Get Your Hands Dirty with Intel® Distribution for Python*
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The document introduces the Intel® Data Analytics Acceleration Library and the Intel® Distribution for Python, highlighting their capabilities in data analytics and machine learning. It outlines various lab activities including linear regression, SVM classification, and k-means clustering, aimed at familiarizing users with the PyDAAL API. Additionally, it discusses performance optimization features for Python and provides setup instructions and learning objectives for practical exercises.
Get Your Hands Dirty with Intel® Distribution for Python*
- 1. Zhang Zhang, Victoriya Fedotova Intel Corporation November 2016
- 2. 2 Agenda Introduction – A quick intro to Intel® Data Analytics Acceleration Library and Intel® Distribution for Python – A brief overview of basic machine learning concepts Lab activities – Warm-up exercises: Learn the gist of PyDAAL API – Linear regression – Classification with SVM – K-Means clustering – PCA Conclusions
- 4. Modelling Data Analytics Flow Example Spam Filter not spam not spam spam Pre- process Collect Store Load Train & Validate Deploy Make Decision
- 5. Computational Aspects of Big Data • Distributed across different nodes/devices • Huge data size not fitting into node/device memory Volume • Non-homogeneous data • Sparse/Missing/Noisy data Variety • Data coming in timeVelocity Converts, Indexing, Repacking Data Recovery Distributed Computing Online Computing D1 DK P1 RK R ... Di Pi+1 Pi Time Memory capacity Attributes OutlierNumeric Categorical Missing Recover Dense Algorithm Sparse Algorithm Counter
- 6. Intel® Data Analytics Acceleration Library (Intel® DAAL) • Targets both data centers (Intel® Xeon® and Intel® Xeon Phi™) and edge-devices (Intel® Atom) • Perform analysis close to data source (sensor/client/server) to optimize response latency, decrease network bandwidth utilization, and maximize security • Offload data to server/cluster for complex and large-scale analytics (De-)Compression (De-)Serialization PCA Statistical moments Quantiles Variance matrix QR, SVD, Cholesky Apriori Outlier detection Regression • Linear • Ridge Classification • Naïve Bayes • SVM • Classifier boosting • kNN Clustering • Kmeans • EM GMM Collaborative filtering • ALS Neural Networks Pre-processing Transformation Analysis Modeling Decision Making Scientific/Engineering Web/Social Business Validation
- 7. Intel® DAAL Main Features Building end-to-end data applications Optimized for Intel architectures, from Intel® Atom™, Intel® Core™, Intel® Xeon®, to Intel® Xeon Phi™ A rich set of widely applicable algorithms for data mining and machine learning Batch, online, and distributed processing Data connectors to a variety of data sources and formats: KDB*, MySQL*, HDFS, CSV, and user-defined sources/formats C++, Java, and Python APIs *Other names and brands may be claimed as the property of others
- 9. Python Landscape Challenge#1: Domain specialists are not professional software programmers. Adoption of Python continues to grow among domain specialists and developers for its productivity benefits Challenge#2: Python performance limits migration to production systems Intel’s solution is to… Accelerate Python performance Enable easy access Empower the community
- 10. 1 Highlights: Intel® Distribution for Python* 2017 Focus on advancing Python performance closer to native speeds • Prebuilt, accelerated Distribution for numerical & scientific computing, data analytics, HPC. Optimized for IA • Drop in replacement for your existing Python. No code changes required Easy, out-of-the-box access to high performance Python • Accelerated NumPy/SciPy/scikit-learn with Intel® Math Kernel Library • Data analytics with pyDAAL, Enhanced thread scheduling with TBB, Jupyter* notebook interface, Numba, Cython • Scale easily with optimized mpi4py and Jupyter notebooks Drive performance with multiple optimization techniques • Distribution and individual optimized packages available through conda and Anaconda Cloud • Optimizations upstreamed back to main Python trunk Faster access to latest optimizations for Intel architecture
- 11. Performance Gain from MKL (Compare to “vanilla” SciPy) Configuration info: - Versions: Intel® Distribution for Python 2017 Beta, icc 15.0; Hardware: Intel® Xeon® CPU E5-2698 v3 @ 2.30GHz (2 sockets, 16 cores each, HT=OFF), 64 GB of RAM, 8 DIMMS of 8GB@2133MHz; Operating System: Ubuntu 14.04 LTS. Linear Algebra • BLAS • LAPACK • ScaLAPACK • Sparse BLAS • Sparse Solvers Fast Fourier Transforms • Multidimensional • FFTW interfaces • Cluster FFT Vector Math • Trigonometric • Hyperbolic • Exponential • Log • Power, Root Vector RNGs • Multiple BRNG • Support methods for independent streams creation • Support all key probability distributions Summary Statistics • Kurtosis • Variation coefficient • Order statistics • Min/max • Variance-covariance And More • Splines • Interpolation • Trust Region • Fast Poisson Solver Up to 100x faster Up to 10x faster! Up to 10x faster! Up to 60x faster!
