1. Install Miniforge.
2. 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>

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.

1. Install Miniforge.
2. 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>

    

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.

Create and activate a virtual environment, replacing with your preferred name for the environment:

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

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.

1. To add APT repository access, install the prerequisites:

   
   sudo apt update
   sudo apt install -y gpg-agent wget

2. 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

3. 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

4. Update the packages list and repository index.

   
   sudo apt update

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.

Create and activate a virtual environment, replacing with your preferred name for the environment:

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

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.

Create the DNF repository file in the /temp directory as a normal user.

tee > /tmp/oneAPI.repo << EOF
[oneAPI]
name=Intel® oneAPI repository
baseurl=https://yum.repos.intel.com/oneapi
enabled=1
gpgcheck=1
repo_gpgcheck=1
gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
EOF

Move the newly created oneAPI.repo file to the YUM configuration directory.

sudo mv /tmp/oneAPI.repo /etc/yum.repos.d