If you have not already installed the AI Tools, refer to Installing the Intel® 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>/..

Conda Environments in the AI Tools

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

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. 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.