Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Intel Corporation

Last update: Dec 25, 2022

Related tags

Machine Learning python machine-learning big-data analytics scikit-learn machine-learning-algorithms data-analysis hacktoberfest mach ai-training oneapi ai-inference swrepo ai-machine-learning

Overview

Intel(R) Extension for Scikit-learn*

Installation | Documentation | Examples | Support | FAQ

With Intel(R) Extension for Scikit-learn you can accelerate your Scikit-learn applications and still have full conformance with all Scikit-Learn APIs and algorithms. This is a free software AI accelerator that brings over 10-100X acceleration across a variety of applications. And you do not even need to change the existing code!

How it works?

Intel(R) Extension for Scikit-learn offers you a way to accelerate existing scikit-learn code. The acceleration is achieved through patching: replacing the stock scikit-learn algorithms with their optimized versions provided by the extension.

One of the ways to patch scikit-learn is by modifying the code. First, you import an additional Python package (sklearnex) and enable optimizations via sklearnex.patch_sklearn(). Then import scikit-learn estimators:

Enable Intel CPU optimizations

import numpy as np
from sklearnex import patch_sklearn
patch_sklearn()

from sklearn.cluster import DBSCAN

X = np.array([[1., 2.], [2., 2.], [2., 3.],
            [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
clustering = DBSCAN(eps=3, min_samples=2).fit(X)

Enable Intel GPU optimizations

import numpy as np
import dpctl
from sklearnex import patch_sklearn, config_context
patch_sklearn()

from sklearn.cluster import DBSCAN

X = np.array([[1., 2.], [2., 2.], [2., 3.],
            [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
with config_context(target_offload="gpu:0"):
    clustering = DBSCAN(eps=3, min_samples=2).fit(X)

👀 Read about other ways to patch scikit-learn and other methods for offloading to GPU devices. Check out available notebooks for more examples.

This software acceleration is achieved through the use of vector instructions, IA hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel platforms at launch time.

Supported Algorithms

❗ The patching only affects selected algorithms and their parameters.

You may still use algorithms and parameters not supported by Intel(R) Extension for Scikit-learn in your code. You will not get an error if you do this. When you use algorithms or parameters not supported by the extension, the package fallbacks into original stock version of scikit-learn.

🚀 Acceleration

Configurations:

HW: c5.24xlarge AWS EC2 Instance using an Intel Xeon Platinum 8275CL with 2 sockets and 24 cores per socket
SW: scikit-learn version 0.24.2, scikit-learn-intelex version 2021.2.3, Python 3.8

Benchmarks code

🛠 Installation

System Requirements | Install via pip or conda | Build from sources

Intel(R) Extension for Scikit-learn is available at the Python Package Index, on Anaconda Cloud in Conda-Forge channel and in Intel channel. You can also build the extension from sources.

The extension is also available as a part of Intel® oneAPI AI Analytics Toolkit (AI Kit). If you already have AI Kit installed, you do not need to install the extension.

Installation via pip package manager is recommended by default:

pip install scikit-learn-intelex

🔗 Important Links

👀 Follow us on Medium

We publish blogs on Medium, so follow us to learn tips and tricks for more efficient data analysis with the help of Intel(R) Extension for Scikit-learn. Here are our latest blogs:

❔ FAQ

[See answers to frequently asked questions]

❓ Are all algorithms affected by patching?

No. The patching only affects selected algorithms and their parameters.

❓ What happens if I use parameters not supported by the extension?

In cases when unsupported parameters are used, the package fallbacks into original stock version of scikit-learn. You will not get an error.

❓ What happens if I run algorithms not supported by the extension?

If you use algorithms for which no optimizations are available, their original version from the stock scikit-learn is used.

❓ Can I see which implementation of the algorithm is currently used?

Yes. To find out which implementation of the algorithm is currently used (Intel(R) Extension for Scikit-learn or original Scikit-learn), use the verbose mode.

❓ How much faster scikit-learn is after the patching?

We compare the performance of Intel(R) Extension for Scikit-Learn to other frameworks in Machine Learning Benchmarks. Read our blogs on Medium if you are interested in the detailed comparison.

❓ What if the patching does not cover my scenario?

If the patching does not cover your scenarios, submit an issue on GitHub with the description of what you would want to have.

💬 Support

Report issues, ask questions, and provide suggestions using:

You may reach out to project maintainers privately at [email protected]

oneAPI

Intel(R) Extension for Scikit-learn is part of oneAPI and Intel® oneAPI AI Analytics Toolkit (AI Kit).

daalpy and oneDAL

The acceleration is achieved through the use of the Intel(R) oneAPI Data Analytics Library (oneDAL). Learn more:

⚠️ Intel(R) Extension for Scikit-learn contains scikit-learn patching functionality that was originally available in daal4py package. All future updates for the patches will be available only in Intel(R) Extension for Scikit-learn. We recommend you to use scikit-learn-intelex package instead of daal4py. You can learn more about daal4py in daal4py documentation.

Comments

Intel(R) Distribution for Python's sklearn patches
of scikit-learn classes as a stand-alone module.

@fschlimb @anton-malakhov @ogrisel

They can be invoked via

python -m daal4py.sklearn_patches script.py args

or by explicitly enabling them via

import daal4py.sklearn_patches.dispatcher daal4py.sklearn_patches.dispatcher.enable()

Names, design, etc. are up for discussion.
opened by oleksandr-pavlyk 18

Intel oneDAL FATAL ERROR on Windows10

Describe the bug The windows wheel is unable to load the dll's needed to run the examples. I tried one of the examples and got the following when calling a library function

Intel oneDAL FATAL ERROR: onedal_thread.1.dll. Error code is 0x80096005. Intel oneDAL FATAL ERROR: onedal_sequential.1.dll. Error code is 0x80096005. Intel oneDAL FATAL ERROR: Cannot load neither onedal_thread.1.dll nor onedal_sequential.1.dll. Intel oneDAL FATAL ERROR: onedal_thread.1.dll. Error code is 0x80096005. Intel oneDAL FATAL ERROR: onedal_sequential.1.dll. Error code is 0x80096005. Intel oneDAL FATAL ERROR: Cannot load neither onedal_thread.1.dll nor onedal_sequential.1.dll.

To Reproduce Steps to reproduce the behavior:

pip install daal4py pandas lightgbm on Windows10
Alter the __init__.py to have path_to_libs = os.path.join(os.path.dirname(sys.executable), "..\\Library\\bin") (note the ..)
Run the following script

import lightgbm as lgb
import numpy as np
import daal4py as d4p


def get_data(n, m):
    x_train = np.random.randn(n, m).astype(np.float32)
    A = np.random.randint(-5, 5, size=(m, 1))
    y_train = (x_train @ A).astype(np.float32)

    return x_train, y_train

n = 1000
m = 25
x_train, y_train = get_data(n, m)

params = {
    'task': 'train',
    'boosting_type': 'gbdt',
    'objective': 'regression',
    'metric': ['rmse'],
    'device': 'cpu',
    'num_leaves': 31,
    'bagging_fraction': 0.5,
    'feature_fraction': 0.5,
    'learning_rate': 0.001,
    'verbose': 2,
    'max_bin': 255,
}
ds_train = lgb.Dataset(x_train, y_train.ravel())#, free_raw_data=False)
gbm = lgb.train(
    params,
    ds_train,
    num_boost_round=10,
    # keep_training_booster=args.keep_training_booster,
)

print("Converting...", flush=True)
daal_model = d4p.get_gbt_model_from_lightgbm(gbm)
print("Converted...", flush=True)
daal_prediction = d4p.gbt_regression_prediction().compute(x_train, daal_model).prediction

See output

[LightGBM] [Debug] Dataset::GetMultiBinFromAllFeatures: sparse rate 0.000000
[LightGBM] [Debug] init for col-wise cost 0.000021 seconds, init for row-wise cost 0.000844 seconds
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001624 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 6375
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 25
[LightGBM] [Info] Start training from score 0.386410
[LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 31 and max_depth = 8
Converting...
Converted...
Intel oneDAL FATAL ERROR: onedal_thread.1.dll. Error code is 0x80096005.
Intel oneDAL FATAL ERROR: onedal_sequential.1.dll. Error code is 0x80096005.
Intel oneDAL FATAL ERROR: Cannot load neither onedal_thread.1.dll nor onedal_sequential.1.dll.
Intel oneDAL FATAL ERROR: onedal_thread.1.dll. Error code is 0x80096005.
Intel oneDAL FATAL ERROR: onedal_sequential.1.dll. Error code is 0x80096005.
Intel oneDAL FATAL ERROR: Cannot load neither onedal_thread.1.dll nor onedal_sequential.1.dll.

