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- W4224320152 abstract "Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger dataset (for both experimentation and production deployment). These usually entail many manual and error-prone steps for the data scientists to fully take advantage of the available hardware resources (e.g., SIMD instructions, multi-processing, quantization, memory allocation optimization, data partitioning, distributed computing, etc.). To address this challenge, we have open sourced BigDL 2.0 at https://github.com/intel-analytics/BigDL/ under Apache 2.0 license (combining the original BigDL and Analytics Zoo projects); using BigDL 2.0, users can simply build conventional Python notebooks on their laptops (with possible AutoML support), which can then be transparently accelerated on a single node (with up-to 9.6x speedup in our experiments), and seamlessly scaled out to a large cluster (across several hundreds servers in real-world use cases). BigDL 2.0 has already been adopted by many real-world users (such as Mastercard, Burger King, Inspur, etc.) in production." @default.
- W4224320152 created "2022-04-26" @default.
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- W4224320152 date "2022-04-02" @default.
- W4224320152 modified "2023-10-17" @default.
- W4224320152 title "BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster" @default.
- W4224320152 doi "https://doi.org/10.48550/arxiv.2204.01715" @default.
- W4224320152 hasPublicationYear "2022" @default.
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