Install Intel® AI Analytics Toolkit via Conda*
Intel provides access to the AI Kit through a public Anaconda repository. If you do not have an existing Conda-based python environment, install Conda and 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).
intel-aikit for data science workstation development. The oneAPI AI kit samples and documents are not applicable to the intel-aikit Conda package.
NOTE:
To get the latest version of the Intel(R) Optimization for TensorFlow*, you must first install Python 3.9, then install the AI Kit through Anaconda.
To instal the AI Kit via Conda, complete the following steps:
Activate your existing python conda environment located in <pythonhome>:
source <pythonhome>/bin/activate
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 If the repo contains an outdated version of a required component, get a newer one by installing via the command line or GUI.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:
For Python 3.7:
conda create -n aikit-modin -c intel intel-aikit-modin
For other Python versions, refer to the instructions in Intel® Distribution of Modin Getting Started Guide.
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
NOTE:
- 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.
List of Available Packages
NOTE:
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.
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.