achieve this need. Let's start with the metapackage blas=1.0=mkl: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/ e699b12/recipe/meta.yaml#L108-L112 Note that mkl is a string of blas. That someone uses the mkl-devel pack- age as a build-time dependency: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/e699b12/recipe/ meta.yaml#L124 By the same token, here’s the metapackage for NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s CUDA) which speed up performance without code changes. Read more about how conda

achieve this need. Let's start with the metapackage blas=1.0=mkl: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/ e699b12/recipe/meta.yaml#L108-L112 Note that mkl is a string of blas. That someone uses the mkl-devel pack- age as a build-time dependency: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/e699b12/recipe/ meta.yaml#L124 By the same token, here’s the metapackage for NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s CUDA) which speed up performance without code changes. Read more about how conda

notes about Anaconda's Terms of Service. Windows x86_64 macOS arm64 (Apple Silicon) macOS x86_64 (Intel) Linux x86_64 (amd64) Linux aarch64 (arm64) Or with Homebrew: brew install miniconda Miniforge for use with the conda-forge channel. Windows x86_64 macOS arm64 (Apple Silicon) macOS x86_64 (Intel) Linux x86_64 (amd64) Linux aarch64 (arm64) Or with Homebrew: brew install miniforge 3 conda a user running macOS on the Apple Silicon platform might want to create a python environment for Intel processors and emulate the executables with Rosetta. The command would be: conda create --platform

notes about Anaconda's Terms of Service. Windows x86_64 macOS arm64 (Apple Silicon) macOS x86_64 (Intel) Linux x86_64 (amd64) Linux aarch64 (arm64) Or with Homebrew: brew install miniconda Miniforge for use with the conda-forge channel. Windows x86_64 macOS arm64 (Apple Silicon) macOS x86_64 (Intel) Linux x86_64 (amd64) Linux aarch64 (arm64) Or with Homebrew: brew install miniforge 3 conda a user running macOS on the Apple Silicon platform might want to create a python environment for Intel processors and emulate the executables with Rosetta. The command would be: conda create --platform

notes about Anaconda's Terms of Service. Windows x86_64 macOS arm64 (Apple Silicon) macOS x86_64 (Intel) Linux x86_64 (amd64) Linux aarch64 (arm64) Or with Homebrew: brew install miniconda Miniforge for use with the conda-forge channel. Windows x86_64 macOS arm64 (Apple Silicon) macOS x86_64 (Intel) Linux x86_64 (amd64) Linux aarch64 (arm64) Or with Homebrew: brew install miniforge 3 conda a user running macOS on the Apple Silicon platform might want to create a python environment for Intel processors and emulate the executables with Rosetta. The command would be: conda create --platform

Windows x86 64-bit macOS Miniconda installer for: macOS with Apple Silicon 64-bit macOS with Intel CPU 64-bit Linux Miniconda installer for: Linux x86 64-bit Homebrew Run the following Homebrew achieve this need. Let's start with the metapackage blas=1.0=mkl: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/ e699b12/recipe/meta.yaml#L108-L112 Note that mkl is a string of blas. That someone uses the mkl-devel pack- age as a build-time dependency: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/e699b12/recipe/ meta.yaml#L124 By the same token, here’s the metapackage for

achieve this need. Let's start with the metapackage blas=1.0=mkl: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/ e699b12/recipe/meta.yaml#L108-L112 Note that mkl is a string of blas. That someone uses the mkl-devel pack- age as a build-time dependency: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/e699b12/recipe/ meta.yaml#L124 By the same token, here’s the metapackage for NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s CUDA) which speed up performance without code changes. Read more about how conda

achieve this need. Let's start with the metapackage blas=1.0=mkl: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/ e699b12/recipe/meta.yaml#L108-L112 Note that mkl is a string of blas. That someone uses the mkl-devel pack- age as a build-time dependency: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/e699b12/recipe/ meta.yaml#L124 By the same token, here’s the metapackage for NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s CUDA) which speed up performance without code changes. Read more about how conda

achieve this need. Let's start with the metapackage blas=1.0=mkl: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/ e699b12/recipe/meta.yaml#L108-L112 Note that mkl is a string of blas. That someone uses the mkl-devel pack- age as a build-time dependency: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/e699b12/recipe/ meta.yaml#L124 By the same token, here’s the metapackage for NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s CUDA) which speed up performance without code changes. Read more about how conda

achieve this need. Let's start with the metapackage blas=1.0=mkl: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/ e699b12/recipe/meta.yaml#L108-L112 Note that mkl is a string of blas. That someone uses the mkl-devel pack- age as a build-time dependency: https://github.com/AnacondaRecipes/intel_repack-feedstock/blob/e699b12/recipe/ meta.yaml#L124 By the same token, here’s the metapackage for NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s CUDA) which speed up performance without code changes. Read more about how conda