If you have not already installed the AI Analytics Toolkit, refer to Installing the Intel® AI Analytics Toolkit.

To configure your system, set environment variables before continuing.

Set Environment Variables for CLI Development

For working at a Command Line Interface (CLI), the tools in the oneAPI toolkits are configured via environment variables. To set environment variables bysourcing the setvars script:

Source setvars.sh every time you open a new terminal window:

You can find the setvars.sh script in the root folder of your oneAPI installation, which is typically /opt/intel/oneapi/ for system wide installations and ~/intel/oneapi/ for private installations.

For system wide installations (requires root or sudo privileges):

. /opt/intel/oneapi/setvars.sh

For private installations:

. ~/intel/oneapi/setvars.sh

To have the environment automatically set up for your projects, include the command source <install_dir>/setvars.sh in a startup script where it will be invoked automatically (replace <install_dir> with the path to your oneAPI install location). The default installation locations are /opt/intel/oneapi/ for system wide installations (requires root or sudo privileges) and ~/intel/oneapi/ for private installations.

For example, you can add the source <install_dir>/setvars.sh command to your ~/.bashrc or ~/.bashrc_profile or ~/.profile file. To make the settings permanent for all accounts on your system, create a one-line .sh script in your system's /etc/profile.d folder that sources setvars.sh (for more details, see Ubuntu documentation on Environment Variables).

Conda Environments in this Toolkit

Use the Conda Clone Function to Add Packages as a Non-Root User

The Intel AI Analytics toolkit is installed in the oneapi folder, which requires root privileges to manage. You may wish to add and maintain new packages using Conda*, but you cannot do so without root access. Or, you may have root access but do not want to enter the root password every time you activate Conda.

To manage your environment without using root access, utilize the Conda clone functionality to clone the packages you need to a folder outside of the /opt/intel/oneapi/ folder:

1. From the same terminal window where you ran setvars.sh, identify the Conda environments on your system:

   conda env list

   You will see results similar to this:

   # conda environments:
   #
   base                  *  /opt/intel/oneapi/intelpython/latest
   2023.1                   /opt/intel/oneapi/intelpython/latest/envs/2023.0
   pytorch                  /opt/intel/oneapi/intelpython/latest/envs/pytorch
   Pytorch-gpu              /opt/intel/oneapi/intelpython/latest/envs/pytorch-gpu
   tensorflow               /opt/intel/oneapi/intelpython/latest/envs/tensorflow 
   tensorflow-gpu           /opt/intel/oneapi/intelpython/latest/envs/tensorflow-gpu 
   modin                    /opt/intel/oneapi/intelpython/latest/envs/modin

2. Use the clone function to clone the environment to a new folder. In the example below, the new environment is named usr_intelpython and the environment being cloned is named base (as shown in the image above).

   conda create --name usr_intelpython --clone base

   The clone details will appear:

   (base) -bash.4.3$ conda create --name usr_intelpython --clone base
   Source: /opt/intel/oneapi/intelpython/latest
   Destination: /___/home/.conda/envs/usr_intelpython

   If the command does not execute, you may not have access to the ~/.conda folder. To fix this, delete the .conda folder and execute this command again: conda create --name usr_intelpython --clone base.

3. Activate the new environment to enable the ability to add packages.

   conda activate usr_intelpython

4. Verify the new environment is active.

   conda env list

   You can now develop using the Conda environment for Intel Distribution for Python.

5. To activate the TensorFlow* or PyTorch* environment:

   TensorFlow:

   conda activate tensorflow

   PyTorch:

   conda activate pytorch

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. This 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.

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.

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