BKK16-404B Data Analytics and Machine Learning- from Node to Cluster
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The document discusses the integration of data analytics and machine learning within the ARM ecosystem, emphasizing the need for optimized tools like Python libraries (e.g., pandas, scikit-learn) and tools for handling big data solutions. It outlines the operational capabilities of Apache Spark, comparing it with Hadoop, while presenting the necessity for a unified platform that supports various machine learning algorithms and efficient data pipelines. The document further explores strategies for scaling data science applications across clusters and highlights significant advancements in Spark's architecture and capabilities, particularly focusing on in-memory processing and resource optimization.
BKK16-404B Data Analytics and Machine Learning- from Node to Cluster
- 1. Presented by Date Event Data Analytics and Machine Learning: From Node to Cluster Understanding use cases to optimize on ARM Ecosystem Viswanath Puttagunta Ganesh Raju BKK16-404B March 10th, 2016 Linaro Connect BKK16 Version 0.1
- 2. Vish Puttagunta ● Technical Program Manager, Linaro ● DSP (TI C64x) optimizations (Image / Signal Processing) ● ARM® NEON™ optimizations upstreamed to opus audio codec ● Data Analysis and Machine Learning: Why ARM Ganesh Raju ● Tech Lead, Big Data, Linaro ● Brings in Enterprise experience in implementing Big Data solutions
- 3. Data Science: Big Picture Data Analytics Machine Learning Deep Learning High Performance Computing (HPC) Big Data Statistics Lin/Log Reg KNN, Decision Trees,PCA... Software Tools Python,R.. Pandas, scikit-learn, nltk.. Caffe, TensorFlow.. Spark, MLlib, Hadoop.. Deep Learning/NN CNN RNN.. Data Science ● Value Prediction ● Classification ● Transformation ● Correlations ● Causalities ● Wrangling...
- 4. Overview ● Basic Data Science use cases ● Pandas Library(Python) ○ Time Series, Correlations, Risk Analysis ● Supervised Learning ○ scikit-learn Library (Python) ● Understand operations done repeatedly ● Open discussion for next steps: ○ Profile, Optimize (ARM Neon, OpenCL, OpenMP..) on a single machine. ○ Scale beyond a single machine ● Ref: https://github.com/viswanath-puttagunta/bkk16_MLPrimer
- 5. Goal ● Make the tools and operations work out of the box on ARM ○ ARM Neon, OpenCL, OpenMP. ○ ‘pip install’ should just work :) ● Why Now? (Hint: Spark)
- 6. Pandas Data Analysis (Pandas) ● Origins in Finance for Data Manipulation and Analysis (Python Library) ● DataFrame object ● Repeat Computations: ○ Means, Min, Max, Percentage Changes ○ Standard Deviation ○ Differentiation (Shift and subtract) ○ ...
- 7. Op: Rolling Mean (Pandas)
- 8. Op: Percentage Change (Pandas) Operations: Shift, Subtract, Divide
- 9. Op: Percentage Change Vs Risk (Variance)
- 10. Op: Correlation (Pandas) Operations: Mean, Variance, Square Root Pearson Correlation Coef Source: https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
- 12. Linear Regression (Predictor) Objective: Fit line/curve to minimize a cost function
- 13. Linear Regression (Predictor)... Operations: Gradient Descent: Matrix Transforms/Inverse/Multiplications. For Lnr Reg, directly reduces to: Θ = (XT X)-1 XT Y Source: https://www.youtube.com/watch?v=SqA6TujbmWw&list=PLE6Wd9FR--Ecf_5nCbnSQMHqORpiChfJf&index=16 https://youtu.be/WnqQrPNYz5Q?list=PLaXDtXvwY-oDvedS3f4HW0b4KxqpJ_imw&t=284
- 14. Logistic Regression (Classification) Operations: Matrix Transforms/Inverse/Multiplications. Source: https://www.youtube.com/watch?v=Zc7ouSD0DEQ&index=27&list=PLE6Wd9FR--Ecf_5nCbnSQMHqORpiChfJf
- 15. Artificial Neural Networks (Eg: Predictor) Objective: Compute parameters to minimize a cost function Operations: Matrix Transforms/Inverse/Multiplications, Dot Products... Source: https://www.yout.ube.com/watch?v=bxe2T-V8XRs
- 16. Bayesian Classifiers Operations (Training): Mean, Standard Deviations, Logs, binning/compare(/histograms) Note: Some variations based on assumptions on variance (LDA, QDA) Source: An Introduction to Statistical Learning with Applications in R (Springer Texts)
- 17. K-Nearest Neighbors (Classifier) Operations: Euclidean Distance Computations Note: K is tunable. Typical to repeat computations for lot of k values
- 18. Decision Tree (Predictors & Classifiers) Operations: Each split done so that Predictor: Minimize Root Mean Square Error Classifier: Minimize Gini Index (Note: depth tunable)
- 19. Bagging / Random Forests Operations: Each tree similar to Decision Tree Note: Typical to see about 200 to 300 trees to stabilize. Source: An Introduction to Statistical Learning with Applications in R (Springer Texts)
- 20. Segway to Spark ● So far, on a single machine with lots of RAM and compute. ○ How to scale to a cluster ● So far, data from simple csv files ○ How to acquire/clean data on large scale
- 21. Discussion ● OpenCL (Deep Learning) ○ Acceleration using GPU, DSPs ○ Outside of Deep Learning: GPU? (scikit-learn faq) ○ But what about Shamrock? OpenCL backend on CPU. ○ Port CUDA kernels into OpenCL kernels in various projects? ● ARM NEON ○ Highly scalable ● OpenMP? ● Tools to profile end-to-end use cases? ○ Perf..
