JarCastingIntel® oneAPI Data Analytics Library
Installation | Documentation | Examples | Samples | Get Help | How to Contribute
Intel® oneAPI Data Analytics Library (oneDAL) is a powerful machine learning library that helps speed up big data analysis. oneDAL solvers are also used in Intel Distribution for Python in Scikit-learn optimization.
Intel® oneAPI Data Analytics Library is an extension of Intel® Data Analytics Acceleration Library (Intel® DAAL).
Table of Contents
- Python API
- oneDAL Apache Spark MLlib samples
- Installation
- Documentation
- Technical Preview Features
- oneDAL and Intel® DAAL
Build yours high-performance data science application with oneDAL
oneDAL uses all capabilities of Intel® hardware, which allows you to get an significant performance boost on the classic machine learning algorithms.
We provide highly optimized algorithmic building blocks for all stages of data analytics: preprocessing, transformation, analysis, modeling, validation, and decision making.
The current version of oneDAL provides Data Parallel C++ (DPC++) API extensions to the traditional C++ interface.
The size of the data is growing exponentially, as is the need for high-performance and scalable frameworks to analyze all this data and extract some benefits from it. Besides superior performance on a single node, the oneDAL distributed computation mode also provides excellent strong and weak scaling (check charts below).
| oneDAL K-means fit, strong scaling result | oneDAL K-means fit, weak scaling results |
|---|---|
 |
 |
technical details: FPType: float32; HW: Intel Xeon Processor E5-2698 v3 @2.3GHz, 2 sockets, 16 cores per socket; SW: Intel® DAAL (2019.3), MPI4Py (3.0.0), Intel® Distribution Of Python (IDP) 3.6.8; Details available in the article https://arxiv.org/abs/1909.11822
Refer to our examples and documentation for more information about our API.
Python API
oneDAL has a python API that is provided as a standalone python library called daal4py. Below is an example of how daal4py can be used for calculation KMeans clusters
import numpy as np
import pandas as pd
import daal4py as d4p
data = pd.read_csv("local_kmeans_data.csv", dtype = np.float32)
init_alg = d4p.kmeans_init(nClusters = 10,
fptype = "float",
method = "randomDense")
centroids = init_alg.compute(data).centroids
alg = d4p.kmeans(nClusters = 10, maxIterations = 50, fptype = "float",
accuracyThreshold = 0, assignFlag = False)
result = alg.compute(data, centroids)
Scikit-learn patching
Python interface to efficient Intel® oneDAL provided by daal4py allows one to create scikit-learn compatible estimators, transformers, clusterers, etc. powered by oneDAL which are nearly as efficient as native programs.
| Speedups of oneDAL powered Scikit-learn over the original Scikit-learn, 28 cores, 1 thread/core |
|---|
 |
| technical details: FPType: float32; HW: Intel(R) Xeon(R) Platinum 8276L CPU @ 2.20GHz, 2 sockets, 28 cores per socket; SW: scikit-learn 0.22.2, Intel® DAAL (2019.5), Intel® Distribution Of Python (IDP) 3.7.4; Details available in the article https://medium.com/intel-analytics-software/accelerate-your-scikit-learn-applications-a06cacf44912 |
daal4py have an API which matches API from scikit-learn. This framework allows you to speed up your existing projects by changing one line of code
from daal4py.sklearn.svm import SVC
from sklearn.datasets import load_digits
digits = load_digits()
X, y = digits.data, digits.target
svm = SVC(kernel='rbf', gamma='scale', C = 0.5).fit(X, y)
print(svm.score(X, y))
In addition daal4py provides an option to replace some scikit-learn methods by oneDAL solvers which makes it possible to get a performance gain without any code changes. This approach is the basis of Intel distribution for python scikit-learn. You can patch stock scikit-learn by using the only following commandline flag
python -m daal4py my_application.py
Patches can also be enabled programmatically:
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from time import time
svm_sklearn = SVC(kernel="rbf", gamma="scale", C=0.5)
digits = load_digits()
X, y = digits.data, digits.target
start = time()
svm_sklearn = svm_sklearn.fit(X, y)
end = time()
print(end - start) # output: 0.141261...
