Intel® AI Analytics Toolkit(Beta)
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Accelerate the Entire AI Application Pipeline
AI is found in almost every industry vertical, with applications and use cases that are transforming business and conferring competitive advantage.
The Intel® AI Analytics Toolkit gives developers, researchers, and data scientists tools to accelerate each step in the pipeline—training deep neural networks, integrating trained models into applications for inference, and executing functions for data science and analytics.
Using this toolkit, you can:
- Deliver high-performance training on CPUs and integrate deep learning (DL) inference into your AI applications with Intel®-optimized DL frameworks: TensorFlow* and PyTorch*.
- Accelerate data science and analytics stages with compute-intensive Python* packages enhanced for Intel® architectures, including NumPy, SciPy, scikit-learn*, and XGboost*.
For the best performance, download the Intel® oneAPI Base Toolkit. Its complementary tools include the Intel® oneAPI Data Analytics Library, Intel® oneAPI DPC++ Compiler, powerful data-centric libraries, and advanced analysis tools.
Develop, Test, and Run Your oneAPI Code in the Cloud
Get what you need to build and optimize your oneAPI projects for free. With an Intel® DevCloud account, you get 120 days of access to the latest Intel® hardware—CPUs, GPUs, FPGAs—and Intel oneAPI tools and frameworks. No software downloads. No configuration steps. No installations.
Download the Toolkit
Optimized Deep Learning Frameworks
Deep learning frameworks provide a high-level programming language to architect, train, and validate deep neural networks. Popular frameworks, such as TensorFlow* and PyTorch*, are directly optimized to fully use the power of Intel® architecture and yield high performance for training and inference.
Python has become the most popular and fastest growing programming language for AI and data analytics. Intel® Distribution for Python* includes accelerated compute intensive packages that are heavily used in machine learning and data science, such as NumPy, SciPy, scikit-learn*, and XGBoost. The algorithms are optimized for Intel® architectures and take advantage of the underlying instruction set to maximize performance. The distribution also includes daal4py, a pythonic interface to the Intel oneAPI Data Analytics Library.
Implement data science and analytics pipelines—preprocessing through machine learning—and scale-out efficiently using the high-performing Intel oneAPI Data Analytics Library, part of the foundational Intel oneAPI Base Toolkit. The library’s set of high-speed algorithms (such as analysis functions, math functions, and training and prediction functions) enable applications to analyze large data sets with available compute resources and make better predictions faster.
Train and infer high-performing, large-scale machine learning models with this Intel-optimized deep-learning framework that's based on Python.
Use this Python-based, Intel-optimized scientific computing package for deep learning training and inference workloads.