Installation Guide

  • 2021.4
  • 09/27/2021
  • Public Content

Install Intel® AI Analytics Toolkit via Conda*

Intel provides access to the AI Kit through a public Anaconda repository. See below for instructions on how to pull the latest versions of the Intel tools. For more information, visit the Conda User Guide.
Installation using Conda requires an existing Conda-based python environment. You can get such an environment by installing the Intel® Distribution for Python or Miniconda*.
To get more details on the AI Analytics Toolkit, visit the Intel AI Analytics toolkit home page.
The AI Kit contains three distinct python environments targeting different use cases:
  • intel-aikit-tensorflow for deep learning workflows using Intel® Optimization for TensorFlow*
  • intel-aikit-pytorch for deep learning workflows using Intel® Optimization for PyTorch*
  • intel-aikit-modin for data analytics and machine learning workflows using Intel® Distribution of Modin (for accelerated data frames) and Intel optimized scikit-learn and XGboost (for ML training and inference)
In the steps outlined below, substitute the bold package name of your choice from the list of three above with the package name in the example:
  1. Activate your existing python conda environment located in
    <pythonhome>
    :
    source <pythonhome>/bin/activate
  2. Install the AI Kit oneAPI packages in a new environment using
    conda create
    . A list of available packages is located at https://anaconda.org/intel/repo. For example, you would use the following to create an AI Kit Tensorflow* environment named
    aikit-tf
    or an Intel:
    conda create -n aikit-tf -c intel intel-aikit-tensorflow Similarly, you can create an AI Kit PyTorch environment named ``aikit-pt``: :: conda create -n aikit-pt -c intel intel-aikit-pytorch
  3. To install the Intel Distribution of Modin, alter the command to include conda forge dependencies:
    conda create -n aikit-modin --override-channels intel-aikit-modin omniscidbe4py python=3.7 -c intel -c conda-forge
  4. Set user environment. After the toolkit is installed, before accessing the tools, you must activate your python environment and set up environment variables to access the tools. For example, to activate the python environment created in the previous step, use:
    conda activate aikit-tf
To install the Model Zoo for Intel® Architecture component of the toolkit, clone the main branch to your local directory:
git clone https://github.com/IntelAI/models.git
.
If you have applications with long-running GPU compute workloads in native environments, you must disable the hangcheck timeout period to avoid terminating workloads.
Intel® packages are available on intel label on the Anaconda* Cloud. You must include
-c intel
on your command line as in the examples above, or add intel to your Conda configuration file using
conda config --add channels intel
.

List of Available Packages

Component Name
Package Name
Platform
Intel® Distribution for Python*
intelpython3_full
linux-x64
Intel® Distribution of Modin* (via Anaconda distribution of the toolkit using the Conda package manager)
intel-aikit-modin
linux-x64
Intel® Low Precision Optimization Tool
lpot
linux-x64
Intel® Optimization for PyTorch*
intel-aikit-pytorch
linux-x64
Intel® Optimization for TensorFlow*
intel-aikit-tensorflow
linux-x64
After you have installed your components, view the Get Started Guide for the Intel oneAPI AI Analytics Toolkit to build and run a sample or explore Getting Started Samples on GitHub.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.