Expected behavior Successfully converted lightgbm model

Output/Screenshots Would note that I do see the dlls in the correct folder and the directory is added to PATH

Environment:

OS: Windows10
Compiler: ?
Version: ?
Python: 3.6.6

bug

opened by CHDev93 15

Ubuntu 18.04: ModuleNotFoundError: No module named 'daal4py'

Hi When I try to run any sample example using Anaconda & jupyter notebook in Ubuntu 18.04, it says ModuleNotFoundError: No module named 'daal4py'

But I can run the same code on python interpretor and Ubuntu 16.04 via anaconda & Jupyter notebook. Have anyone else identified this issue? I am experienceing this issue after I migrated to Ubuntu 18.04 itself. Any solution? Thank you

opened by abhi-84 15
OneAPI ifaces for kNN Regression
Description

Please include a summary of the change. For large or complex changes please include enough information to introduce your change and explain motivation for it.

Changes proposed in this pull request:
opened by KalyanovD 14
Library crashes on sklearn >= 1.1.0
Describe the bug Version 2021.5.3 of scikit-learn-intelex seems incompatible with latest 1.1.1 version of scikit-learn. Latest scikit-learn which works is 1.0.2, so I had to downgrade it.

Moreover, I know that scikit-learn-intelex 2021.6.0 is suggested in my upgrades but pip gives an error every time it tries to download it ( #1003), so I am referring 2021.5.3 as the latest version.

To Reproduce

Install latest scikit-learn-intelex (2021.5.3) and scikit-learn (1.1.1)

Import it to an existing sklearn python project file

from sklearnex import patch_sklearn patch_sklearn()

Run it and obtain this error:

from sklearn.utils.fixes import sparse_lsqr ImportError: cannot import name 'sparse_lsqr' from 'sklearn.utils.fixes'

Expected behavior Run it without any importing error.

Output/Screenshots None.

Environment:

OS: Linux Mint 20.3

Compiler: Python 3.8.10

Version: 2021.5.3

bug
opened by gabrielication 12

Cannot import patch_sklearn

Describe the bug Calling

from sklearnex import patch_sklearn
patch_sklearn()

leads to the following error:

Python 3.8.12 (default, Oct 12 2021, 06:23:56) 
Type "copyright", "credits" or "license" for more information.

IPython 7.29.0 -- An enhanced Interactive Python.

from sklearnex import patch_sklearn
patch_sklearn()
Traceback (most recent call last):

  File "/var/folders/lg/d49pbfkx1mxg1y8v5l810v4w0000gn/T/ipykernel_2384/4125287122.py", line 1, in <module>
    from sklearnex import patch_sklearn

  File "/Users/muhlbach/opt/anaconda3/envs/main/lib/python3.8/site-packages/sklearnex/__init__.py", line 18, in <module>
    from .dispatcher import patch_sklearn

  File "/Users/muhlbach/opt/anaconda3/envs/main/lib/python3.8/site-packages/sklearnex/dispatcher.py", line 22, in <module>
    from daal4py.sklearn._utils import daal_check_version

  File "/Users/muhlbach/opt/anaconda3/envs/main/lib/python3.8/site-packages/daal4py/__init__.py", line 30, in <module>
    from _daal4py import *

ImportError: dlopen(/Users/muhlbach/opt/anaconda3/envs/main/lib/python3.8/site-packages/_daal4py.cpython-38-darwin.so, 2): Library not loaded: @rpath/libonedal_core.dylib
  Referenced from: /Users/muhlbach/opt/anaconda3/envs/main/lib/python3.8/site-packages/_daal4py.cpython-38-darwin.so
  Reason: image not found

This is despite having installed the requirements, for instance,

% conda list | grep daal
daal                      2021.3.0           hecd8cb5_555  
daal4py                   2021.3.0         py38h01d92e1_0

and

% conda list | grep scik
scikit-learn              1.0.1            py38hae1ba45_0  
scikit-learn-intelex      2021.3.0         py38hecd8cb5_0  
scikit-lego               0.6.8              pyhd8ed1ab_0    conda-forge
scikit-misc               0.1.3            py38h050221e_2    conda-forge
scikit-optimize           0.9.0              pyhd8ed1ab_0    conda-forge

Prior to running this, I updated all packages via conda update --all.

Environment:

ProductName:	macOS
ProductVersion:	11.6
BuildVersion:	20G165

Compiler: [e.g. GCC9.2]

% gcc --version
Configured with: --prefix=/Library/Developer/CommandLineTools/usr --with-gxx-include-dir=/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/4.2.1
Apple clang version 12.0.5 (clang-1205.0.22.9)
Target: arm64-apple-darwin20.6.0
Thread model: posix
InstalledDir: /Library/Developer/CommandLineTools/usr/bin

