slow. Hadoop can speculatively start additional mappers. We use the ﬁrst to ﬁnish reading all data once.
- 28. What is Hadoop-Compatible AllReduce? Program Data 1 “Map” job moves program to data. 2 Delayed initialization: Most failures are disk failures. First read (and cache) all data, before initializing allreduce. Failures autorestart on diﬀerent node with identical data. 3 Speculative execution: In a busy cluster, one node is often slow. Hadoop can speculatively start additional mappers. We use the ﬁrst to ﬁnish reading all data once. The net eﬀect: Reliable execution out to perhaps 10K node-hours.
- 29. Algorithms: Preliminaries Optimize so few data passes required ⇒ Smart algorithms. Basic problem with gradient descent = confused units. fw (x) = i wi xi (x)−y 2 ⇒ ∂(fw ∂wi ) = 2(fw (x) − y )xi which has units of i. But wi naturally has units of 1/i since doubling xi implies halving wi to get the same prediction. Crude ﬁxes: ∂ 2 −1 1 Newton: Multiply inverse Hessian: ∂wi ∂wj by gradient to get update direction. 2 Normalize update so total step size is controlled.
- 30. Algorithms: Preliminaries Optimize so few data passes required ⇒ Smart algorithms. Basic problem with gradient descent = confused units. fw (x) = i wi xi (x)−y 2 ⇒ ∂(fw ∂wi ) = 2(fw (x) − y )xi which has units of i. But wi naturally has units of 1/i since doubling xi implies halving wi to get the same prediction. Crude ﬁxes: 2 −1 1 Newton: Multiply inverse Hessian: ∂w∂∂wj i by gradient to get update direction...but computational complexity kills you. 2 Normalize update so total step size is controlled...but this just works globally rather than per dimension.
- 31. Approach Used 1 Optimize hard so few data passes required. 1 L-BFGS = batch algorithm that builds up approximate inverse ∆ ∆T hessian according to: ∆w ∆w where ∆w is a change in weights T w g w and ∆g is a change in the loss gradient g .
- 32. Approach Used 1 Optimize hard so few data passes required. 1 L-BFGS = batch algorithm that builds up approximate inverse ∆ ∆T hessian according to: ∆w ∆w where ∆w is a change in weights T w g w and ∆g is a change in the loss gradient g . 2 Dimensionally correct, adaptive, online, gradient descent for small-multiple passes. 1 Online = update weights after seeing each example. 1 2 Adaptive = learning rate of feature i according to √P gi2 where gi = previous gradients. 3 Dimensionally correct = still works if you double all feature values. 3 Use (2) to warmstart (1).
- 33. Approach Used 1 Optimize hard so few data passes required. 1 L-BFGS = batch algorithm that builds up approximate inverse ∆ ∆T hessian according to: ∆w ∆w where ∆w is a change in weights T w g w and ∆g is a change in the loss gradient g . 2 Dimensionally correct, adaptive, online, gradient descent for small-multiple passes. 1 Online = update weights after seeing each example. 1 2 Adaptive = learning rate of feature i according to √P gi2 where gi = previous gradients. 3 Dimensionally correct = still works if you double all feature values. 3 Use (2) to warmstart (1). 2 Use map-only Hadoop for process control and error recovery.
- 34. Approach Used 1 Optimize hard so few data passes required. 1 L-BFGS = batch algorithm that builds up approximate inverse ∆ ∆T hessian according to: ∆w ∆w where ∆w is a change in weights T w g w and ∆g is a change in the loss gradient g . 2 Dimensionally correct, adaptive, online, gradient descent for small-multiple passes. 1 Online = update weights after seeing each example. 1 2 Adaptive = learning rate of feature i according to √P gi2 where gi = previous gradients. 3 Dimensionally correct = still works if you double all feature values. 3 Use (2) to warmstart (1). 2 Use map-only Hadoop for process control and error recovery. 3 Use custom AllReduce code to sync state.
- 35. Approach Used 1 Optimize hard so few data passes required. 1 L-BFGS = batch algorithm that builds up approximate inverse ∆ ∆T hessian according to: ∆w ∆w where ∆w is a change in weights T w g w and ∆g is a change in the loss gradient g . 2 Dimensionally correct, adaptive, online, gradient descent for small-multiple passes. 1 Online = update weights after seeing each example. 1 2 Adaptive = learning rate of feature i according to √P gi2 where gi = previous gradients. 3 Dimensionally correct = still works if you double all feature values. 3 Use (2) to warmstart (1). 2 Use map-only Hadoop for process control and error recovery. 3 Use custom AllReduce code to sync state. 4 Always save input examples in a cacheﬁle to speed later passes.
- 36. Approach Used 1 Optimize hard so few data passes required. 1 L-BFGS = batch algorithm that builds up approximate inverse ∆ ∆T hessian according to: ∆w ∆w where ∆w is a change in weights T w g w and ∆g is a change in the loss gradient g . 2 Dimensionally correct, adaptive, online, gradient descent for small-multiple passes. 1 Online = update weights after seeing each example. 1 2 Adaptive = learning rate of feature i according to √P gi2 where gi = previous gradients. 3 Dimensionally correct = still works if you double all feature values. 3 Use (2) to warmstart (1). 2 Use map-only Hadoop for process control and error recovery. 3 Use custom AllReduce code to sync state. 4 Always save input examples in a cacheﬁle to speed later passes. 5 Use hashing trick to reduce input complexity.
