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Microsoft launched ML.NET 2.0 a little more than a month ago.
However, they are anxious to present the first preview version of ML.NET 3.0 and are not content to stop there. This release includes several hardware acceleration enhancements that let you optimize your computing power when training.
To test out the most recent enhancements driven by Intel oneDAL, download the most recent ML.NET 3.0 and Intel oneDaL preview packages.
For the .NET developer platform, ML.NET is a machine learning framework that is open-source, free, and cross-platform.
You can train, create, and ship unique machine learning models with ML.NET using C# or F# for various ML applications.
Machine learning is now even simpler to integrate into your apps thanks to ML.NET’s capabilities like automatic machine learning (AutoML) and tools like ML.NET CLI and ML.NET Model Builder.
By offering highly optimized algorithmic building blocks for all phases of data analytics (preprocessing, transformation, analysis, modeling, validation, and decision-making) in batch, online, and distributed processing modes of computation, the Intel® oneAPI Data Analytics Library (oneDAL) helps to accelerate big data analysis.
The library optimizes algorithmic processing and data intake to improve performance and scalability. It has connectors to well-known data sources, including Spark and Hadoop, as well as Java and C++ APIs. The Intel Python Distribution includes wrappers for oneDAL written in Python.
In addition to the standard functionality, oneDAL offers GPU utilization for special algorithms and DPC++ SYCL API additions to the standard C++ interface.
In particular, the library is beneficial for distributed computing. It offers a comprehensive set of communication-independent distributed algorithm-building elements.
This enables users to build distributed applications that are quick to scale and employ the communication methods they choose.
Visit the official Intel oneAPI Data Analytics Library website for the full list of features, documentation, code samples, and downloads.
OneDAL utilizes the 64-bit architectures with SIMD extensions found in Intel and AMD CPUs.
By speeding current trainers while being trained, oneDAL integrates into ML.NET. The following ML.NET instructors now offer oneDAL support.
Please take a look at the trainer and their machine learning task.
1. Download and install the most recent Microsoft.ML 3.0 preview. You must install extra packages if you’re using OLS or FastTree.
2. Install the NuGet package for Microsoft ML.OneDal.
3. Set the ONEDAL environment variable for MLNET_BACKEND. No code modifications are necessary if you’re using one of the trainers that oneDAL supports.
4. Utilize one of the ML.NET trainers that oneDAL supports to build a pipeline.
5. Develop your model.
You can encounter a library loading issue on Windows. Add the “runtimes win-x64 native” directory in your application’s “bin” directory to the PATH environment variable to get yourself unblocked.
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