Version: 2021.3.0
General environment

% conda list
# packages in environment at /Users/muhlbach/opt/anaconda3/envs/main:
#
# Name                    Version                   Build  Channel
_ipyw_jlab_nb_ext_conf    0.1.0                    py38_0  
_py-xgboost-mutex         2.0                       cpu_0  
abseil-cpp                20210324.2           h23ab428_0  
alabaster                 0.7.12             pyhd3eb1b0_0  
anaconda-client           1.9.0            py38hecd8cb5_0  
anyio                     2.2.0            py38hecd8cb5_1  
appdirs                   1.4.4              pyhd3eb1b0_0  
applaunchservices         0.2.1              pyhd3eb1b0_0  
appnope                   0.1.2           py38hecd8cb5_1001  
argh                      0.26.2                   py38_0  
argon2-cffi               20.1.0           py38h9ed2024_1  
arrow                     0.13.1                   py38_0  
arrow-cpp                 4.0.1           py38h0e2c213_7_cpu    conda-forge
arviz                     0.11.2             pyhd3eb1b0_0  
astroid                   2.6.6            py38hecd8cb5_0  
async_generator           1.10               pyhd3eb1b0_0  
atomicwrites              1.4.0                      py_0  
attrs                     21.2.0             pyhd3eb1b0_0  
autograd                  1.3                pyhd3eb1b0_1  
autopep8                  1.5.7              pyhd3eb1b0_0  
aws-c-cal                 0.5.11               hd2e2f4b_0    conda-forge
aws-c-common              0.6.2                h9ed2024_0  
aws-c-event-stream        0.2.7               hb9330a7_13    conda-forge
aws-c-io                  0.10.5               h35aa462_0    conda-forge
aws-checksums             0.1.11               h0010a65_7    conda-forge
aws-sdk-cpp               1.8.186              h766a74d_3    conda-forge
babel                     2.9.1              pyhd3eb1b0_0  
backcall                  0.2.0              pyhd3eb1b0_0  
backports                 1.0                pyhd3eb1b0_2  
backports.functools_lru_cache 1.6.4              pyhd3eb1b0_0  
backports.tempfile        1.0                pyhd3eb1b0_1  
backports.weakref         1.0.post1                  py_1  
bats                      0.4.0                         1    bioconda
beautifulsoup4            4.10.0             pyh06a4308_0  
binaryornot               0.4.4              pyhd3eb1b0_1  
black                     19.10b0                    py_0  
blas                      1.0                    openblas  
bleach                    4.0.0              pyhd3eb1b0_0  
blosc                     1.21.0               he49afe7_0    conda-forge
bokeh                     2.4.1            py38hecd8cb5_0  
boost-cpp                 1.67.0               h1de35cc_4  
bottleneck                1.3.2            py38hf1fa96c_1  
brotli                    1.0.9                hb1e8313_2  
brotlipy                  0.7.0           py38h9ed2024_1003  
build                     0.7.0              pyhd8ed1ab_0    conda-forge
bzip2                     1.0.8                h1de35cc_0  
c-ares                    1.17.1               h9ed2024_0  
ca-certificates           2021.10.26           hecd8cb5_2  
cctools_osx-64            949.0.1             hc7db93f_23  
certifi                   2021.10.8        py38hecd8cb5_0  
cffi                      1.14.6           py38h2125817_0  
cftime                    1.5.0            py38he3068b8_0  
chardet                   4.0.0           py38hecd8cb5_1003  
charset-normalizer        2.0.4              pyhd3eb1b0_0  
clang                     11.1.0               h694c41f_1    conda-forge
clang-11                  11.1.0          default_he082bbe_1    conda-forge
clang_osx-64              11.1.0               hb91bd55_4    conda-forge
clangxx                   11.1.0          default_he082bbe_1    conda-forge
clangxx_osx-64            11.1.0               h7e1b574_4    conda-forge
click                     8.0.3              pyhd3eb1b0_0  
cloudpickle               2.0.0              pyhd3eb1b0_0  
clyent                    1.2.2                    py38_1  
cmarkgfm                  0.4.2            py38h9ed2024_0  
colorama                  0.4.4              pyhd3eb1b0_0  
compiler-rt               11.1.0               h654b07c_0    conda-forge
compiler-rt_osx-64        11.1.0               h8c5fa43_0    conda-forge
conda-content-trust       0.1.1              pyhd3eb1b0_0  
conda-package-handling    1.7.3            py38h9ed2024_1  
conda-repo-cli            1.0.4              pyhd3eb1b0_0  
conda-verify              3.4.2                      py_1  
convertdate               2.3.2              pyhd3eb1b0_0  
cookiecutter              1.7.2              pyhd3eb1b0_0  
cryptography              35.0.0           py38h2fd3fbb_0  
curl                      7.78.0               h7bc2e8c_0  
cvxpy                     1.1.17           py38h50d1736_1    conda-forge
cvxpy-base                1.1.17           py38ha53d530_1    conda-forge
cycler                    0.10.0                   py38_0  
cython                    0.29.23          py38h23ab428_0  
cytoolz                   0.11.0           py38haf1e3a3_0  
daal                      2021.3.0           hecd8cb5_555  
daal4py                   2021.3.0         py38h01d92e1_0  
dal                       2021.3.0           hecd8cb5_555  
dask                      2021.10.0          pyhd3eb1b0_0  
dask-core                 2021.10.0          pyhd3eb1b0_0  
dataclasses               0.8                pyh6d0b6a4_7  
dbus                      1.13.18              h18a8e69_0  
dcor                      0.5.3              pyhd8ed1ab_0    conda-forge
debugpy                   1.5.1            py38he9d5cce_0  
decorator                 5.1.0              pyhd3eb1b0_0  
defusedxml                0.7.1              pyhd3eb1b0_0  
deprecated                1.2.12             pyhd3eb1b0_0  
deprecation               2.1.0              pyhd3eb1b0_0  
descartes                 1.1.0              pyhd3eb1b0_4  
diff-match-patch          20200713           pyhd3eb1b0_0  
distributed               2021.10.0        py38hecd8cb5_0  
docutils                  0.17.1           py38hecd8cb5_1  
ecos                      2.0.7.post1      py38he3068b8_0  
entrypoints               0.3                      py38_0  
ephem                     4.0.0.2          py38h9ed2024_0  
et_xmlfile                1.1.0            py38hecd8cb5_0  
expat                     2.4.1                h23ab428_2  
fastparquet               0.5.0            py38he3068b8_1  
feature_engine            1.1.2              pyhd8ed1ab_0    conda-forge
featuretools              0.27.1             pyhd3eb1b0_2  
filelock                  3.3.1              pyhd3eb1b0_1  
flake8                    3.9.2              pyhd3eb1b0_0  
flask                     1.1.2              pyhd3eb1b0_0  
fonttools                 4.25.0             pyhd3eb1b0_0  
freetype                  2.11.0               hd8bbffd_0  
freezegun                 1.1.0              pyhd3eb1b0_0  
fsspec                    2021.10.1          pyhd3eb1b0_0  
future                    0.18.2                   py38_1  
gensim                    4.0.1            py38h23ab428_0  
gettext                   0.21.0               h7535e17_0  
gflags                    2.2.2                h0a44026_0  
giflib                    5.2.1                haf1e3a3_0  
glib                      2.69.1               hdf23fa2_0  
glob2                     0.7                pyhd3eb1b0_0  
glog                      0.5.0                h23ab428_0  
greenlet                  1.1.1            py38h23ab428_0  
grpc-cpp                  1.39.0               hbd5ceb7_2    conda-forge
hcrystalball              0.1.10             pyhd8ed1ab_0    conda-forge
hdf4                      4.2.13               h39711bb_2  
hdf5                      1.10.6          nompi_hc5d9132_1114    conda-forge
heapdict                  1.0.1              pyhd3eb1b0_0  
hijri-converter           2.2.2              pyhd3eb1b0_0  
holidays                  0.11.3.1           pyhd3eb1b0_0  
html5lib                  1.1                pyhd3eb1b0_0  
icu                       58.2                 h0a44026_3  
idna                      3.2                pyhd3eb1b0_0  
imagesize                 1.2.0              pyhd3eb1b0_0  
importlib-metadata        4.8.1            py38hecd8cb5_0  
importlib_metadata        4.8.1                hd3eb1b0_0  
inflection                0.5.1            py38hecd8cb5_0  
intel-openmp              2021.4.0          hecd8cb5_3538  
intervaltree              3.1.0              pyhd3eb1b0_0  
ipykernel                 6.4.1            py38hecd8cb5_1  
ipython                   7.29.0           py38h01d92e1_0  
ipython_genutils          0.2.0              pyhd3eb1b0_1  
ipywidgets                7.6.5              pyhd3eb1b0_1  
isort                     5.9.3              pyhd3eb1b0_0  
itsdangerous              2.0.1              pyhd3eb1b0_0  
jaydebeapi                1.2.3                      py_0  
jbig                      2.1                  h4d881f8_0  
jdcal                     1.4.1              pyhd3eb1b0_0  
jedi                      0.18.0           py38hecd8cb5_1  
jinja2                    2.11.3             pyhd3eb1b0_0  
jinja2-time               0.2.0              pyhd3eb1b0_2  
joblib                    1.1.0              pyhd3eb1b0_0  
jpeg                      9d                   h9ed2024_0  
jplephem                  2.15               pyh2f6353c_0    conda-forge
jpype1                    1.2.1            py38hf7b0b51_0  
json5                     0.9.6              pyhd3eb1b0_0  
jsonschema                3.2.0              pyhd3eb1b0_2  
jupyter-packaging         0.10.4             pyhd3eb1b0_0  
jupyter_client            6.1.12             pyhd3eb1b0_0  
jupyter_core              4.9.1            py38hecd8cb5_0  
jupyter_server            1.4.1            py38hecd8cb5_0  
jupyterlab                3.2.1              pyhd3eb1b0_1  
jupyterlab_pygments       0.1.2                      py_0  
jupyterlab_server         2.8.2              pyhd3eb1b0_0  
jupyterlab_widgets        1.0.0              pyhd3eb1b0_1  
keyring                   23.1.0           py38hecd8cb5_0  
kiwisolver                1.3.1            py38h23ab428_0  
korean_lunar_calendar     0.2.1              pyhd3eb1b0_0  
krb5                      1.19.2               hcd88c3b_0  
lazy-object-proxy         1.6.0            py38h9ed2024_0  
lcms2                     2.12                 hf1fd2bf_0  
ld64_osx-64               530                 he8994da_21    conda-forge
ldid                      2.1.2                h2d21305_2  
lerc                      2.2.1                h23ab428_0  
libarchive                3.5.2                h2b60450_1    conda-forge
libblas                   3.9.0           12_osx64_openblas    conda-forge
libboost                  1.67.0               hebc422b_4  
libbrotlicommon           1.0.9                h0d85af4_6    conda-forge
libbrotlidec              1.0.9                h0d85af4_6    conda-forge
libbrotlienc              1.0.9                h0d85af4_6    conda-forge
libcblas                  3.9.0           12_osx64_openblas    conda-forge
libclang-cpp11.1          11.1.0          default_he082bbe_1    conda-forge
libcurl                   7.78.0               hb8e4fae_0  
libcxx                    12.0.0               h2f01273_0  
libdeflate                1.7                  h9ed2024_5  
libedit                   3.1.20210910         hca72f7f_0  
libev                     4.33                 h9ed2024_1  
libevent                  2.1.8                hddc9c9b_1  
libffi                    3.3                  hb1e8313_2  
libgfortran               5.0.0           9_3_0_h6c81a4c_23    conda-forge
libgfortran5              9.3.0               h6c81a4c_23    conda-forge
libiconv                  1.16                 h1de35cc_0  
liblapack                 3.9.0           12_osx64_openblas    conda-forge
liblief                   0.10.1               h0a44026_0  
libllvm10                 10.0.1               h76017ad_5  
libllvm11                 11.1.0               hd011deb_2    conda-forge
libllvm12                 12.0.0               h9b2ccf5_3  
libnetcdf                 4.6.1                hfd9a460_4  
libnghttp2                1.46.0               ha29bfda_0  
libopenblas               0.3.18          openmp_h3351f45_0    conda-forge
libpng                    1.6.37               ha441bb4_0  
libprotobuf               3.16.0               hcf210ce_0    conda-forge
libsodium                 1.0.18               h1de35cc_0  
libspatialindex           1.9.3                h23ab428_0  
libssh2                   1.9.0                ha12b0ac_1  
libthrift                 0.14.2               h054ceb0_0  
libtiff                   4.3.0                h1167814_1    conda-forge
libutf8proc               2.6.1                h9ed2024_0  
libwebp                   1.2.0                hacca55c_0  
libwebp-base              1.2.0                h9ed2024_0  
libxgboost                1.3.3                h23ab428_0  
libxml2                   2.9.12               hcdb78fc_0  
libxslt                   1.1.34               h83b36ba_0  
lightgbm                  3.2.1            py38h23ab428_0  
linearmodels              4.24             py38hbe852b5_3    conda-forge
llvm-openmp               12.0.1               hda6cdc1_1    conda-forge
llvm-tools                11.1.0               hd011deb_2    conda-forge
llvmlite                  0.37.0           py38he4411ff_1  
locket                    0.2.1            py38hecd8cb5_1  
