All packages are for Linux only. Components have been validated with Python 3.11. Older versions of Python may not be supported. For compatibility details, refer to the System Requirements.
AI Frameworks and Tools
Software tools at all levels of the AI stack unlock the full capabilities of your Intel® hardware. All Intel® AI tools and frameworks are built on the foundation of a standards-based, unified oneAPI programming model that helps you get the most performance from your end-to-end pipeline on all your available hardware.
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Accelerate your machine learning and data science pipelines with the power of open libraries optimized for Intel® architectures.
Modin* Deprecation Notice
Modin* is no longer available through the AI Tools selector in the 2025.2 release.
You can continue to acquire Modin source, pip packages, and Docker* images from the GitHub* respository.
Boost the performance of your workloads, reduce model size, and improve the speed of your Deep Learning deployments on Intel® Xeon® processors with Intel® Extension for PyTorch.
TensorFlow Notice
Intel® Extension for TensorFlow* is no longer available from this webpage as of the 2025.2 release. You can continue to acquire Intel Extension for TensorFlow source, pip packages, and Docker* images from the Intel Extension for TensorFlow repository on GitHub*.
Reduce model size and improve the speed of your Deep Learning deployments on Intel® Xeon® processors with JAX.
This open source toolkit enables you to optimize a deep learning model from almost any framework and deploy it with best-in-class performance on a range of Intel processors and other hardware platforms.
A separate download is required.
Intel® Gaudi® Software
The Intel® Gaudi® AI accelerator is designed to maximize training throughput and efficiency while providing developers with optimized software and tools that scale to many workloads and systems. Intel® Gaudi® software was developed with the end user in mind, providing versatility and ease of programming to address the unique needs of users’ proprietary models, while allowing for a simple and seamless transition of their existing models over to Intel® Gaudi® technology. The Intel Gaudi software enables efficient mapping of neural network topologies onto Intel Gaudi technology.
The software suite includes a graph compiler and runtime, Tensor Processor Core (TPC) kernel library, firmware and drivers, and developer tools. Intel Gaudi software is integrated with PyTorch, and supports DeepSpeed for large language models (LLM) and performance-optimized Hugging Face models for transformer and diffusion uses.
The links below will allow you to access the PyTorch for Docker containers that contain the full Intel Gaudi software and PyTorch framework. It is recommended to use the Docker images when running models. Use the installation guide to learn how to run the Docker images or perform a manual installation on a bare metal system. Refer to the Support Matrix to see the latest versions of external software and drivers that were used in the Intel Gaudi software release.
Intel, the Intel logo, and Intel Gaudi technology are trademarks of Intel Corporation or its subsidiaries.
Customize your tool selections for conda and pip.
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a. Create and activate a new conda environment using the following syntax. If needed, replace my-env with your preferred name for the environment.
conda create -n my-env
conda activate my-env
b. (Optional) To speed up running, use libmamba as the solver, which is the default in the latest conda distribution. For older conda installations, use this command to set libmamba: conda config --set solver libmamba.
c. For a detailed walk-through on setting up conda, see Set Up System Before Installation: conda.
a. Create and activate a new conda environment using the following syntax. If needed, replace my-env with your preferred name for the environment.
conda create -n my-env
conda activate my-env
b. (Optional) To speed up running, use libmamba as the solver, which is the default in the latest conda distribution. For older conda installations, use this command to set libmamba: conda config --set solver libmamba. For a detailed walk-through on setting up conda, see Set Up System Before Installation: conda.
c. GPU installations have one additional step. For GPU optimizations, install the latest GPU drivers separately as described in Intel® Software for General Purpose GPU Capabilities.
a. Prerequisite: If you do not have pip, use the Installation Instructions. After installation, make sure that you can run pip from the command line.
b. Create and activate a virtual environment using the following syntax. Replace my-env with your preferred name for the environment:
python3 -m venv my-env
source my-env/bin/activate
To install Python and define your environment, see Set Up System Before Installation: pip.
a. Prerequisite: If you do not have pip, use the Installation Instructions. After installation, make sure that you can run pip from the command line.
b. Create and activate a virtual environment using the following syntax. If needed, replace my-env with your preferred name for the environment:
python3 -m venv my-env
source my-env/bin/activate
To install Python and define your environment, see Set Up System Before Installation: pip.
