| **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](https://mxnet.apache.org/get_started/download). |
| |
| Run the following command: |
| |
| <div class="v1-9-1"> |
| {% highlight bash %} |
| pip install mxnet |
| {% endhighlight %} |
| |
| </div> <!-- End of v1-9-1 --> |
| |
| <div class="v1-8-0"> |
| {% highlight bash %} |
| pip install mxnet==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 <a href="https://github.com/oneapi-src/oneDNN">here</a>. |
| |
| You can find performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.8.0/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| To install native MXNet without oneDNN, run the following command: |
| |
| {% highlight bash %} |
| pip install mxnet-native==1.8.0.post0 |
| {% endhighlight %} |
| |
| </div> <!-- End of v1-8-0 --> |
| |
| <div class="v1-7-0"> |
| {% highlight bash %} |
| pip install mxnet==1.7.0.post2 |
| {% 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 <a href="https://github.com/oneapi-src/oneDNN">here</a>. |
| |
| You can find performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.7.0/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| To install native MXNet without oneDNN, run the following command: |
| |
| {% highlight bash %} |
| pip install mxnet-native==1.7.0 |
| {% endhighlight %} |
| |
| </div> <!-- End of v1-7-0 --> |
| |
| <div class="v1-6-0"> |
| {% highlight bash %} |
| pip install mxnet==1.6.0 |
| {% endhighlight %} |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| {% highlight bash %} |
| pip install mxnet-mkl==1.6.0 |
| {% endhighlight %} |
| |
| </div> <!-- End of v1-6-0 --> |
| |
| <div class="v1-5-1"> |
| {% highlight bash %} |
| pip install mxnet==1.5.1 |
| {% endhighlight %} |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| {% highlight bash %} |
| pip install mxnet-mkl==1.5.1 |
| {% endhighlight %} |
| |
| </div> <!-- End of v1-5-1 --> |
| |
| <div class="v1-4-1"> |
| |
| {% 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 |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| {% highlight bash %} |
| pip install mxnet-mkl==1.4.1 |
| {% endhighlight %} |
| |
| </div> <!-- End of v1-4-1 --> |
| <div class="v1-3-1"> |
| |
| {% 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 |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| {% highlight bash %} |
| pip install mxnet-mkl==1.3.1 |
| {% endhighlight %} |
| |
| </div> <!-- End of v1-3-1 --> |
| <div class="v1-2-1"> |
| |
| {% 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 |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| {% highlight bash %} |
| pip install mxnet-mkl==1.2.1 |
| {% endhighlight %} |
| |
| </div> <!-- End of v1-2-1 --> |
| |
| <div class="v1-1-0"> |
| |
| {% highlight bash %} |
| pip install mxnet==1.1.0 |
| {% endhighlight %} |
| |
| </div> <!-- End of v1-1-0--> |
| |
| <div class="v1-0-0"> |
| |
| {% highlight bash %} |
| pip install mxnet==1.0.0 |
| {% endhighlight %} |
| |
| </div> <!-- End of v1-0-0--> |
| |
| |
| <div class="v0-12-1"> |
| |
| {% highlight bash %} |
| pip install mxnet==0.12.1 |
| {% endhighlight %} |
| |
| For MXNet 0.12.0: |
| |
| {% highlight bash %} |
| pip install mxnet==0.12.0 |
| {% endhighlight %} |
| |
| </div> <!-- End of v0-12-1--> |
| |
| <div class="v0-11-0"> |
| |
| {% highlight bash %} |
| pip install mxnet==0.11.0 |
| {% endhighlight %} |
| |
| </div> <!-- End of v0-11-0--> |
| |
| <br> |
| |
| |
| {% include /get_started/pip_snippet.md %} |