lunarcalendar             0.0.9                      py_0    conda-forge
lunardate                 0.2.0                      py_0    conda-forge
lxml                      4.6.3            py38h26b266a_0  
lz4-c                     1.9.3                h23ab428_1  
lzo                       2.10                 haf1e3a3_2  
markupsafe                1.1.1            py38h1de35cc_1  
matplotlib                3.4.3            py38hecd8cb5_0  
matplotlib-base           3.4.3            py38h0a11d32_0  
matplotlib-inline         0.1.2              pyhd3eb1b0_2  
mccabe                    0.6.1                    py38_1  
mistune                   0.8.4           py38h1de35cc_1001  
mizani                    0.7.3              pyhd8ed1ab_0    conda-forge
mkl                       2021.4.0           hecd8cb5_637  
mkl-service               2.4.0            py38h9ed2024_0  
mkl_fft                   1.3.1            py38h0834169_1    conda-forge
mkl_random                1.2.2            py38h1f261ad_0    conda-forge
mlregression              0.1.0                    pypi_0    pypi
mlxtend                   0.19.0             pyhd8ed1ab_0    conda-forge
mock                      4.0.3              pyhd3eb1b0_0  
more-itertools            8.10.0             pyhd3eb1b0_0  
mpi                       1.0                       mpich  
mpich                     3.3.2                external_0  
msgpack-python            1.0.2            py38hf7b0b51_1  
munkres                   1.1.4                      py_0  
mypy_extensions           0.4.3                    py38_0  
navigator-updater         0.2.1                    py38_0  
nbclassic                 0.2.6              pyhd3eb1b0_0  
nbclient                  0.5.3              pyhd3eb1b0_0  
nbconvert                 6.1.0            py38hecd8cb5_0  
nbformat                  5.1.3              pyhd3eb1b0_0  
ncurses                   6.3                  hca72f7f_2  
nest-asyncio              1.5.1              pyhd3eb1b0_0  
netcdf4                   1.5.7            py38h1695cb1_0  
nltk                      3.6.5              pyhd3eb1b0_0  
notebook                  6.4.6            py38hecd8cb5_0  
numba                     0.54.1           py38hae1ba45_0  
numexpr                   2.7.3            py38h7ec9b2a_1  
numpy                     1.19.5           py38had91d27_2    conda-forge
numpy-base                1.19.1           py38h68fea81_0    anaconda
numpydoc                  1.1.0              pyhd3eb1b0_1  
olefile                   0.46               pyhd3eb1b0_0  
openjpeg                  2.4.0                h66ea3da_0  
openpyxl                  3.0.9              pyhd3eb1b0_0  
openssl                   1.1.1l               h9ed2024_0  
orc                       1.6.9                hfe4c36d_0    conda-forge
osqp                      0.6.2.post0      py38ha53d530_3    conda-forge
packaging                 21.0               pyhd3eb1b0_0  
palettable                3.3.0              pyhd3eb1b0_0  
pandas                    1.3.4            py38h743cdd8_0  
pandocfilters             1.4.3            py38hecd8cb5_1  
parso                     0.8.2              pyhd3eb1b0_0  
partd                     1.2.0              pyhd3eb1b0_0  
pathspec                  0.7.0                      py_0  
patsy                     0.5.2            py38hecd8cb5_0  
pcre                      8.45                 h23ab428_0  
pep517                    0.12.0           py38h50d1736_1    conda-forge
pexpect                   4.8.0              pyhd3eb1b0_3  
pickleshare               0.7.5           pyhd3eb1b0_1003  
pillow                    8.4.0            py38h98e4679_0  
pip                       21.2.4           py38hecd8cb5_0  
pkginfo                   1.7.1            py38hecd8cb5_0  
plotly                    5.1.0              pyhd3eb1b0_0  
plotnine                  0.8.0              pyhd8ed1ab_0    conda-forge
pluggy                    1.0.0            py38hecd8cb5_0  
pmdarima                  1.8.2            py38h96a0964_3    conda-forge
poyo                      0.5.0              pyhd3eb1b0_0  
prometheus_client         0.12.0             pyhd3eb1b0_0  
prompt-toolkit            3.0.20             pyhd3eb1b0_0  
property-cached           1.6.4                      py_0    conda-forge
property_cached           1.6.4                         0    conda-forge
prophet                   1.0.1            py38h6c79ece_3    conda-forge
psutil                    5.8.0            py38h9ed2024_1  
ptyprocess                0.7.0              pyhd3eb1b0_2  
py-lief                   0.10.1           py38haf313ee_0  
py-xgboost                1.3.3            py38hecd8cb5_0  
pyaml                     20.4.0             pyhd3eb1b0_0  
pyarrow                   4.0.1            py38hdf3e9eb_3  
pycalverter               1.6.1                      py_0    conda-forge
pycodestyle               2.7.0              pyhd3eb1b0_0  
pycosat                   0.6.3            py38h1de35cc_1  
pycparser                 2.21               pyhd3eb1b0_0  
pydocstyle                6.1.1              pyhd3eb1b0_0  
pyflakes                  2.3.1              pyhd3eb1b0_0  
pygments                  2.10.0             pyhd3eb1b0_0  
pyhdfe                    0.1.0              pyhd8ed1ab_0    conda-forge
pylint                    2.9.6            py38hecd8cb5_1  
pyls-spyder               0.4.0              pyhd3eb1b0_0  
pyluach                   1.3.0              pyhd8ed1ab_0    conda-forge
pymeeus                   0.5.11             pyhd3eb1b0_1  
pynndescent               0.5.4              pyhd3eb1b0_0  
pyodbc                    4.0.31           py38h23ab428_0  
pyopenssl                 21.0.0             pyhd3eb1b0_1  
pyparsing                 3.0.4              pyhd3eb1b0_0  
pypyodbc                  1.3.5            py38h50d1736_3    conda-forge
pyqt                      5.9.2            py38h655552a_2  
pyrsistent                0.18.0           py38hca72f7f_0  
pysocks                   1.7.1                    py38_1  
pystan                    2.19.1.1         py38h1f261ad_3    conda-forge
pytables                  3.6.1            py38hfb086ad_3    conda-forge
python                    3.8.12               h88f2d9e_0  
python-dateutil           2.8.2              pyhd3eb1b0_0  
python-jsonrpc-server     0.4.0                      py_0  
python-libarchive-c       2.9                pyhd3eb1b0_1  
python-lsp-black          1.0.0              pyhd3eb1b0_0  
python-lsp-jsonrpc        1.0.0              pyhd3eb1b0_0  
python-lsp-server         1.2.4              pyhd3eb1b0_0  
python-slugify            5.0.2              pyhd3eb1b0_0  
python.app                3                py38hca72f7f_0  
python_abi                3.8                      2_cp38    conda-forge
pytz                      2021.3             pyhd3eb1b0_0  
pyyaml                    6.0              py38hca72f7f_1  
pyzmq                     22.2.1           py38h23ab428_1  
qdarkstyle                3.0.2              pyhd3eb1b0_0  
qdldl-python              0.1.5            py38ha53d530_2    conda-forge
qstylizer                 0.1.10             pyhd3eb1b0_0  
qt                        5.9.7                h468cd18_1  
qtawesome                 1.0.2              pyhd3eb1b0_0  
qtconsole                 5.1.1              pyhd3eb1b0_0  
qtpy                      1.10.0             pyhd3eb1b0_0  
re2                       2021.08.01           he49afe7_0    conda-forge
readline                  8.1                  h9ed2024_0  
readme_renderer           24.0             py38hecd8cb5_0  
regex                     2021.8.3         py38h9ed2024_0  
requests                  2.26.0             pyhd3eb1b0_0  
requests-toolbelt         0.9.1              pyhd3eb1b0_0  
rfc3986                   1.4.0              pyhd3eb1b0_0  
ripgrep                   12.1.1                        0  
rope                      0.21.1             pyhd3eb1b0_0  
rtree                     0.9.7            py38hecd8cb5_1  
ruamel_yaml               0.15.100         py38h9ed2024_0  
scikit-learn              1.0.1            py38hae1ba45_0  
scikit-learn-intelex      2021.3.0         py38hecd8cb5_0  
scikit-lego               0.6.8              pyhd8ed1ab_0    conda-forge
scikit-misc               0.1.3            py38h050221e_2    conda-forge
scikit-optimize           0.9.0              pyhd8ed1ab_0    conda-forge
scipy                     1.7.2            py38hd329d04_0    conda-forge
scs                       3.0.0            py38hd383344_1    conda-forge
seaborn                   0.11.2             pyhd3eb1b0_0  
send2trash                1.8.0              pyhd3eb1b0_1  
setuptools                49.6.0           py38h50d1736_3    conda-forge
sgp4                      2.20             py38hb0f0857_1    conda-forge
sip                       4.19.8           py38h0a44026_0  
six                       1.15.0           py38hecd8cb5_0  
skorch                    0.10.0             pyh59e0f4d_0    conda-forge
sktime                    0.8.1            py38hffa2396_0    conda-forge
skyfield                  1.40               pyh6c4a22f_0    conda-forge
skyfield-data             3.0.0              pyhd3deb0d_0    conda-forge
smart_open                5.2.1              pyhd8ed1ab_0    conda-forge
snappy                    1.1.8                hb1e8313_0  
sniffio                   1.2.0            py38hecd8cb5_1  
snowballstemmer           2.1.0              pyhd3eb1b0_0  
sortedcontainers          2.4.0              pyhd3eb1b0_0  
soupsieve                 2.3.1              pyhd3eb1b0_0  
sphinx                    4.2.0              pyhd3eb1b0_1  
sphinxcontrib-applehelp   1.0.2              pyhd3eb1b0_0  
sphinxcontrib-devhelp     1.0.2              pyhd3eb1b0_0  
sphinxcontrib-htmlhelp    2.0.0              pyhd3eb1b0_0  
sphinxcontrib-jsmath      1.0.1              pyhd3eb1b0_0  
sphinxcontrib-qthelp      1.0.3              pyhd3eb1b0_0  
sphinxcontrib-serializinghtml 1.1.5              pyhd3eb1b0_0  
spyder                    5.1.5            py38hecd8cb5_1  
spyder-kernels            2.1.3            py38hecd8cb5_0  
sqlalchemy                1.4.22           py38h9ed2024_0  
sqlite                    3.36.0               hce871da_0  
statsmodels               0.12.2           py38h9ed2024_0  
tabulate                  0.8.9            py38hecd8cb5_0  
tapi                      1100.0.11            h0025ef7_0  
tbb                       2021.4.0             haf03e11_0  
tblib                     1.7.0              pyhd3eb1b0_0  
tenacity                  8.0.1            py38hecd8cb5_0  
terminado                 0.9.4            py38hecd8cb5_0  
testfixtures              6.18.0             pyhd3eb1b0_0  
testpath                  0.5.0              pyhd3eb1b0_0  
text-unidecode            1.3                pyhd3eb1b0_0  
textdistance              4.2.1              pyhd3eb1b0_0  
threadpoolctl             2.2.0              pyh0d69192_0  
three-merge               0.1.1              pyhd3eb1b0_0  
thrift                    0.11.0           py38h23ab428_0  
tinycss                   0.4             pyhd3eb1b0_1002  
tk                        8.6.11               h7bc2e8c_0  
toml                      0.10.2             pyhd3eb1b0_0  
tomli                     1.2.2              pyhd8ed1ab_0    conda-forge
tomlkit                   0.7.2            py38hecd8cb5_1  
toolz                     0.11.2             pyhd3eb1b0_0  
tornado                   6.1              py38h9ed2024_0  
tqdm                      4.62.3             pyhd3eb1b0_1  
traitlets                 5.1.1              pyhd3eb1b0_0  
treebased-synthetic-controls 0.1.10                   pypi_0    pypi
tslearn                   0.5.2            py38hbe852b5_0    conda-forge
twine                     3.4.1            py38hecd8cb5_1  
typed-ast                 1.4.3            py38h9ed2024_1  
typing-extensions         3.10.0.2             hd3eb1b0_0  
typing_extensions         3.10.0.2           pyh06a4308_0  
ujson                     4.0.2            py38h23ab428_0  
umap-learn                0.5.2            py38h50d1736_0    conda-forge
unidecode                 1.2.0              pyhd3eb1b0_0  
unixodbc                  2.3.9                haf1e3a3_0  
urllib3                   1.26.7             pyhd3eb1b0_0  
watchdog                  2.1.3            py38hd5f6b7e_0  
wcwidth                   0.2.5              pyhd3eb1b0_0  
webencodings              0.5.1                    py38_1  
werkzeug                  2.0.2              pyhd3eb1b0_0  
wheel                     0.37.0             pyhd3eb1b0_1  
whichcraft                0.6.1              pyhd3eb1b0_0  
widgetsnbextension        3.5.1                    py38_0  
workalendar               16.1.0             pyhd8ed1ab_0    conda-forge
wrapt                     1.12.1           py38haf1e3a3_1  
wurlitzer                 2.1.1            py38hecd8cb5_0  
xarray                    0.19.0             pyhd3eb1b0_1  
xgboost                   1.3.3            py38hecd8cb5_0  
xlrd                      2.0.1              pyhd3eb1b0_0  
xmltodict                 0.12.0             pyhd3eb1b0_0  
xz                        5.2.5                h1de35cc_0  
yaml                      0.2.5                haf1e3a3_0  
yapf                      0.31.0             pyhd3eb1b0_0  
zeromq                    4.3.4                h23ab428_0  
zict                      2.0.0              pyhd3eb1b0_0  
zipp                      3.6.0              pyhd3eb1b0_0  
zlib                      1.2.11               h1de35cc_3  
zstd                      1.5.0                hcb37349_1

