Get Started

Get Started with the AI Tools for Linux*

ID 766885
Date 12/16/2024
Public

Configure Your System - AI Tools

Activate AI Tools Base Environment

Linux

Open a terminal window and type the following:

  • If the default path is used during the installation:

  • source $HOME/intel/oneapi/intelpython/bin/activate
    
  • If a non-default path is used:

  • source <custom_path>/bin/activate
    

Verify that conda is installed and running on your system, and list environments, by typing:

conda --version
conda env list

Intel® AI Reference Models folder will be located in $HOME/intel/oneapi/ai_reference_models.

If a custom path was used, Intel® AI Reference Models will be installed one level below: <custom_path>/..

Next Steps

  • For Conda users, continue on to the next section.

  • For developing on a GPU, continue on to GPU Users

Conda Environments in the AI Tools

The following conda environments are included in the AI Tools.

Conda Environment Name AI Tool
tensorflow Intel® Extension for TensorFlow* (CPU)

Intel® Neural Compressor

ONNX* Runtime*

tensorflow-gpu Intel® Extension for TensorFlow* (GPU)

Intel® Neural Compressor*

Intel® Optimization for Horovod*

pytorch Intel® Extension for PyTorch* (CPU)

Intel® Neural Compressor

ONNX Runtime*

pytorch-gpu Intel® Extension for PyTorch* (GPU)

Intel® Neural Compressor*

Intel® oneCCL Bindings for PyTorch*

modin Modin*

oneMKL

jax JAX*

oneMKL

base Intel® Optimization for XGBoost*

Intel® Extension for Scikit-learn*

  1. From the same terminal window where the AI Tools Base Environment was activated, identify the Conda environments on your system:
    conda env list
    You will see results similar to this:
    # conda environments:
    #
    base                  *  $HOME/intel/oneapi/intelpython/
    pytorch                  $HOME/intel/oneapi/intelpython/envs/pytorch
    Pytorch-gpu              $HOME/intel/oneapi/intelpython/envs/pytorch-gpu
    tensorflow               $HOME/intel/oneapi/intelpython/envs/tensorflow 
    tensorflow-gpu           $HOME/intel/oneapi/intelpython/envs/tensorflow-gpu 
    modin                    $HOME/intel/oneapi/intelpython/envs/modin
    jax                      $HOME/intel/oneapi/intelpython/envs/jax
  2. Additional environments can be activated with:
    conda activate <environment>
    For example, to activate the TensorFlow* or PyTorch* environment:

    TensorFlow:

    conda activate tensorflow

    PyTorch:

    conda activate pytorch

  3. Verify the new environment is active. An asterisk will be displayed next to the active environment.

    conda env list
  4. Additionally, the components installed on the active environment can be listed with:

    conda list

GPU Users

For those who are developing on a GPU, follow these steps:

1. Install GPU drivers

If you followed the instructions in the Installation Guide to install GPU Drivers, you may skip this step. If you have not installed the drivers, follow the directions in the Installation Guide.

2. Add User to Video Group

For GPU compute workloads, non-root (normal) users do not typically have access to the GPU device. Make sure to add your normal user(s) to the video group; otherwise, binaries compiled for the GPU device will fail when executed by a normal user. To fix this problem, add the non-root user to the video group:

sudo usermod -a -G video <username>

3. Disable Hangcheck

For applications with long-running GPU compute workloads in native environments, disable hangcheck. Disabling hangcheck is not recommended for virtualizations or other standard usages of GPU, such as gaming.

A workload that takes more than four seconds for GPU hardware to execute is a long running workload. By default, individual threads that qualify as long-running workloads are considered hung and are terminated. By disabling the hangcheck timeout period, you can avoid this problem.

NOTE:
If the kernel is updated, hangcheck is automatically enabled. Run the procedure below after every kernel update to ensure hangcheck is disabled.

  1. Open a terminal.
  2. Open the grub file in /etc/default.
  3. In the grub file, find the line GRUB_CMDLINE_LINUX_DEFAULT="" .
  4. Enter this text between the quotes (""):
    i915.enable_hangcheck=0
  5. Run this command:
    sudo update-grub
  6. Reboot the system. Hangcheck remains disabled.

Uninstalling AI Tools

To uninstall the AI Tools, follow the steps below:

  1. Revert Conda changes by using the following command:
    conda init --reverse -–all

    Use the --dry-run flag if you want to check what will be reverted before executing the command.

  2. Remove the installation directory.

    If the default path was used during the installation:

    rm -rf ${HOME}/intel

    If a non-default path was used:

    rm -rf <custom_path>
  3. Remove .sh file.

    rm -rf l_AITools.2024.1.0.9.sh
    NOTE:
    For this command, you will need to customize the .sh file name, as this file name will be different depending on the package version. The above command uses 2024.1 as an example.

Next Step

Now that you have configured your system, proceed to Build and Run a Sample Project.