Data Scientists! Intel oneAPI AI Analytics Toolkit is all you were looking for!
As a Data Scientist, you know how difficult is it to work with a dataset of more than 5 million records with 100+ columns, right?
There are 100+ Python Package and optimizations done in popular Data Scientists packages such as Pandas, Numpy, scikit-learn and many more.
Even with all the open-source contributions and maintainers working hard to merge the PRs and get the latest versions of each package ready and push it to public use. It’s not just fast or it’s not enough for our use case.
What’s the solution then, Intel recently launched their own suit of tools for Data Scientists. Mostly it’s a wrapper around the existing Python Packages.
Some of the offerings for AI Analytics Toolkit,
Modin — Accelerate your Pandas operations
Modin shines and gives us noticable performance while performing operations with larger datasets, where Panadas struggles or slows down. Mostly the speed up is because Modin uses all of your processor cores to do the computation.
Note: This is just one package inside the Intel oneAPI Analytics toolkit.
A simple performance Benchmark:
GitHub Stats:
Downloads- 5M+
Initial Release- Goes back to 2018
Latest Release- Jan 26, 2023
License- Apache-2.0 license
Stars- 8.4k stars
Watchers- 108 watching
Forks- 592 forks
Here are some other players in this space,
Buzz around Intel’s oneAPI toolkits,
On November 28th 2022, Amazon announced at its re:Invent 2022 conference that Modin will be offered as a part of AWS Glue and SDK for Pandas. (Source: aws.amazon.com)
CERN Uses Intel® Deep Learning Boost & oneAPI to Juice Inference without Accuracy Loss (Source: intel.com)
LAIKA Studios* and the Intel Applied Machine Learning team used tools from the AI Kit to realize the limitless scope of stop-motion animation (Source: intel.com)
Intel said its optimizations, which also make use of the company’s inference-focused OpenVino toolkit, were shown to enable 20 percent faster training performance and 55 percent faster inference performance for the quality control model compared to Accenture’s stock implementation. (Source: theregister.com)
Intel’s oneAPI toolkit makes it easier to build applications that can run on multiple types of chips. According to the company, oneAPI reduces the amount of code that must be changed when an application is ported from one processor architecture to another. The result is that developers can complete software projects faster. (Source: siliconangle.com)
Why It Matters: Developers are looking to infuse AI into their solutions and the reference kits contribute to that goal. These kits build on and complement Intel’s AI software portfolio of end-to-end tools and framework optimizations. Built on the foundation of the oneAPI open, standards-based, heterogeneous programming model, which delivers performance across multiple types of architectures, these tools help data scientists train models faster and at lower cost by overcoming the limitations of proprietary environments. (Source: intel.com)
Projects using Modin:
https://github.com/ludwig-ai — 8.7k stars
https://github.com/flyteorg — 3.1k stars
https://github.com/unionai-oss — 2k stars
https://github.com/sfu-db — 1.5k stars
https://github.com/ray-project — 24.2k stars
https://github.com/jmcarpenter2/swifter — 2.2k stars
https://github.com/ml-tooling/ml-workspace -2.9k stars
Note: I’ve listed only few projects here there are 800+ repos using it
In the era where code writes code, as humans it’s extremely important to optimise our working style and time.
There is a lot going on in this space but it’s worth to explore Modin and other packages in Intel oneAPI toolkit. Using tools wisely and choosing the right tool for the job is key to accomplish your targets with ease and get ahead of the curve.
It’s still early to say that every Data Scientist should switch to Intel’s oneAPI toolkits but it’s worth exploring new arenas.
Happy Learning!!