bug

opened by muhlbach 10

Estimators memory leak testing
Description

Changes proposed in this pull request:

Move memory leak tests from unittest to pytest

Enable memory leak testing for all patched estimators except TSNE

testing
opened by Alexsandruss 10
Device information in patching, option to fallback on host
Scikit-learn patching messages reformulated to mention Intel(R) Extension for Scikit-learn* instead of oneDAL

SKLEARNEX INFO: <estimator.method>: running accelerated version on [CPU/GPU]

SKLEARNEX INFO: <estimator.method>: fallback to original Scikit-learn

SKLEARNEX INFO: <estimator.method>: failed to run accelerated version, fallback to original Scikit-learn

Logging information extended with basic device information where accelerated code runs.

New parameter host_offload_on_fail added to sycl_execution_context that allows to fallback on the host if GPU execution is failed.

oneAPI sklearn-patch
opened by michael-smirnov 9
python -m sklearnex my_application.py VERSUS patch_sklearn()
Hi developers and maintainers! First of all, I love this improvement and it really speeds up my programs by 100x! It's amazing.

To my question (not a bug): Say I have a program (my_application.py) that imports packages from Scikit-learn, e.g., from sklearn.ensemble import RandomForestRegressor and runs some subsequent code.

Question: What is the difference between executing the program via the Terminal by calling python -m sklearnex my_application.py OR editing the program itself, e.g. by

from sklearnex import patch_sklearn patch_sklearn() from sklearn.ensemble import RandomForestRegressor

Looking forward to hearing from you!
bug
opened by muhlbach 8
added tests for RandomForestClassifier class_weight and sample_weight & tests for KNN

Added tests which are testing accuracy ratio RandomForestClassifier between Scikit-learn and daal4py on the next parameters: sample_weight, class_weight.

opened by OnlyDeniko 8

Python 3.8 support

Is there a target date on this?

2020-06-04T17:01:20.6412343Z + conda install -y -c intel daal4py
2020-06-04T17:01:22.7290486Z Collecting package metadata (current_repodata.json): ...working... done
2020-06-04T17:01:26.3949643Z Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve.
2020-06-04T17:01:31.4115908Z Solving environment: ...working... failed with repodata from current_repodata.json, will retry with next repodata source.
2020-06-04T17:01:51.7151675Z Collecting package metadata (repodata.json): ...working... done
2020-06-04T17:02:13.9808483Z Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve.
2020-06-04T17:02:44.0945912Z 
2020-06-04T17:02:44.0947250Z Building graph of deps:   0%|          | 0/3 [00:00<?, ?it/s]
2020-06-04T17:02:45.2639035Z Examining daal4py:   0%|          | 0/3 [00:00<?, ?it/s]     
2020-06-04T17:02:45.2641419Z Examining @/linux-64::__glibc==2.27=0:  33%|███▎      | 1/3 [00:01<00:02,  1.17s/it]
2020-06-04T17:02:45.2642511Z Examining @/linux-64::__glibc==2.27=0:  67%|██████▋   | 2/3 [00:01<00:00,  1.71it/s]
2020-06-04T17:02:45.8060030Z Examining python=3.8:  67%|██████▋   | 2/3 [00:01<00:00,  1.71it/s]                 
2020-06-04T17:02:45.8069291Z                                                                    
2020-06-04T17:02:45.8069895Z 
2020-06-04T17:02:45.8070659Z Determining conflicts:   0%|          | 0/3 [00:00<?, ?it/s]
2020-06-04T17:02:46.2331334Z Examining conflict for daal4py python:   0%|          | 0/3 [00:00<?, ?it/s]
2020-06-04T17:02:46.2480019Z                                                                             
2020-06-04T17:02:46.2480327Z UnsatisfiableError: The following specifications were found
2020-06-04T17:02:46.2480556Z to be incompatible with the existing python installation in your environment:
2020-06-04T17:02:46.2480693Z 
2020-06-04T17:02:46.2480886Z Specifications:
2020-06-04T17:02:46.2481004Z 
2020-06-04T17:02:46.2482075Z   - daal4py -> python[version='>=2.7,<2.8.0a0|>=3.6,<3.7.0a0|>=3.7,<3.8.0a0']
2020-06-04T17:02:46.2482226Z 
2020-06-04T17:02:46.2482420Z Your python: python=3.8
2020-06-04T17:02:46.2482525Z 
2020-06-04T17:02:46.2483077Z If python is on the left-most side of the chain, that's the version you've asked for.
2020-06-04T17:02:46.2483330Z When python appears to the right, that indicates that the thing on the left is somehow
2020-06-04T17:02:46.2483606Z not available for the python version you are constrained to. Note that conda will not
2020-06-04T17:02:46.2483868Z change your python version to a different minor version unless you explicitly specify
2020-06-04T17:02:46.2484089Z that.
2020-06-04T17:02:46.2484188Z 
2020-06-04T17:02:46.2484283Z 
2020-06-04T17:02:46.2484394Z 
2020-06-04T17:02:46.2484596Z Solving environment: ...working... 
2020-06-04T17:02:46.2484806Z Found conflicts! Looking for incompatible packages.
2020-06-04T17:02:46.2485296Z This can take several minutes.  Press CTRL-C to abort.
2020-06-04T17:02:46.2485507Z failed

