Intel® Distribution for Python*
Achieve near-native code performance with this set of essential packages optimized for high-performance numerical and scientific computing.
High-Performance Python
The Intel® Distribution for Python* provides:
- Scalable performance using all available CPU cores on laptops, desktops, and powerful servers
- Support for the latest CPU instructions
- Near-native performance through acceleration of core numerical and machine learning packages with libraries like the Intel® oneAPI Math Kernel Library (oneMKL) and Intel® oneAPI Data Analytics Library
- Productivity tools for compiling Python code into optimized instructions
- Essential Python bindings for easing integration of Intel native tools with your Python project
Develop for Accelerated Compute
Data Parallel Extensions for Python*
Enable standards-based accelerated computing on CPUs and GPUs without using low-level proprietary programming APIs. Optimize performance and portability by extending the familiar CPU programming model to a GPU with a compute follows data model.
Data Parallel Control Library (dpctl)
This library provides utilities for device selection, allocation of data on devices, tensor data structure, the Python* Array API Standard implementation, and support for the creation of user-defined data-parallel extensions.
Data Parallel Extension for NumPy*
This is a drop-in replacement for a subset of NumPy APIs that enable running on Intel CPU and GPUs.
Data Parallel Extension for Numba*
This extension enables you to program GPUs the same way CPUs are programmed with Numba.
Who Needs This Product
AI & Machine Learning Developers
- Build high-performance, end-to-end AI and machine learning pipelines on Intel platforms with the Intel Distribution for Python and the AI Tools. For more information, see AI & Machine Learning.
Analysts, Researchers, and Scientific Computing Developers
- Gain easy access to all CPU cores and GPU accelerated performance with optimizations for NumPy, SciPy, and Numba that scale from laptops up to powerful servers.
High-Performance Computing (HPC) Developers
- Tune for highest efficiency at scale using advanced tools for multithreading and multiprocessing with OpenMP*, tbb4py, smp, and mpi4py.
- Create your own Python libraries and applications that maximize performance using oneMKL, Intel® oneAPI DPC++/C++ Compiler, and Intel® Fortran Compiler runtimes.
Beginners and Students
- Learn how to productively program in Python using standards-based libraries for the highest performance.
Download the Stand-Alone Version
A stand-alone version of Intel® Distribution for Python* is available.
Develop in the Intel® Tiber™ Developer Cloud
Build and optimize oneAPI multiarchitecture applications using the latest Intel-optimized oneAPI and AI tools, and test your workloads across Intel® CPUs and GPUs. No hardware installations, software downloads, or configuration necessary.
What's Included
Package and Environment Managers
Get essential tools for installing, updating, and deleting Python packages and environments.
Data Processing and Modeling Packages
Use these packages in numeric and data science workflows for data collection, ingestion, preprocessing, normalization, transformation, aggregation, and analysis.
Machine Learning Packages
Foundational packages that allow a machine to automatically learn from data without programming it explicitly.
Python Interpreter and Compilers
Use these tools for a versatile interactive experience and to achieve scaled performance.
Advanced Programming Packages
Essential packages that enable fine-grained controls for data management, devices management, concurrency, and parallelism.
Development Packages and Runtimes
Use these runtime packages for enabling performance across Intel-optimized Python packages.
Priority Support
Available through the Intel® oneAPI Base Toolkit.
Benchmarks
Intel-Optimized NumPy & SciPy Linear Algebra Performance
Intel-Optimized NumPy Fast Fourier Transform Performance
Intel-Optimized SciPy Fast Fourier Transform Performance (out-of-place memory placement performance)
Intel-Optimized SciPy Fast Fourier Transform Performance (in-place memory placement performance)
Intel Distribution for Python Oversubscription Performance
Intel Distribution for Python Oversubscription Performance (successful unbalanced workload performance)
Documentation & Code Samples
- Get Started
- Release Notes
- Known Issues
- End User License Agreement (EULA)
- Data Parallel Extensions for Python*
- Data Parallel Extension for NumPy*
- Data Parallel Extension for Numba*: Documentation | Get Started Guide
- dpctl: Documentation | Quick Start Guide
- Examples of Data Parallel Extension for Python
Use these Hello World examples to get started with Data Parallel Extensions for Python. - Intel Distribution for Python: NumPy vs Data Parallel Extension for Numba (numba-dpex)
Learn how to use and optimize a k-nearest neighbor (KNN) model by numba-dpex operations without sacrificing accuracy. Run the KNN algorithm using three different libraries: NumPy, Numba, and numba-dpex. - Pairwise Distance: Open Source XPU Programming with Data Parallel Extension for Python*
Learn how Data Parallel Extension for NumPy and Data Parallel Extension for Numba can be used to compute pairwise distance in NumPy code efficiently and scale performance through open source heterogeneous computing and DPC++ (the oneAPI implmentation of SYCL*) compilation.
Training
Get Started
- What is Intel Distribution for Python? An Introduction
- A Guide to Multithreading in Python
- How to Create a Deep Learning Environment for Computer Vision
Data Parallel Extensions for Python
System Requirements
CPUs:
- Intel® Core™ processor family
- Intel® Xeon® processor family
GPUs:
- Intel® UHD Graphics for 11th generation Intel processors or newer
- Intel® Iris® Xᵉ graphics
- Intel® Arc™ graphics
- Intel® Data Center GPU Flex Series
- Intel® Data Center GPU Max Series
Operating systems:
- Linux*
- Windows® 10
- Windows* 11
Language:
- Python 3.9, Python 3.10
Package management:
- conda*
- pip
Compatible with:
- Microsoft Visual Studio*
- PyCharm*
For more information, see the system requirements.
Get Help
Your success is our success. Access these support resources when you need assistance.
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