Perform the following steps for GPU installation. GPU installations have additional steps.
c. Install Intel® Deep Learning Essentials for Linux 2025.0.2.
d. Install the latest GPU drivers separately, as described in Intel® Software for General Purpose GPU Capabilities.
Docker* Containers
Before running the containers, install Docker as described in the Docker Installation Instructions.
2. Install with conda
2. Install with conda
Alert: To display the command string, select the checkbox next to the software title you need.
If applicable, disregard a “ClobberError” message associated with installation paths. This error does not impact the functionality of the installed packages.
2. Install with pip
2. Install with pip
Alert: To display the command string, select the checkbox next to the software title you need. PyTorch must be installed first if you are downloading this component.
2. Install with Docker
You may need to use sudo with the pull command to ensure proper permissions.
To verify that the Classic Machine Learning preset is properly installed, enter the following commands:
Intel® Optimization for XGBoost: python -c "import xgboost as xgb; print(xgb.__version__)"
Intel® Extension for Scikit-learn: python -c "from sklearnex import patch_sklearn; patch_sklearn()"
To verify that the Deep Learning PyTorch CPU preset is properly installed, enter the following commands:
Intel® Extension for PyTorch (CPU): python -c "import torch; import intel_extension_for_pytorch as ipex; print(torch.__version__); print(ipex.__version__);"
Intel® Neural Compressor: python -c "import neural_compressor as inc; print(inc.__version__)"
ONNX Runtime: python -c "import onnxruntime; print(onnxruntime.__version__)"
To verify that the Deep Learning PyTorch GPU preset is properly installed, enter the following commands:
Intel® Extension for PyTorch (GPU): python -c "import torch; import intel_extension_for_pytorch as ipex; print(torch.__version__); print(ipex.__version__); [print(f'[{i}]: {torch.xpu.get_device_properties(i)}') for i in range(torch.xpu.device_count())];"
Intel® Neural Compressor: python -c "import neural_compressor as inc; print(inc.__version__)"
To verify that the Deep Learning JAX CPU preset is properly installed, enter the following command:
JAX: python -c "import jax; print(jax.__version__)"
To verify that the selected CPU-compatible components are properly installed, enter the following commands:
Intel® Extension for PyTorch (CPU): python -c "import torch; import intel_extension_for_pytorch as ipex; print(torch.__version__); print(ipex.__version__);"
Intel® Optimization for XGBoost: python -c "import xgboost as xgb; print(xgb.__version__)"
Intel® Extension for Scikit-learn: python -c "from sklearnex import patch_sklearn; patch_sklearn()"
Intel® Neural Compressor: python -c "import neural_compressor as inc; print(inc.__version__)"
JAX: python -c "import jax; print(jax.__version__)"
ONNX Runtime: python -c "import onnxruntime; print(onnxruntime.__version__)"
To verify that the selected GPU-compatible components are properly installed, enter the following commands:
Intel® Extension for PyTorch* (GPU): python -c "import torch; import intel_extension_for_pytorch as ipex; print(torch.__version__); print(ipex.__version__); [print(f'[{i}]: {torch.xpu.get_device_properties(i)}') for i in range(torch.xpu.device_count())];"
Intel® Optimization for XGBoost*: python -c "import xgboost as xgb; print(xgb.__version__)"
Intel® Extension for Scikit-learn*: python -c "from sklearnex import patch_sklearn; patch_sklearn()"
Intel® Neural Compressor: python -c "import neural_compressor as inc; print(inc.__version__)"
To run a preset container and a get-started sample, follow the instructions in Intel AI Tools Selector Preset Containers. There are several options to run a container on a CPU or GPU.
After a successful installation, to start using the installed product, see Get Started Samples for AI Tools.
Next Steps
- The Intel® AI Reference Models (formerly Model Zoo) repository contains links to pretrained models, sample scripts, best practices, and tutorials for many popular open source machine learning models optimized by Intel.
- The Working with Preset Containers document provides more information about preset containers and instructions on how to run them.
Additional Resources
Versions
Products have been updated to include functional and security updates. Customers should update to the latest versions as they become available.
Docker License Information
By accessing, downloading, or using this software and any required dependent software (the “Software Package”), you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third-party software included with the Software Package. Preset containers are published under Apache License 2.0.
Support
Start-up support is available if there is an issue with the tool selector functionality.