enhancement

opened by pdxjohnny 8

chore(deps): update dependency jupyter-core to v5.1.2
This PR contains the following updates:

| Package | Change | Age | Adoption | Passing | Confidence | |---|---|---|---|---|---| | jupyter-core | ==5.1.1 -> ==5.1.2 | | | | |

Release Notes

jupyter/jupyter_core

v5.1.2

Compare Source

(Full Changelog)

Maintenance and upkeep improvements

Add spell check and ensure docstrings #327 (@blink1073)

Maint: Some more precise typing. #326 (@Carreau)

Documentation improvements

Add api docs #328 (@blink1073)

Contributors to this release

(GitHub contributors page for this release)

@blink1073 | @Carreau

Configuration

📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

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chore(deps): update dependency importlib-resources to v5.10.2
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python/importlib_resources

v5.10.2

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=======

#274: Prefer write_bytes to context manager as proposed in gh-100586.

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chore(deps): update dependency nbconvert to v7
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chore(deps): update dependency charset-normalizer to v3
This PR contains the following updates:

| Package | Change | Age | Adoption | Passing | Confidence | |---|---|---|---|---|---| | charset-normalizer | ==2.1.1 -> ==3.0.1 | | | | |

Release Notes

Ousret/charset_normalizer

v3.0.1

Compare Source

Fixed

Multi-bytes cutter/chunk generator did not always cut correctly (PR #233)

Changed

Speedup provided by mypy/c 0.990 on Python >= 3.7

v3.0.0

Compare Source

Added

Extend the capability of explain=True when cp_isolation contains at most two entries (min one), will log in details of the Mess-detector results

Support for alternative language frequency set in charset_normalizer.assets.FREQUENCIES

Add parameter language_threshold in from_bytes, from_path and from_fp to adjust the minimum expected coherence ratio

normalizer --version now specify if current version provide extra speedup (meaning mypyc compilation whl)

Changed

Build with static metadata using 'build' frontend

Make the language detection stricter

Optional: Module md.py can be compiled using Mypyc to provide an extra speedup up to 4x faster than v2.1

Fixed

CLI with opt --normalize fail when using full path for files

TooManyAccentuatedPlugin induce false positive on the mess detection when too few alpha character have been fed to it

Sphinx warnings when generating the documentation

Removed

Coherence detector no longer return 'Simple English' instead return 'English'

Coherence detector no longer return 'Classical Chinese' instead return 'Chinese'

Breaking: Method first() and best() from CharsetMatch

UTF-7 will no longer appear as "detected" without a recognized SIG/mark (is unreliable/conflict with ASCII)

Breaking: Class aliases CharsetDetector, CharsetDoctor, CharsetNormalizerMatch and CharsetNormalizerMatches

Breaking: Top-level function normalize

Breaking: Properties chaos_secondary_pass, coherence_non_latin and w_counter from CharsetMatch

Support for the backport unicodedata2

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chore(deps): update dependency sphinx-tabs to v3.4.1
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executablebooks/sphinx-tabs

v3.4.1

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Added

Weekly scheduled testing, to catch breaking changes in unpinned dependencies

Changed

docutils version pin to allow use of verison 0.18.x

Removed

sphinx version pinning - only the latest version of sphinx will now be fully supported, but previous versions will work if sphinx dependencies (i.e. jinja2) are managed correctly. This is inline with the approach at sphinx

tests that were specific to older versions of sphinx and pygments

jinja2 version pinning, as this is now pinned in latest version of sphinx

v3.4.0

Compare Source

Added

Testing for sphinx 5

Tesing for python 3.10

Fixed

Fixed parsing of MyST content, where first line was being stripped

Typos in documentation

Failing regression tests

Changed

Testing to use an up-to-date pytest version

Removed

Testing for python 3.6 and sphinx versions 2 and 4 (see #164). Note that the package will likely continue to work fine with these, but this won't be assured by tests

v3.3.1

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Fixed

Inserting CSS at the start of the static path list, so that it can be overwritten

Assume light theme is the default, even when browser setting prefers dark. Necessary when most sphinx themes don't set data-theme.

v3.3.0

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Added

Testing for Python 3.9 and a few pygments versions

Dark theme selectors

Changed

Bumped docutils dependency to 0.17

Remaining string formatting to use f-strings

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chore(deps): update dependency jinja2 to v3.1.2
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Releases(2021.7.1)

2021.7.1(Dec 1, 2022)
The release Intel® Extension for Scikit-learn 2021.7.1 introduces the following changes:

📚 Support Materials

[Tabular Playground Series - Sep 2022] Tuning of ElasticNet hyperparameters

Accelerated Random Forest for Rent Prediction

🚨 What's New

oneAPI interface for kNN regression

Fix for wrong column names of pandas DataFrame in sklearn.model_selection.train_test_split patched function

Source code(tar.gz)
Source code(zip)
2021.6.0(Aug 18, 2022)
The release Intel® Extension for Scikit-learn 2021.6 introduces the following changes:

📚 Support Materials

Kaggle kernels:

Fast Feature Importance using scikit-learn-intelex

[Tabular Playground Series - December 2021] Fast Feature Importance with sklearnex

[Tabular Playground Series - December 2021] SVC with sklearnex 20x speedup

[Tabular Playground Series - January 2022] Fast PyCaret with Scikit-learn-Intelex

[Tabular Playground Series - February 2022] KNN with sklearnex 13x speedup

Fast SVM for Sparse Data from NLP Problem

Introduction to scikit-learn-intelex

[Datasets] Fast Feature Importance using sklearnex

[Tabular Playground Series - March 2022] Fast workflow using scikit-learn-intelex

🛠️ Library Engineering

Reduced the size of oneDAL python run-time package by approximately 8%

Added Python 3.10 support for daal4py and Intel(R) Extension for Scikit-learn packages

🚨 What's new

Improved performance for the following Intel® Extension for Scikit-learn algorithms:

t-SNE for “Burnes-Hut” algorithm

Introduced new functionality for Intel® Extension for Scikit-learn:

Manhattan, Minkowski, Chebyshev and Cosine distances for KNeighborsClassifier and NearestNeighbors with “brute” algorithm

Fixed the following issues in Intel® Extension for Scikit-learn:

An issue with the search of common data type in pandas DataFrame

Patching overhead of finiteness checker for specific small data sizes

Incorrect values in a tree visualization with plot_tree function in RandomForestClassifier

Unexpected error for device strings in {device}:{device_index} format while using config context

Source code(tar.gz)
Source code(zip)
2021.5.0(Dec 22, 2021)
The release Intel® Extension for Scikit-learn 2021.5 introduces the following changes:

📚 Support Materials

Kaggle kernels:

[Tabular Playground Series - Sep 2021] Ridge with sklearn-intelex 2x speedup

[Tabular Playground Series - Oct 2021] Fast AutoML with Intel Extension for Scikit-learn

[Titanic – Machine Learning from Disaster] AutoML with Intel Extension for Sklearn

[Tabular Playground Series - Nov 2021] AutoML with Intel® Extension

[Tabular Playground Series - Nov 2021] Log Regression with sklearnex 17x speedup

[Tabular Playground Series - Dec 2021] SVC with sklearnex 20x speedup

[Tabular Playground Series - Dec 2021] Fast Feature Importance with sklearnex

Added demo samples of the Intel® Extension for Scikit-learn usage with the performance comparison to original Scikit-learn for ElasticNet, K-means, Lasso Regression, Linear regression, and Ridge Regression

Added demo samples of the Modin usage

🛠️ Library Engineering

Reduced the size of oneDAL library by approximately ~15%, this is a required dependency of Intel® extension for scikit learn.