- 12. PyDAAL (Python API for Intel® DAAL) Turbocharged machine learning tool for Python developers Interoperability and composability with the SciPy ecosystem: – Work directly with NumPy ndarrays – Faster than scikit-learn We’ll see how to use it in this lab
- 14. Problems – A company wants to define the impact of the pricing changes on the number of product sales – A biologist wants to define the relationships between body size, shape, anatomy and behavior of the organism Solution: Linear Regression – A linear model for relationship between features and the response Regression 14 Source: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. (2014). An Introduction to Statistical Learning. Springer
- 15. Problems – An emailing service provider wants to build a spam filter for the customers – A postal service wants to implement handwritten address interpretation Solution: Support Vector Machine (SVM) – Works well for non-linear decision boundary – Two kernel functions are provided: – Linear kernel – Gaussian kernel (RBF) – Multi-class classifier – One-vs-One Classification Source: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. (2014). An Introduction to Statistical Learning. Springer
- 16. Problems – A news provider wants to group the news with similar headlines in the same section – Humans with similar genetic pattern are grouped together to identify correlation with a specific disease Solution: K-Means – Pick k centroids – Repeat until converge: – Assign data points to the closest centroid – Re-calculate centroids as the mean of all points in the current cluster – Re-assign data points to the closest centroid Cluster Analysis
- 17. Problems – Data scientist wants to visualize a multi- dimensional data set – A classifier built on the whole data set tends to overfit Solution: Principal Component Analysis – Compute eigen decomposition on the correlation matrix – Apply the largest eigenvectors to compute the largest principal components that can explain most of variance in original data Dimensionality Reduction
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- 19. Setup Unpack the archive to the local disk Run setup script: – Linux, OS X: ./setup.sh – Windows: setup.bat Set path to conda: – Linux, OS X: export PATH=<path_to_idp>/bin:$PATH – Windows: set PATH=<path_to_idp>Scripts;%PATH%
- 20. Lab 1: Warm-up Exercise Learning objectives: Understand NumericTable - The main data structure of DAAL – Create NumericTable from data sources – Interoperability with NumPy, Pandas, scikit-learn – Get NumPy ndarray from NumericTable Understand code sequence of using DAAL API – Create an algorithm object – Pass in input data – Set algorithm specific parameters – Compute – Get results
- 21. Lab 2: Linear Regression Learning objectives: Understand the 2 regression algorithms currently available in DAAL – Linear regression without regularization – Ridge regression Learn supervised learning workflow – Train a model using known data – Test the model by making predictions on new data Visualize prediction results
- 22. Lab 3: Classification with SVM Learning objectives: Understand SVM algorithm usage model – Multi-class classification with SVM – Two-class classification with SVM Understand quality metrics in classification – Confusion matrix – Metrics computed using the confusion matrix (accuracy, etc.)
- 23. Lab 4: Clustering with K-Means Learning objectives: Understand the K-Means algorithm supported in DAAL Learn basic clustering workflow – Initialize cluster centroids – Minimize the goal function Visualize clusters
- 24. Lab 5: Principal Component Analysis Learning objectives: Understand PCA algorithms support in DAAL: – Correlation matrix method – SVD method Evaluate and visualize principal components
- 25. References Intel DAAL User’s Guide and Reference Manual – https://software.intel.com/sites/products/documentation/doclib/daal/daa l-user-and-reference-guides/index.htm Intel Distribution for Python Documentation – https://software.intel.com/en-us/intel-distribution-for-python- support/documentation
- 26. What’s Next - Takeaways Learn more about Intel® DAAL – It supports C++ and Java, too! – We want you to use DAAL in your data projects Learn more about Intel® Distribution for Python – Beyond machine learning, many more benefits Keep an eye on the tutorial repository – https://github.com/daaltces/pydaal-tutorials – I’m adding more labs, samples, etc.
- 27. Zhang Zhang (zhang.zhang@intel.com) Victoriya Fedotova (victoriya.s.fedotova@intel.com) www.intel.com/hpcdevcon