Get Intel® oneAPI Math Kernel Library

Intel® oneAPI Math Kernel Library: Online: Offline: conda: NuGet: pip: Online: Offline: APT: Cloudera: conda*: DNF: pip: Spack: YUM: Zypper

Accelerate math processing routines, increase application performance, and reduce development time.

For the most current functional and security features, update to the latest version as it becomes available.

Set Up Your Environment

Install Miniforge.
Create and activate a new conda environment, replacing <your-env-name> with your preferred name for the environment:
```
conda create -n <your-env-name>
conda activate <your-env-name>
```

Install with conda


conda install -c https://software.repos.intel.com/python/conda/ -c conda-forge <package-name>

The following packages are available for installation:

For running applications that require oneMKL:
- mkl includes runtime only
For developing or compiling applications:
- mkl-devel includes libraries, headers, and tools for dynamic linking
- If needed, add mkl-include if your development workflow manages the libraries separately
For static linking:
- mkl-static to statically link oneMKL, creating self-contained binaries
For running Data Parallel C++ (DPC++) applications that require oneMKL:
- mkl-dpcpp provides the runtime support for oneMKL with DPC++
For developing and compiling DPC++ applications:
- mkl-devel-dpcpp includes the development tools and headers for oneMKL with DPC++
For using domain-specific libraries in SYCL-based applications:
- onemkl-sycl-blas provides Basic Linear Algebra Subprograms (BLAS) routines
- onemkl-sycl-lapack provies Linear Algebra Package (LAPACK) routines for more advanced linear algebra computations
- onemkl-sycl-dft provides Discrete Fourier Transform functionality
- onemkl-sycl-sparse provides sparse matrix operations
- onemkl-sycl-vm provides vector math (VM) operations, which optimize common mathematical functions applied to vectors
- onemkl-sycl-datafitting provides functionality for data fitting operations

For the next steps, see the Get Started Guide.

Set Up Your Environment

Install Miniforge.
Create and activate a new conda environment, replacing <your-env-name> with your preferred name for the environment:
```
conda create -n <your-env-name>
conda activate <your-env-name>
```

Install with conda


conda install -c https://software.repos.intel.com/python/conda/ -c conda-forge <package-name>

The following packages are available for installation:

For running applications that require oneMKL:
- mkl includes runtime only
For developing or compiling applications:
- mkl-devel includes libraries, headers, and tools for dynamic linking
- if needed, add mkl-include if your development workflow manages the libraries separately
For static linking:
- mkl-static to statically link oneMKL, creating self-contained binaries
For running Data Parallel C++ (DPC++) applications that require oneMKL:
- mkl-dpcpp provides the runtime support for oneMKL with DPC++
For developing and compiling DPC++ applications:
- mkl-devel-dpcpp includes the development tools and headers for oneMKL with DPC++
For using domain-specific libraries in SYCL-based applications:
- onemkl-sycl-blas provides Basic Linear Algebra Subprograms (BLAS) routines
- onemkl-sycl-lapack provies Linear Algebra Package (LAPACK) routines for more advanced linear algebra computations
- onemkl-sycl-dft provides Discrete Fourier Transform functionality
- onemkl-sycl-sparse provides sparse matrix operations
- onemkl-sycl-vm provides vector math (VM) operations, which optimize common mathematical functions applied to vectors
- onemkl-sycl-datafitting provides functionality for data fitting operations

For the next steps, see the Get Started Guide.

Install with NuGet

You can install NuGet packages for oneMKL via Microsoft* Visual Studio or command line interface. For more information, refer to the NuGet documentation.

The following packages are available for installation

Development packages contain the development libraries and headers, enabling you to compile and link your applications with the oneMKL libraries: intelmkl.devel.win-x64 intelmkl.devel.win-x86
Static packages contain the static libraries, allowing you to link oneMKL libraries statically into your application. This means your application will not depend on external dynamic libraries at runtime, simplifying distribution: intelmkl.static.win-x64 intelmkl.static.win-x86

Additionally, oneMKL cluster components development and static packages are available: intelmkl.devel.cluster.win-x64 intelmkl.static.cluster.win-x64

For the next steps, see the Get Started Guide.

Set Up Your Environment

Create and activate a virtual environment, replacing <your-env-name> with your preferred name for the environment:

python3.10 -m venv <your-env-name>
source <your-env-name>/bin/activate

Install with pip


pip install <package-name>

The following packages are available for installation:

For running applications that require oneMKL:
- mkl includes runtime only
For developing or compiling applications:
- mkl-devel includes libraries, headers, and tools for dynamic linking
- If needed, add mkl-include if your development workflow manages the libraries separately
For static linking:
- mkl-static to statically link oneMKL, creating self-contained binaries
For running Data Parallel C++ (DPC++) applications that require oneMKL:
- mkl-dpcpp provides the runtime support for oneMKL with DPC++
For developing and compiling DPC++ applications:
- mkl-devel-dpcpp includes the development tools and headers for oneMKL with DPC++
For using domain-specific libraries in SYCL-based applications:
- onemkl-sycl-blas provides Basic Linear Algebra Subprograms (BLAS) routines
- onemkl-sycl-lapack provies Linear Algebra Package (LAPACK) routines for more advanced linear algebra computations
- onemkl-sycl-dft provides Discrete Fourier Transform functionality
- onemkl-sycl-sparse provides sparse matrix operations
- onemkl-sycl-vm provides vector math (VM) operations, which optimize common mathematical functions applied to vectors
- onemkl-sycl-datafitting provides functionality for data fitting operations

For the next steps, see the Get Started Guide.