🚨 New Features

Scikit-learn 1.0 support

🚀 Improved performance

[GPU] KNN algorithm prediction

[GPU] SVC and SVR algorithms training

🐛 Bug Fixes

Stabilized the results of Linear Regression in oneDAL and Intel® Extension for Scikit-learn

Fixed an issue with RPATH on MacOS

Source code(tar.gz)
Source code(zip)
2021.4.0(Oct 14, 2021)
The release Intel(R) Extension for Scikit-learn 2021.4 introduces the following changes:

📚 Support Materials

Medium blogs:

Save Time and Money with Intel Extension for Scikit-learn

Anaconda blogs:

Scikit-learn Speed-up with Intel and Anaconda

Oracle blogs:

Accelerate your model build process with the Intel® Extension for Scikit-learn

Kaggle kernels:

[Tabular Playground Series - Jun 2021] Fast LogReg with scikit-learn-intelex

[Tabular Playground Series - Jun 2021] AutoGluon with sklearnex

[Tabular Playground Series - Jul 2021] Fast RandomForest with sklearnex

[Tabular Playground Series - Jul 2021] RF with Intel Extension for Scikit-learn

[Tabular Playground Series - Jul 2021] Stacking with scikit-learn-intelex

[Tabular Playground Series - Aug 2021] NuSVR with Intel Extension for Sklearn

[Predict Future Sales] Stacking with scikit-learn-intelex

[House Prices - Advanced Regression Techniques] NuSVR sklearn-intelex 4x speedup

Added demo samples comparing the usage of Intel® Extension for Scikit-learn and the original Scikit-learn for KNN, Logistic Regression, SVM and Random Forest algorithms

🛠️ Library Engineering

Introduced new functionality for Intel® Extension for Scikit-learn*:

Enabled patching for all Scikit-learn applications at once:

You can enable global patching via command line:

python -m sklearnex.glob patch_sklearn

Or via code:

from sklearnex import patch_sklearn patch_sklearn(global_patch=True)

Read more in Intel® Extension for Scikit-learn documentation.

Added the support of Python 3.9 for both Intel® Extension for Scikit-learn and daal4py. The packages are available from PyPI and the Intel Channel on Anaconda Cloud.

🚨 New Features

Enabled the global patching of all Scikit-learn applications

Provided an integration with dpctl for heterogeneous computing (the support of dpctl.tensor.usm_ndarray for input and output)

Extended API with set_config and get_config methods. Added the support of target_offload and allow_fallback_to_host options for device offloading scenarios

Added the support of predict_proba in RandomForestClassifier estimator

[CPU] Added the support of Sigmoid kernel in SVM algorithms

[GPU] Added binary SVC support with Linear and RBF kernels

🚀 Improved performance

[CPU] SVR algorithm training

[CPU] NuSVC and NuSVR algorithms training

[CPU] RandomForestRegression and RandomForestClassifier algorithms training and prediction

[CPU] KMeans algorithm training

🐛 Bug Fixes

Fixed an incorrectly raised exception during the patching of Random Forest algorithm when the number of trees was more than 7000.

[CPU] Fixed an accuracy issue in Random Forest algorithm caused by the exclusion of constant features.

[CPU] Fixed an issue in NuSVC Multiclass.

[CPU] Fixed an issue with KMeans convergence inconsistency.

[CPU] Fixed incorrect work of train_test_split with specific subset sizes.

[GPU] Fixed incorrect bias calculation in SVM.

❗ Known Issues

[GPU] For most algorithms, performance degradations were observed when the 2021.4 version of Intel® oneAPI DPC++ Compiler was used.

[GPU] Examples are failing when run with Visual Studio Solutions on hardware that does not support double precision floating-point operations.

Source code(tar.gz)
Source code(zip)
2021.3.0(Jul 5, 2021)
The release Intel(R) Extension for Scikit-learn 2021.3 introduces the following changes:

📚 Support Materials

Medium blogs:

Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors

Leverage Intel Optimizations in Scikit-Learn (SVM Performance Training and Inference)

Optimizing CatBoost Performance

Performance Optimizations for End-to-End AI Pipelines

Kaggle kernels:

[Tabular Playground Series - Apr 2021] RF with Intel Extension for Scikit-learn

[Tabular Playground Series - Apr 2021] SVM with Intel Extension for Scikit-learn

[Tabular Playground Series - Apr 2021] SVM with Intel(R) Extension for Scikit-learn

[Tabular Playground Series - Jun 2021] AutoGluon with Intel(R) Extension for Scikit-learn

[Tabular Playground Series - Jun 2021] Fast LogReg with Intel(R) Extension for Scikit-learn

[Tabular Playground Series - Jun 2021] Fast ML stack with Intel(R) Extension for Scikit-learn

[Tabular Playground Series - Jun 2021] Fast Stacking with Intel(R) Extension for Scikit-learn

Samples that illustrate the usage of Intel Extension for Scikit-learn

🛠️ Library Engineering

Introduced optional dependencies on DPC++ runtime to Intel Extension for Scikit-learn and daal4py. To enable DPC++ backend, install dpcpp_cpp_rt package. It reduces the default package size with all dependencies from 1.2GB to 400 MB.

🚨 New Features

Introduced the support of scikit-learn 1.0 version in Intel(R) Extension for Scikit-learn. The 2021.3 release of Intel(R) Extension for Scikit-learn supports the latest scikit-learn releases: 0.22.X, 0.23.X, 0.24.X and 1.0.X.

The support of patch_sklearn for several algorithms: patch_sklearn(["SVC", "DBSCAN"])

[CPU] Acceleration of SVR estimator

[CPU] Acceleration of NuSVC and NuSVR estimators

[CPU] Polynomial kernel support in SVM algorithms

🚀 Improved performance

[CPU] SVM algorithms training and prediction

[CPU] Linear, Ridge, ElasticNet, and Lasso regressions prediction

🐛 Bug Fixes

Fixed binary incompatibility for the versions of numpy earlier than 1.19.4

Fixed an issue with a very large number of trees (> 7000) for Random Forest algorithm

Fixed patch_sklearn to patch both fit and predict methods of Logistic Regression when the algorithm is given as a single parameter to patch_sklearn

[CPU] Reduced the memory consumption of SVM prediction

[GPU] Fixed an issue with kernel compilation on the platforms without hardware FP64 support

❗ Known Issues

Intel(R) Extension for Scikit-learn package installed from PyPI repository can’t be found on Debian systems (including Google Collab). Mitigation: add “site-packages” folder into Python packages searching before importing the packages:

import sys import os import site sys.path.append(os.path.join(os.path.dirname(site.getsitepackages()[0]), "site-packages"))
Source code(tar.gz)
Source code(zip)
2021.2.3(May 27, 2021)
🚨 New Features

Added support of patching scikit-learn version 1.0. scikit-learn version 0.21. * is no longer supported

Source code(tar.gz)
Source code(zip)
2021.2.2(Mar 30, 2021)
⚡️ New package - Intel(R) Extension for Scikit-learn*

Intel(R) Extension for Scikit-learn* contains scikit-learn patching functionality originally available in daal4py package. All future updates for the patching will be available in Intel(R) Extension for Scikit-learn only. Please use the package instead of daal4py.

⚠️ Deprecations

Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package - Intel(R) Extension for Scikit-learn*. All future updates for the patching will be available in Intel(R) Extension for Scikit-learn only. Please use the package instead of daal4py for the Scikit-learn acceleration.

📚 Support Materials

Medium blogs:

Intel Gives Scikit-Learn the Performance Boost Data Scientists Need

From Hours to Minutes: 600x Faster SVM

Improve the Performance of XGBoost and LightGBM Inference

Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit

Kaggle kernels:

Accelerate sklearn algorithms using sklearnex

Fast SVC using scikit-learn-intelex for NLP

Fast SVC using scikit-learn-intelex for MNIST

Fast KNN using  scikit-learn-intelex for MNIST

Using scikit-learn-intelex for What's Cooking

🛠️ Library Engineering

Enabled new PyPI distribution channel for Intel(R) Extension for Scikit-learn and daal4py:

Four latest Python versions (3.6, 3.7, 3.8) are supported on Linux, Windows and MacOS.

Support of both CPU and GPU is included in the package.

You can download daal4py using the following command: pip install daal4py

You can download Intel(R) Extension for Scikit-learn using the following command: pip install scikit-learn-intelex

🚨 New Features

Patches for four latest scikit-learn releases: 0.21.X, 0.22.X, 0.23.X and 0.24.X

[CPU] Acceleration of roc_auc_score function

[CPU] Bit-to-bit results reproducibility for: LinearRegression, Ridge, SVC, KMeans, PCA, Lasso, ElasticNet, tSNE, KNeighborsClassifier, KNeighborsRegressor, NearestNeighbors, RandomForestClassifier, RandomForestRegressor

🚀 Improved performance

[CPU] RandomForestClassifier and RandomForestRegressor scikit-learn estimators: training and prediction

[CPU] Principal Component Analysis (PCA) scikit-learn estimator: training

[CPU] Support Vector Classification (SVC) scikit-learn estimators: training and prediction

[CPU] Support Vector Classification (SVC) scikit-learn estimator with the probability==True parameter: training and prediction

🐛 Bug Fixes

[CPU] Improved accuracy of RandomForestClassifier and RandomForestRegressor scikit-learn estimators

[CPU] Fixed patching issues with pairwise_distances

[CPU] Fixed the behavior of the patch_sklearn and unpatch_sklearn functions

[CPU] Fixed unexpected behavior that made accelerated functionality unavailable through scikit-learn patching if the input was not of float32 or float64 data types. Scikit-learn patching now works with all numpy data types.