- 37. Approach Used 1 Optimize hard so few data passes required. 1 L-BFGS = batch algorithm that builds up approximate inverse ∆ ∆T hessian according to: ∆w ∆w where ∆w is a change in weights T w g w and ∆g is a change in the loss gradient g . 2 Dimensionally correct, adaptive, online, gradient descent for small-multiple passes. 1 Online = update weights after seeing each example. 1 2 Adaptive = learning rate of feature i according to √P gi2 where gi = previous gradients. 3 Dimensionally correct = still works if you double all feature values. 3 Use (2) to warmstart (1). 2 Use map-only Hadoop for process control and error recovery. 3 Use custom AllReduce code to sync state. 4 Always save input examples in a cacheﬁle to speed later passes. 5 Use hashing trick to reduce input complexity. Open source in Vowpal Wabbit 6.0. Search for it.
- 38. Robustness & Speedup Speed per method 10 Average_10 9 Min_10 8 Max_10 linear 7 Speedup 6 5 4 3 2 1 0 10 20 30 40 50 60 70 80 90 100 Nodes
- 39. Webspam optimization Webspam test results 0.5 Online 0.45 Warmstart L-BFGS 1 0.4 L-BFGS 1 Single machine online 0.35 test log loss 0.3 0.25 0.2 0.15 0.1 0.05 0 0 5 10 15 20 25 30 35 40 45 50 pass
- 40. How does it work? The launch sequence (on Hadoop): 1 mapscript.sh hdfs output hdfs input maps asked 2 mapscript.sh starts spanning tree on the gateway. Each vw connects to spanning tree to learn which other vw is adjacent in the binary tree. 3 mapscript.sh starts hadoop streaming map-only job: runvw.sh 4 runvw.sh calls vw twice. 5 First vw call does online learning while saving examples in a cacheﬁle. Weights are averaged before saving. 6 Second vw uses online solution to warmstart L-BFGS. Gradients are shared via allreduce. Example: mapscript.sh outdir indir 100 Everything also works in a nonHadoop cluster: you just need to script more to do some of the things that Hadoop does for you.
- 41. How does this compare to other cluster learning eﬀorts? Mahout was founded as the MapReduce ML project, but has grown to include VW-style online linear learning. For linear learning, VW is superior: 1 Vastly faster. 2 Smarter Algorithms. Other algorithms exist in Mahout, but they are apparently buggy. AllReduce seems a superior paradigm in the 10K node-hours regime for Machine Learning. Graphlab(@CMU) doesn’t overlap much. This is mostly about graphical model evaluation and learning. Ultra LDA(@Y!), overlaps partially. VW has noncluster-parallel LDA which is perhaps x3 more eﬀecient.
- 42. Can allreduce be used by others? It might be incorporated directly into next generation Hadoop. But for now, it’s quite easy to use the existing code. void all reduce(char* buﬀer, int n, std::string master location, size t unique id, size t total, size t node); buﬀer = pointer to some ﬂoats n = number of bytes (4*ﬂoats) master location = IP address of gateway unique id = nonce (unique for diﬀerent jobs) total = total number of nodes node = node id number The call is stateful: it initializes the topology if necessary.
- 43. Further Pointers search://Vowpal Wabbit mailing list: vowpal wabbit@yahoogroups.com VW tutorial (Preparallel): http://videolectures.net Machine Learning (Theory) blog: http://hunch.net
- 44. Bibliography: Original VWCaching L. Bottou. Stochastic Gradient Descent Examples on Toy Problems, http://leon.bottou.org/projects/sgd, 2007.Release Vowpal Wabbit open source project, http://github.com/JohnLangford/vowpal_wabbit/wiki, 2007.Hashing Q. Shi, J. Petterson, G. Dror, J. Langford, A. Smola, and SVN Vishwanathan, Hash Kernels for Structured Data, AISTAT 2009.Hashing K. Weinberger, A. Dasgupta, J. Langford, A. Smola, and J. Attenberg, Feature Hashing for Large Scale Multitask Learning, ICML 2009.
- 45. Bibliography: AlgorithmicsL-BFGS J. Nocedal, Updating Quasi-Newton Matrices with Limited Storage, Mathematics of Computation 35:773–782, 1980.Adaptive H. B. McMahan and M. Streeter, Adaptive Bound Optimization for Online Convex Optimization, COLT 2010.Adaptive J. Duchi, E. Hazan, and Y. Singer, Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, COLT 2010. Import. N. Karampatziakis, and J. Langford, Online Importance Weight Aware Updates, UAI 2011.
- 46. Bibliography: Parallelgrad sum C. Teo, Q. Le, A. Smola, V. Vishwanathan, A Scalable Modular Convex Solver for Regularized Risk Minimization, KDD 2007.averaging G. Mann, R. Mcdonald, M. Mohri, N. Silberman, and D. Walker. Eﬃcient large-scale distributed training of conditional maximum entropy models, NIPS 2009.averaging K. Hall, S. Gilpin, and G. Mann, MapReduce/Bigtable for Distributed Optimization, LCCC 2010. (More forthcoming, of course)

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