bppoy.blogg.se - Ubuntu vs mac for data science

#Ubuntu vs mac for data science software#

The toolkit also includes stock scikit-learn to provide a comprehensive Python environment installed with all required packages. This extension package dynamically patches scikit-learn estimators to use Intel® oneAPI Data Analytics Library (oneDAL) as the underlying solver, while achieving the speed up for your machine learning algorithms. Seamlessly speed up your scikit-learn applications on Intel® CPUs and GPUs across single nodes and multi-nodes. Provide a unified, low-precision inference interface across multiple deep learning frameworks optimized by Intel with this open-source Python library. This package provides the binary version of the latest PyTorch release for CPUs, and further adds extensions and bindings from Intel with oneCCL for efficient distributed training.Īccess pretrained models, sample scripts, best practices, and step-by-step tutorials for many popular open-source, machine learning models optimized by Intel to run on Intel Xeon Scalable processors. In collaboration with Facebook*, this popular deep learning framework is now directly combined with many optimizations from Intel to provide superior performance on Intel architecture.

This package provides the latest TensorFlow binary version compiled with CPU-enabled settings ( -config=mkl). In collaboration with Google*, TensorFlow has been directly optimized for Intel architecture using the primitives of oneDNN to maximize performance.

#Ubuntu vs mac for data science software#

Gain direct access to analytics and AI optimizations from Intel to ensure that your software works together seamlessly.

Achieve drop-in acceleration for data preprocessing and machine learning workflows with compute-intensive Python* packages, Modin*, scikit-learn*, and XGBoost, optimized for Intel.

Deliver high-performance, deep learning training on Intel® XPUs and integrate fast inference into your AI development workflow with Intel®-optimized, deep learning frameworks for TensorFlow* and PyTorch*, pretrained models, and low-precision tools.

The AI Kit gives data scientists, AI developers, and researchers familiar Python* tools and frameworks to accelerate end-to-end data science and analytics pipelines on Intel® architecture. The components are built using oneAPI libraries for low-level compute optimizations. This toolkit maximizes performance from preprocessing through machine learning, and provides interoperability for efficient model development.