[CPU] Fixed a memory leak that appeared when DataFrame from pandas was used as an input type

[CPU] Fixed performance issue for interoperability with Modin

Source code(tar.gz)
Source code(zip)
2020.3.1(Dec 25, 2020)
What's New

Added support of patching scikit-learn version 0.24.

Source code(tar.gz)
Source code(zip)
2021.1(Dec 14, 2020)
What's New

Introduced new daal4py functionality:

GPU:

Batch algorithms: K-means, Covariance, PCA, Logistic Regression, Linear Regression, Random Forest Classification and Regression, Gradient Boosting Classification and Regression, kNN, SVM, DBSCAN and Low-order moments

Online algorithms: Covariance, PCA, Linear Regression and Low-order moments

Improved daal4py performance for the following algorithms:

CPU:

Logistic Regression training and prediction

k-Nearest Neighbors prediction with Brute Force method

Logistic Loss and Cross Entropy objective functions

Introduced new functionality for scikit-learn patching through daal4py:

CPU:

Acceleration of NearestNeighbors and KNeighborsRegressor scikit-learn estimators with Brute Force and K-D tree methods

Acceleration of TSNE scikit-learn estimator

GPU:

Intel GPU support in scikit-learn for DBSCAN, K-means, Linear and Logistic Regression

Improved performance of the following scikit-learn estimators via scikit-learn patching:

CPU:

LogisticRegression fit, predict and predict_proba methods

KNeighborsClassifier predict, predict_proba and kneighbors methods with “brute” method

Known Issues

train_test_split in daal4py patches for Scikit-learn can produce incorrect shuffling on Windows*

Installation

To install this package with conda run the following:

conda install -c intel daal4py
Source code(tar.gz)
Source code(zip)
2020.3(Nov 6, 2020)
What's New in Intel® daal4py 2020 Update 3:

Introduced new daal4py functionality:

Conversion of trained XGBoost* and LightGBM* models into a daal4py Gradient Boosted Trees model for fast prediction

Support of Modin* DataFrame as an input

Brute Force method for k-Nearest Neighbors classification algorithm, which for datasets with more than 13 features demonstrates a better performance than the existing K-D tree method

k-Nearest Neighbors search for K-D tree and Brute Force methods with computation of distances to nearest neighbors and their indices

Extended existing daal4py functionality:

Voting methods for prediction in k-Nearest Neighbors classification and search: based on inverse-distance and uniform weighting

New parameters in Decision Forest classification and regression: minObservationsInSplitNode, minWeightFractionInLeafNode, minImpurityDecreaseInSplitNode, maxLeafNodes with best-first strategy and sample weights

Support of Support Vector Machine (SVM) decision function for Multi-class Classifier

Improved daal4py performance for the following algorithms:

SVM training and prediction

Decision Forest classification training

RBF and Linear kernel functions

Introduced new functionality for scikit-learn patching through daal4py:

Acceleration of KNeighborsClassifier scikit-learn estimator with Brute Force and K-D tree methods

Acceleration of RandomForestClassifier and RandomForestRegressor scikit-learn estimators

Sparse input support for KMeans and Support Vector Classification (SVC) scikit-learn estimators

Prediction of probabilities for SVC scikit-learn estimator

Support of ‘normalize’ parameter for Lasso and ElasticNet scikit-learn estimators

Improved performance of the following functionality for scikit-learn patching through daal4py:

train_test_split()

Support Vector Classification (SVC) fit and prediction

To install this package with conda run the following: conda install -c intel daal4py
Source code(tar.gz)
Source code(zip)
2020.2(Aug 17, 2020)
Introduced new functionality:

Thunder method for Support Vector Machine (SVM) training algorithm, which demonstrates better training time than the existing sequential minimal optimization method

Extended existing functionality:

Training with the number of features greater than the number of observations for Linear Regression, Ridge Regression, and Principal Component Analysis

New sample_weights parameter for SVM algorithm

New parameter in K-Means algorithm, resultsToEvaluate, which controls computation of centroids, assignments, and exact objective function

Improved performance for the following:

Support Vector Machine training and prediction, Elastic Net and LASSO training, Principal Component Analysis training and transform, K-D tree based k-Nearest Neighbors prediction

K-Means algorithm in batch computation mode

RBF kernel function

Deprecated 32-bit support:

2020 product line will be the last one to support 32-bit

Introduced improvements to daal4py library:

Performance optimizations for pandas input format

Scikit-learn compatible API for AdaBoost classifier, Decision Tree classifier, and Gradient Boosted Trees classifier and regressor

Improved performance of the following Intel Scikit-learn algorithms and functions:

fit and prediction in K-Means and Support Vector Classification (SVC), fit in Elastic Net and LASSO, fit and transform in PCA

Support Vector Classification (SVC) with non-default weights of samples and classes

train_test_split() and assert_all_finite()

To install this package with conda run the following: conda install -c intel daal4py
Source code(tar.gz)
Source code(zip)
2020.1(Aug 17, 2020)
Introduced new functionality:

Elastic Net algorithm with L1 and L2 regularization in batch computation mode. The algorithm supports various optimization solvers that handle non-smooth functions.

Probabilistic classification for Decision Forest Classification algorithm with a choice voting method to calculate probabilities.

Extended existing functionality:

Performance optimizations for distributed Spark samples, K-means algorithm for some input dimensions, Gradient Boosted Trees training stage for large datasets on multi-core platforms and Decision Forest prediction stage for datasets with a small number of observations on processors that support Intel® Advanced Vector Extensions 2 (Intel® AVX2) and Intel® Advanced Vector Extensions 512 (Intel® AVX-512)

Performance optimizations across algorithms that use SOA (Structure Of Arrays) NumericTable as an input on processors that support Intel® Advanced Vector Extensions 512 (Intel® AVX-512)

Source code(tar.gz)
Source code(zip)
2020.0(Dec 19, 2019)

Added support for Brownboost, Logistboost as well as Stump regression and Stump classification algorithms to daal4py. Added support for Adaboost classification algorithm, including support for method="SAMME" or "SAMMER" for multi-class data. "Variable Importance" feature has been added in Gradient Boosting Trees. Ability to compute class prediction probabilities has been added to appropriate classifiers, including logistic regression, tree-based classifiers, etc.
Source code(tar.gz)
Source code(zip)
2019.5(Oct 5, 2019)

Single node support for DBSCAN, LASSO, Coordinate Descent (CD) solver algorithms Distributed model support for SVD, QR, K-means init++ and parallel++ algorithms
Source code(tar.gz)
Source code(zip)
2019.3(Apr 2, 2019)

Product release with Intel(R) Parallel Studio 2019 Update 3
Source code(tar.gz)
Source code(zip)

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Related tags

Overview

Intel(R) Extension for Scikit-learn*

How it works?

Supported Algorithms

🚀 Acceleration

🛠 Installation

🔗 Important Links

👀 Follow us on Medium

❔ FAQ

❓ Are all algorithms affected by patching?

❓ What happens if I use parameters not supported by the extension?

❓ What happens if I run algorithms not supported by the extension?

❓ Can I see which implementation of the algorithm is currently used?

❓ How much faster scikit-learn is after the patching?

❓ What if the patching does not cover my scenario?

💬 Support

oneAPI

daalpy and oneDAL

Comments

Description

Changes proposed in this pull request:

Description

Release Notes

Maintenance and upkeep improvements

Documentation improvements

Contributors to this release

Configuration

Release Notes

Configuration

Configuration

Release Notes

Fixed

Changed

Added

Changed

Fixed

Removed

Configuration

Release Notes

Added

Changed

Removed

Added

Fixed

Changed

Removed

Fixed

Added

Changed

Configuration

Configuration

Releases(2021.7.1)

2021.7.1(Dec 1, 2022)

📚 Support Materials

🚨 What's New

2021.6.0(Aug 18, 2022)

📚 Support Materials

🛠️ Library Engineering

🚨 What's new

2021.5.0(Dec 22, 2021)

📚 Support Materials

🛠️ Library Engineering

🚨 New Features

🚀 ​Improved performance

🐛 Bug Fixes

2021.4.0(Oct 14, 2021)

📚 Support Materials

🛠️ Library Engineering

🚨 New Features

🚀 ​Improved performance

🐛 Bug Fixes

❗ Known Issues

2021.3.0(Jul 5, 2021)

📚 Support Materials

🛠️ Library Engineering

🚨 New Features

🚀 ​Improved performance

🐛 Bug Fixes

🚀 Improved performance

🚀 Improved performance

🚀 Improved performance

🚀 Improved performance