WARNING: the following PyPI package names are provided for your convenience but they point to packages that are not provided nor endorsed by the Apache Software Foundation. As such, they might contain software components with more restrictive licenses than the Apache License and you'll need to decide whether they are appropriate for your usage. The packages linked here contain GPL GCC Runtime Library components. Like all Apache Releases, the official Apache MXNet releases consist of source code only and are found at the Download page.

Run the following command:

Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled in pip packages by default.

oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. The library is optimized for Intel Architecture Processors, Intel Processor Graphics and Xe architecture-based Graphics. Support for other architectures such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is experimental.

oneDNN is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs, more details can be found here.

You can find performance numbers in the MXNet tuning guide.

To install native MXNet without oneDNN, run the following command:

{% highlight bash %} pip install mxnet-native==1.8.0.post0 {% endhighlight %}

Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled in pip packages by default.

oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. The library is optimized for Intel Architecture Processors, Intel Processor Graphics and Xe architecture-based Graphics. Support for other architectures such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is experimental.

oneDNN is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs, more details can be found here.

You can find performance numbers in the MXNet tuning guide.

To install native MXNet without oneDNN, run the following command:

{% highlight bash %} pip install mxnet-native==1.7.0 {% endhighlight %}

MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the MXNet tuning guide.

{% highlight bash %} pip install mxnet-mkl==1.6.0 {% endhighlight %}

MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the MXNet tuning guide.

{% highlight bash %} pip install mxnet-mkl==1.5.1 {% endhighlight %}

{% highlight bash %} pip install mxnet==1.4.1 {% endhighlight %}

MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the MXNet tuning guide.

{% highlight bash %} pip install mxnet-mkl==1.4.1 {% endhighlight %}

{% highlight bash %} pip install mxnet==1.3.1 {% endhighlight %}

MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the MXNet tuning guide.

{% highlight bash %} pip install mxnet-mkl==1.3.1 {% endhighlight %}

{% highlight bash %} pip install mxnet==1.2.1 {% endhighlight %}

MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the MXNet tuning guide.

{% highlight bash %} pip install mxnet-mkl==1.2.1 {% endhighlight %}

{% highlight bash %} pip install mxnet==1.1.0 {% endhighlight %}

{% highlight bash %} pip install mxnet==1.0.0 {% endhighlight %}

{% highlight bash %} pip install mxnet==0.12.1 {% endhighlight %}

For MXNet 0.12.0:

{% highlight bash %} pip install mxnet==0.12.0 {% endhighlight %}

{% highlight bash %} pip install mxnet==0.11.0 {% endhighlight %}

{% include /get_started/pip_snippet.md %}