- 22. Backup Slides ● Text Analytics ○ Sparse Matrices and operations ○ Word counts (Naive Bayes) ● Libraries (Python) ○ sklearn (No GPU Support!! FAQ) ○ gensim (Text Analytics) ○ TensorFlowTM (GPU backend available) ○ Caffe (GPU backend available) ○ Theano (GPU backend available)
- 23. Presented by Date Event Data science in Distributed Environment Ganesh Raju Tech Lead, Big Data Linaro Thursday 10 March 2016 BKK16
- 24. Overview 1. Review of Data Science in Single Node 2. Data Science in Distributed Environment a. Hadoop and its limitations 3. Data Science using Apache Spark a. Spark Ecosystem b. Spark ML c. Spark Streaming d. Spark 2.0 and Roadmap 4. Q & A
- 25. Hadoop Vs Spark 80+ operators compared to 2 operators in Hadoop
- 26. Hadoop and its limitations
- 27. Spark - Unified Platform source: www.databricks.com
- 28. Machine Learning Data Pipeline Why in Spark: ● Machine learning algorithms are ○ Complex, multi-stage ○ Iterative ● MapReduce/Hadoop unsuitable
- 29. source: http://www.slideshare.net/databricks/2015-0317-scala-days Spark is In-Memory and Fast
- 30. Apache Spark Stack Unified Engine across diverse workloads and Environments R Python Scala Java Spark Core API Spark SQL + DataFrames Streaming MLib / ML Machine Learning GraphX Graph Computation
- 31. Spark Core Internals SparkContext connects to a cluster manager Obtains executors on cluster nodes Sends app code to them Sends task to the executors Driver Program Cluster Manager Worker Node Executor Worker Node Cluster Spark Context Scheduler Scheduler Task Cache Task Executor Task Cache Task
- 32. Application Code Distribution ● PySpark enables developers to write driver programs in Python ● Application code is serialized and sent to the worker nodes ● Execution happens in native language (Python/R) SparkContext Driver Py4J Java Spark Context File system Worker Node Worker Node Cluster Python Python Python Python Python Python Pipe Socket
- 33. Apache Spark MLlib / ML Algorithms Supported: ● Classification: Logistic Regression, Linear SVM, Naive Bayes, classification tree ● Regression: Generalized Linear Models (GLMs), Regression Tree ● Collaborative Filtering: Alternating Least Squares (ALS), Non-Negative Matrix Factorization (NMF) ● Clustering: K-Means ● Decomposition: SVD, PCA ● Optimization: Stochastic Gradient Descent (SGD), L-BFGS
- 34. Spark Streaming ● Run a streaming computation as a series of very small, deterministic batch jobs ○ Live data streaming is converted into micro batches of input. Batch can be of ½ sec latency. ○ Each batch is processed in Spark ○ Output is returned as micro batches ○ Potential for combining batch and streaming processing. ● Linear models can be trained in streaming fashion ● Model weights can be updated via SGD, thus amenable to streaming ● Consistent API ● Scalable
- 35. Apache Spark General-purpose cluster computing system ● Unified End-to-End data pipeline platform with support for Machine Learning, Streaming, SQL and Graph Pipelines ● Fast and Expressive Cluster Computing Engine. No Intermediate storage. In-memory Processing. Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. ● Rich higher level APIs in Scala, Python, R, Java. Typically less code (2-5x) ● REPL ● Interoperability with other ecosystem components ○ Mesos, YARN ○ EC2, EMR ○ HDFS, S3, ○ HBase, Cassandra, Parquet, Hive, JSON, ElasticSearch
- 36. Source: https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html Tungsten Phase 2 speedups of 5-10x Structured Streaming real-time engine on SQL / DataFrames Unifying Datasets and DataFrames Major Features in 2.0 - Project Tungsten
- 37. Focus on CPU and Memory Optimization Code Generation ● Runtime Code Generation by dynamically generating bytecode during evaluation and eliminating Java boxing of primitive data types ● Faster serializer and compression on dataformat like Parquet. Manual Memory Management and Binary Processing ● Off heap memory management. Avoiding non-transient Java objects (store them in binary format), which reduces GC overhead. ● Minimizing memory usage through denser in-memory data format, with less spill (I/O) ● Better memory accounting (size of bytes) rather than relying on heuristics ● For operators that understand data types (in the case of DataFrames and SQL), work directly against binary format in memory, i.e. have no serialization/deserialization Cache-aware Computation ● Exploiting cache locality. Faster sorting and hashing for aggregations, joins, and shuffle Spark 2.0 - Project Tungsten features
- 38. Unified API, One Engine, Automatically Optimized PythonSQL Java/Scala R ... DataFrame Logical Plan Language Frontend LLVMJVM GPU NVRAM SSE/SIMD Tungsten Backend ...OpenCL
- 39. Apache Spark Roadmap . ● Use LLVM for compilation ● Re-implement parallelizable code using OpenCL/SSE/SIMD to utilize underlying CPU / GPU advancements towards machine learning. Spark slave nodes can achieve better performance and energy efficiency if with GPU acceleration by doing further data parallelization and algorithm acceleration.
- 40. De facto Analytics Platform source: www.databricks.com
- 41. Q & A