Source: https://www.oneapi.io/
The standard Python machine studying toolkit Scikit-learn is a straightforward but efficient framework for classical machine studying. Scikit-learn is the popular alternative for coaching fashions based mostly on linear regression, logistic regression, determination timber, or random forest strategies.
Feature engineering — discovering the correct qualities — and handpicking the right algorithms related to the enterprise challenges are each anticipated in conventional machine studying. For most issues involving structured knowledge saved in relational databases, spreadsheets, or flat recordsdata, that is the most effective method.
Deep studying, alternatively, is a subset of machine studying that makes use of monumental datasets and loads of computing energy to discover high-level options and hidden patterns in knowledge. Deep studying strategies based mostly on well-defined neural community structure are chosen by ML engineers and researchers when coaching fashions based mostly on unstructured enter equivalent to photographs, video, and audio.
Other superior AI frameworks, equivalent to TensorFlow, PyTorch, Apache MXNet, XGBoost, and others, can be utilized to practice fashions based mostly on structured or unstructured datasets and all kinds of strategies utilized in deep studying and traditional machine studying workflows, as well as to Scikit-learn. Researchers and engineers within the discipline of machine studying desire variations of those frameworks which have been tuned for velocity. The AI acceleration is achieved by means of each {hardware} and software program.
Deep studying frameworks equivalent to Apache MXNet, TensorFlow, and PyTorch use NVIDIA CUDA and cuDNN acceleration instruments to give interfaces to the underlying Nvidia GPUs. Heterogeneous Interface for Portability (HIP) and ROCm, which allow entry to AMD GPUs, present a comparable combine. The GPU, software program drivers, runtime, and libraries are all used to speed up AI in these eventualities. Deep studying frameworks are strongly related with AI acceleration applied sciences to speed up deep studying mannequin coaching and inference on the GPUs.
While GPUs are extensively utilized in deep studying coaching, CPUs are extra frequent in the entire end-to-end AI workflow, which incorporates knowledge preprocessing/analytics, machine studying modeling/deployment, and knowledge preprocessing/analytics. Intel Xeon Scalable processors are, in actuality, probably the most generally utilized AI server platform from the cloud to the sting.
Intel has been on the vanguard of oneAPI, a cross-industry, open, standards-based unified programming paradigm that targets a wide range of architectures, together with the aforementioned CPUs and GPUs, in addition to FPGAs and different AI accelerators. Developers can use the oneAPI toolkit as a group of toolkits related to HPC, AI, IoT, and ray tracing utility instances.
The Intel oneAPI AI Analytics Toolkit (AI Kit) is a Python-based toolkit for knowledge scientists and AI engineers. It comes with optimized AI frameworks for Scikit-learn, XGBoost, TensorFlow, and PyTorch as a part of Intel’s end-to-end portfolio of AI improvement instruments.
The Intel Distribution of Modin and Intel Extension for Scikit-learn, that are extremely tuned for the CPU and promise a 10-100X velocity achieve, is probably the most thrilling element of the AI Kit for builders and knowledge scientists using a machine studying course of. The better part about these frameworks is that they’re backward suitable with Pandas and vanilla Scikit-learn, permitting for straightforward swapping.
The Intel Distribution of Modin is a high-performance, parallel, distributed, pandas-compatible DataFrame acceleration answer aimed toward serving to knowledge scientists work extra effectively. The Pandas API is totally suitable with this bundle. Its backend is powered by OmniSci, and it presents sooner analytics on Intel methods.
Modin is Pandas-compatible, and it makes use of Ray and Dask to allow distributed knowledge processing. It’s a Pandas drop-in substitute that turns single-threaded Pandas into multithreaded Pandas, profiting from all CPU cores and immediately dashing up knowledge processing workflows. Modin performs notably properly on giant datasets when pandas may in any other case run out of reminiscence or grow to be exceedingly sluggish.
Modin additionally comes with a robust interface that helps SQL, spreadsheets, and Jupyter notebooks. Modin permits knowledge scientists to reap the benefits of parallelized knowledge processing capabilities whereas nonetheless utilizing the acquainted Pandas API.
Modin is simple to arrange. It’s accessible by way of Intel oneAPI AI Analytics Toolkit’s Conda bundle administration.
A easy Modin implementation is proven within the code snippet under:
The Intel Extension for Scikit-learn offers optimized variations of quite a few scikit-learn algorithms which might be suitable with the unique model and ship sooner outcomes. The bundle merely reverts to Scikit-original be taught’s habits strategies or makes use of parameters that the extension doesn’t assist, giving builders a constant expertise. A given ML utility will proceed to work as earlier than, if not faster, with out the necessity to rewrite further code.
Patching replaces the inventory scikit-learn algorithms with their optimized variations offered by the extension, leading to a sooner studying velocity. The module could be put in utilizing both conda or pip.
Github: https://github.com/oneapi-src
References:
https://www.oneapi.io/https://www.intel.com/content/www/us/en/developer/tools/oneapi/toolkits.html#gs.r4ousuhttps://thenewstack.io/intel-oneapis-unified-programming-model-for-python-machine-learning/
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https://www.marktechpost.com/2022/02/25/introduction-to-intels-oneapi-unified-programming-model-for-python-machine-learning/