print(svm_sklearn.score(X, y)) # output: 0.9905397885364496
from daal4py.sklearn import patch_sklearn
patch_sklearn() # <-- apply patch
from sklearn.svm import SVC
svm_d4p = SVC(kernel="rbf", gamma="scale", C=0.5)
start = time()
svm_d4p = svm_d4p.fit(X, y)
end = time()
print(end - start) # output: 0.032536...
print(svm_d4p.score(X, y)) # output: 0.9905397885364496
Distributed multi-node mode
Often data scientists require different tools for analysis regular and big data. daal4py offers various processing models, which makes it easy to enable distributed multi-node mode.
import numpy as np
import pandas as pd
import daal4py as d4p
d4p.daalinit() # <-- Initialize SPMD mode
data = pd.read_csv("local_kmeans_data.csv", dtype = np.float32)
init_alg = d4p.kmeans_init(nClusters = 10,
fptype = "float",
method = "randomDense",
distributed = True) # <-- change model to distributed
centroids = init_alg.compute(data).centroids
alg = d4p.kmeans(nClusters = 10, maxIterations = 50, fptype = "float",
accuracyThreshold = 0, assignFlag = False,
distributed = True) # <-- change model to distributed
result = alg.compute(data, centroids)
For more details browse daal4py documentation.
oneDAL Apache Spark MLlib samples
oneDAL provides scala / java interfaces that match Apache Spark MlLib API and use oneDAL solvers under the hood. This implementation allows you to get a 3-18X increase in performance compared to default Apache Spark MLlib.
technical details: FPType: double; HW: 7 x m5.2xlarge AWS instances; SW: Intel DAAL 2020 Gold, Apache Spark 2.4.4, emr-5.27.0; Spark config num executors 12, executor cores 8, executor memory 19GB, task cpus 8
Check samples tab for more details.
Installation
You can install oneDAL:
- from oneDAL home page as a part of Intel® oneAPI Base Toolkit.
- from GitHub*.
Installation from Source
See Installation from Sources for details.
Examples
Except C++ and Python API oneDAL also provide API for C++ SYCL and Java languages. Check out tabs below for more examples.
Documentation
- System Requirements
- Get Started Guide
- Developer Guide
- daal4py documentation
- Specification
- Release Notes
- Known Issues
Samples
Samples is an examples of how oneDAL can be used in different applications.
Technical Preview Features
Technical preview features are introduced to gain early feedback from developers. A technical preview feature is subject to change in the future releases. Using a technical preview feature in a production code base is therefore strongly discouraged.
In C++ APIs, technical preview features are located in daal::preview and oneapi::dal::preview namespaces. In Java APIs, technical preview features are located in packages that have the com.intel.daal.preview name prefix.
The preview features list:
- Graph Analytics:
- Undirected graph without edge and vertex weights (
undirected_adjacency_array_graph), where vertex indices can only be of type int32 - Jaccard Similarity Coefficients for all pairs of vertices, a batch algorithm that processes the graph by blocks
- Undirected graph without edge and vertex weights (
oneDAL and Intel® DAAL
Intel® oneAPI Data Analytics Library is an extension of Intel® Data Analytics Acceleration Library (Intel® DAAL).
This repository contains branches corresponding to both oneAPI and classical versions of the library. We encourage you to use oneDAL located under the master branch.
| Product | Latest release | Branch | Resources |
|---|---|---|---|
| oneDAL | 2021.1 | master rls/2021-gold-mnt |
Home page Documentation System Requirements |
| Intel® DAAL | 2020 Update 3 | rls/daal-2020-u3-rls | Home page Developer Guide System Requirements |
Contribute
See CONTRIBUTING for more information.
License
Distributed under the Apache License 2.0 license. See LICENSE for more information.
Security
To report a vulnerability, refer to Intel vulnerability reporting policy.
