Quick Start Guide

Device Drivers

To start programming data parallel devices beyond CPU, you will need an appropriate hardware. The Data Parallel Extension for NumPy* works fine on Intel © laptops with integrated graphics. In majority of cases, your Windows*-based laptop already has all necessary device drivers installed. But if you want the most up-to-date driver, you can always update it to the latest one. Follow device driver installation instructions to complete the step.

Python Interpreter

You will need Python 3.8, 3.9, or 3.10 installed on your system. If you do not have one yet the easiest way to do that is to install Intel Distribution for Python*. It installs all essential Python numerical and machine learning packages optimized for the Intel hardware, including Data Parallel Extension for NumPy*. If you have Python installation from another vendor, it is fine too. All you need is to install Data Parallel Extension for NumPy* manually as shown in the next installation section.

Installation

Install Package from Anaconda

It is recommended to use conda packages from the anaconda.org/intel channel. You will need one of the commands below:

• Conda: conda install dpnp -c intel -c conda-forge

• Pip: pip install -i https://pypi.anaconda.org/intel/simple dpnp

These commands install dpnp package along with its dependencies, including dpctl package with Data Parallel Control Library and all required compiler runtimes and OneMKL.

Note

Before installing with conda or pip it is strongly advised to update conda and pip to latest versions

Build and Install Conda Package

Alternatively you can create and activate a local conda build environment:

conda create -n build-env conda-build
conda activate build-env

And to build dpnp package from the sources:

conda build conda-recipe -c intel -c conda-forge

Finanly, to install the result package:

conda install dpnp -c local

Build and Install with scikit-build

Another way to build and install dpnp package from the source is to use Python setuptools and scikit-build. You will need to create a local conda build environment by command below depending on hosting OS.

On Linux:

conda create -n build-env dpctl cython dpcpp_linux-64 mkl-devel-dpcpp tbb-devel onedpl-devel cmake scikit-build ninja pytest -c intel -c conda-forge
conda activate build-env

On Windows:

conda create -n build-env dpctl cython dpcpp_win-64 mkl-devel-dpcpp tbb-devel onedpl-devel cmake scikit-build ninja pytest -c intel -c conda-forge
conda activate build-env

To build and install the package on Linux OS, run:

python setup.py install -- -G Ninja -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icpx

To build and install the package on Windows OS, run:

python setup.py install -- -G Ninja -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icx

Alternatively, to develop on Linux OS, you can use the driver script:

python scripts/build_locally.py

Testing

If you want to execute the scope of Python test suites which are available by the source, you will need to run a command as below:

pytest -s tests

Examples

The examples below demonstrates a simple usage of the Data Parallel Extension for NumPy*

How to create an array and to sum the elements

import dpnp as np

x = np.asarray([1, 2, 3])
print("Array x allocated on the device:", x.device)

y = np.sum(x)

print("Result y is located on the device:", y.device)  # The same device as x
print("Shape of y is:", y.shape)  # 0-dimensional array
print("y =", y)  # Expect 6

How to create an array on the specific device type and how the next computations follow it

import dpnp as np

x = np.empty(3)
try:
    x = np.asarray([1, 2, 3], device="gpu")
except Exception:
    print("GPU device is not available")

print("Array x allocated on the device:", x.device)

y = np.sum(x)

print("Result y is located on the device:", y.device)  # The same device as x
print("Shape of y is:", y.shape)  # 0-dimensional array
print("y=", y)  # Expect 6

More examples on how to use dpnp can be found in dpnp/examples.