Installation Guide

  • 2022.2
  • 04/13/2022
  • 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 Panda data frames), Intel® Extension for Scikit-learn* and Intel optimizations for XGboost (for ML training and inference)
To instal the AI Kit via Conda, complete the following steps:
  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. Not all packages in the Anaconda repository are up to date with the current release. If the repo contains an outdated version of a required component, get a newer one by installing via the command line or GUI.
    If the repository contains the desired version, create an AI Kit Tensorflow* environment named
    aikit-tf
    with this version:
    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
    You can also create an AI Kit Modin and machine learning environment named
    aikit-modin
    :
    conda create -n aikit-modin -c intel intel-aikit-modin
  3. 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® Neural Compressor
neural-compressor
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.