Prerequisites for First-Time Users

To add APT repository access, install the prerequisites:
```
sudo apt update
sudo apt install -y gpg-agent wget
```

Set up the repository. To do this, download the key to the system keyring:


wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | sudo tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null

Add the signed entry to APT sources and configure the APT client to use the Intel repository:


echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list

Update the packages list and repository index.
```
sudo apt update
```

Install with APT

For running applications that require oneMKL:


sudo apt install intel-oneapi-mkl

For developing and compiling oneMKL applications:


sudo apt install intel-oneapi-mkl-devel

For the next steps, see the Get Started Guide.

Install with Cloudera

In the Cloudera Manager Admin Console, access the Parcels page by clicking the Parcels indicator in the left navigation bar.
At the Parcels page, click the Parcel Repositories & Network Settings button.
In the Remote Parcel Repository URLs list, click the plus symbol to open an additional row. Enter the path to oneMKL Parcel repository: http://parcels.repos.intel.com/mkl/latest. Click the Save & Verify configuration button.
Click the Check for New Parcels button. In the Location selector, click Available Remotely. The latest oneMKL parcel should be available for download.
Click the Download button for the oneMKL parcel. By downloading the package, you agree with the terms and conditions stated in the End-User License Agreement (EULA).
When download is completed, click the Distribute button to distribute the parcel on all cluster nodes.
When distribution is completed, click the Activate button to activate the parcel on all cluster nodes.

For additional information about Clouder parcels, refer to Parcels documentation.

For the next steps, see the Get Started Guide.

Set Up Your Environment

Create and activate a virtual environment, replacing <your-env-name> with your preferred name for the environment:

python3.10 -m venv <your-env-name>
source <your-env-name>/bin/activate

Install with pip


pip install <package-name>

The following packages are available for installation:

For running applications that require oneMKL:
- mkl includes runtime only
For developing or compiling applications:
- mkl-devel includes libraries, headers, and tools for dynamic linking
- If needed, add mkl-include if your development workflow manages the libraries separately
For static linking:
- mkl-static to statically link oneMKL, creating self-contained binaries
For running Data Parallel C++ (DPC++) applications that require oneMKL:
- mkl-dpcpp provides the runtime support for oneMKL with DPC++
For developing and compiling DPC++ applications:
- mkl-devel-dpcpp includes the development tools and headers for oneMKL with DPC++
For using domain-specific libraries in SYCL-based applications:
- onemkl-sycl-blas provides Basic Linear Algebra Subprograms (BLAS) routines
- onemkl-sycl-lapack provies Linear Algebra Package (LAPACK) routines for more advanced linear algebra computations
- onemkl-sycl-dft provides Discrete Fourier Transform functionality
- onemkl-sycl-sparse provides sparse matrix operations
- onemkl-sycl-vm provides vector math (VM) operations, which optimize common mathematical functions applied to vectors
- onemkl-sycl-datafitting provides functionality for data fitting operations

For the next steps, see the Get Started Guide.

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Command Line Download

Command Line Installation Parameters


wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/dc93af13-2b3f-40c3-a41b-2bc05a707a80/intel-onemkl-2025.1.0.803_offline.sh

sudo sh ./intel-onemkl-2025.1.0.803_offline.sh

Installation Instructions

Step 1: From the console, locate the downloaded install file.

Step 2: Use $ sudo sh ./<installer>.sh to launch the GUI Installer as the root.

Optionally, use $ sh ./<installer>.sh to launch the GUI Installer as the current user.

Step 3: Follow the instructions in the installer.

Step 4: Explore the Get Started Guide.

Additional Resources

System Requirements
Complete Installation Guide
Release Notes
Intel Simplified Software License

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Get Intel® oneAPI Math Kernel Library (oneMKL)

Intel® oneAPI Math Kernel Library: Online: Offline: conda: NuGet: pip: Online: Offline: APT: Cloudera: conda*: DNF: pip: Spack: YUM: Zypper

Install with Spack

Install with YUM

Set Up Your Environment

Install with conda

Set Up Your Environment

Install with conda

Install with NuGet

Set Up Your Environment

Install with pip

Prerequisites for First-Time Users

Install with APT

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Get Intel® oneAPI Math Kernel Library (oneMKL)

Intel® oneAPI Math Kernel Library: Online: Offline: conda*: NuGet: pip: Online: Offline: APT: Cloudera*: conda*: DNF: pip: Spack: YUM: Zypper

Install with Spack

Install with YUM

Install with NuGet

Install with Cloudera

Install with DNF

Install with Zypper

Download

Thank You

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Installation Instructions

Command Line Download

Command Line Download

Installation Instructions

Prerequisites for First-Time Users

Prerequisites for First-Time Users

Additional Resources

Intel® oneAPI Math Kernel Library: Online: Offline: conda: NuGet: pip: Online: Offline: APT: Cloudera: conda*: DNF: pip: Spack: YUM: Zypper