This document provides a general summary of new features and important notes about the Intel® Data Analytics Acceleration library (Intel® DAAL) software product. Please see the following links to the online resources and documents for the latest information regarding Intel DAAL:
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What's New in Intel® DAAL 2017 Update 4
- Small fixes for Python examples
- Tune Microsoft Visual Studio solution for cpp examples: disable debug for release configurations; set start point for relative paths; add possibility to run examples from IDE
- Enabled support for macOS with Xcode 8.3
- Performance tuning for few algorithms to address previous degradation
- Fixes in documentation
What's New in Intel® DAAL 2017 Update 3
- Intel® DAAL (on Linux and macOS) can now be installed directly from yum, apt, and conda repositories.
- Bug fixes and performance improvements
- Intel® DAAL (for Linux and macOS) switched to the Apache License 2.0
What's New in Intel® DAAL 2017 Update 2
- Lots of improvements for the neural networks API:
- Added the transposed convolution layer
- Added the reshape layer
- Extended interface of loss softmax cross-entropy layer to support input tensors of arbitrary dimensions
- Added sigmoid cross-entropy criterion
- Added truncated Gaussian initializer for tensors
- Extended support for distributed computing by adding the objective function with pre-computed characteristics
- Improved performance of neural network layers used in topologies such as AlexNet
- Added more samples to demonstrate the usage of this library. You can find and download the latest samples from: Intel® Data Analytics Acceleration Library Code Samples
What's New in Intel® DAAL 2017 Update 1
- Added K-Nearest Neighbors (KNN) algorithm for batch computing mode
- Added distributed processing mode for neural network training to support distributed parallel data processing
- Introduced diagonal variance-covariance matrices in EM GMM and controls to treat degenerated covariance matrices
- Introduced k-means++ and k-means|| initialization methods for K-Means clustering
- Introduced the Gaussian initializer for neural network model parameters (weights and biases) initialization
- Introduced min-max normalization algorithm
- Added multiple ground truth tensors and multiple result tensors for neural networks training and inference stage, respectively
- Added optional arguments and results in the SGD solver to enable computation resumption from a paused state
- Added support for merging of the numeric tables by rows
- Added support for symmetric and triangular packed numeric tables in Java
- Performance improvements for the following functions:
- Neural network training and inference, including support for batch mode on the inference stage
- Local response normalization layer and 2D max pooling layer
- Abs and Tanh backward layers
- Cosine distance for result in lower triangular layout, correlation distance for result in full, lower- and upper triangular layouts
- Lower order moments
- z-score normalization
- Kernel functions for CSR NumericTables
- CSV feature manager
- Bug fixes for the following components:
- Multi-class classifier
- IBFGS optimization solver
What's New in Intel® DAAL 2017
- Introducing Python programming language API
- Introducing Neural Networks functionality
- Uniform and Xavier initialization methods
- Two-dimensional convolutional
- One-, two-, and three-dimensional max pooling
- One-, two-, and three-dimensional average pooling
- Spatial pyramid pooling, stochastic pooling and locally connected layers
- Fully connected
- Hyperbolic tangent
- Rectifier Linear Unit (ReLu)
- Parametric Rectifier Linear Unit (pReLu)
- Smooth Rectifier Linear Unit (smooth ReLu)
- Softmax with cross-entropy loss
- Absolute value (abs)
- Batch normalization
- Local response normalization
- Local contrast normalization
- Optimization solvers
- Stochastic gradient descent
- Mini-batch stochastic gradient descent
- Stochastic limited memory Broyden–Fletcher–Goldfarb–Shanno (lBFGS)
- Mini-batch Adagrad optimization solver
- Objective functions
- Mean squared error (MSE)
- Tensor: Support multiple data layouts, axes control, and computation of tensor size
- Other: Support for user-defined memory allocation to store layer results in Neural Networks
- Added Ridge Linear regression algorithm in batch/online/distributed processing mode
- Added support for quality metrics for linear regression
- Added z-score normalization
- Improved performance for QR, SVD, PCA, variance-covariance, linear regression, Expectation Maximization (EM) for Gaussian Mixture Models (GMM), K-means, and the Naïve Bayes algorithms on the 2nd generation of Intel® Xeon Phi™ processors (codenamed Knights Landing), as well as on the Intel® Xeon® E5-xxxx v3 (codenamed Haswell) and the Intel® Xeon® E5-xxxx v4 (codenamed Broadwell) processors.
- Bug fixes and other improvements in the library and its documentation
- Intel DAAL User's Guide and the API documentation are available for online browsing, and are removed from the installer packages
- Intel DAAL samples are now available as online download and removed from the installer packages
Support removed for installation on IA-32 architecture hosts. The 32-bit library continues to exist and can be used on Intel® 64 architecture hosts.
Intel DAAL can be installed as a part of the following suite:
- Intel® Parallel Studio XE 2017 Composer, Professional, or Cluster Edition
Download from https://software.intel.com/en-us/intel-parallel-studio-xe.
Intel DAAL consists of one package for both IA-32 and Intel® 64 architectures.
- Intel DAAL Python API (a.k.a. pyDAAL) is provided as source. When build it on Windows, users may see warning messages. These warning messages do not indicate critical issues and do not affect the library's functionality.
- Intel DAAL Python API (a.k.a. pyDAAL) built from the source does not work on OS X* El Capitan (version 10.11). Workaround: Users can get the Intel Distribution of Python as an Anaconda package (http://anaconda.org/intel/), which contains a pre-built pyDAAL that works on OS X* El Capitan.
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