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Popular tools such as Pytorch, TensorFlow, and JAX can be used with the Intel distribution for Python (use the offline installer on the login nodes) together with certain special framework-specific extensions. Environments can be separately prepared for each framework below for use with Intel GPUs. Note that the module intel/2024.0.0 (under sw.pvc) must be loaded for these frameworks to be installed or run properly.

Pytorch

Load the Intel OneAPI module and create a new conda environment within your Intel python distribution:

module load intel/2024.0.0

conda create -n intel_pytorch_gpu python=3.9
conda activate intel_pytorch_gpu

Once the new environment has been activated, the following commands install Pytorch:

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We also offer a standalone module (intel_AI_tools/2024.0.0) that loads a conda installation with the following pre-installed, Intel GPU/XPU-ready environments:

• intel_pytorch_2.1.0a0

• intel_tensorflow_2.14.0

• intel_jax_0.4.20

Pytorch

Load the Intel OneAPI module and create a new conda environment within your Intel python distribution:

module load intel/2024.0.0

conda create -n intel_pytorch_gpu python=3.9
conda activate intel_pytorch_gpu

Once the new environment has been activated, the following commands install Pytorch:

python -m pip install torch==2.1.0a0 torchvision==0.16.0a0 torchaudio==2.1.0a0 intel-extension-for-pytorch==2.1.10+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

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Examples of how to use the Intel extension for Pytorch can be found here.

TensorFlow

Similar to Pytorch, an Intel extension for TensorFlow exists. To prepare a TensorFlow environment for use with Intel GPUs, first create a new conda environment:

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pip install tensorflow==2.14.0
pip install --upgrade intel-extension-for-tensorflow[xpu]==2.14.0

This installs TensorFlow together with it's Intel extension necessary to run non-CUDA operations on Intel GPUs. On a compute node, the presence of GPUs can be assessed:

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Examples of how to use the Intel extension for TensorFlow can be found here.

JAX

Like Pytorch and TensorFlow, JAX also has an extension via OpenXLA. To prepare a JAX environment for use with Intel GPUs, first create a new conda environment:

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Once the environment is activated, the following commands install JAX

pip install numpy==1.24.4
pip install jax==0.4.20 jaxlib==0.4.20
pip install --upgrade intel-_extension-_for-openxla_openxla==0.2.1

This installs JAX together with its Intel extension necessary to run non-CUDA operations on Intel GPUs. On a compute node, the presence of GPUs can be assessed:

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Examples for using the Intel extension for JAX can be found here.

Distributed Training

multigpu and multinode jobs can be executed using the following strategy in a job submission script:

module load intel/2024.0.0
module load impi

export CCL_ROOT=/sw/compiler/intel/oneapi/ccl/2021.12
export LD_LIBRARY_PATH=$I_MPI_ROOT/lib:$LD_LIBRARY_PATH
hnode=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
export MASTER_ADDR=$(scontrol getaddrs $hnode | cut -d' ' -f 2 | cut -d':' -f 1)
export MASTER_PORT=29500

It is advantageous to define the GPU tile usage (each Intel Max 1550 has two compute “tiles”) using affinity masks, wherein the format GPU_ID.TILE_ID (zero-base index) specifies which GPU(s) and tile(s) to use. Eg, two use two GPUs and four tiles, one can specify:

export ZE_FLAT_DEVICE_HIERARCHY=COMPOSITE
export ZE_AFFINITY_MASK=0.0,0.1,1.0,1.1

To use four GPUs and eight tiles, one would specify:

export ZE_FLAT_DEVICE_HIERARCHY=COMPOSITE
export ZE_AFFINITY_MASK=0.0,0.1,1.0,1.1,2.0,2.1,3.0,3.1

These specifications are applied to all nodes of a job. For more information, and alternative modes, please see the intel level-zero documentation.

Intel MPI can then be used to distribute and run your job, eg:

mpirun -np 8 -ppn 8 your_exe your_exe_flags