RAPIDS is a suite of open-source GPU-accelerated data science and AI libraries that are well supported for scale-out with distributed engines like Spark and Dask. Ray is a popular open-source distributed Python framework commonly used to scale AI and machine learning (ML) applications. Ray particularly excels at simplifying and scaling training and inference pipelines and can easily target both…
]]>The latest developments in large language model (LLM) scaling laws have shown that when scaling the number of model parameters, the number of tokens used for training should be scaled at the same rate. The Chinchilla and LLaMA models have validated these empirically derived laws and suggest that previous state-of-the-art models have been under-trained regarding the total number of tokens used…
]]>Custom “row-by-row” processing logic (sometimes called sequential User-Defined Functions) is prevalent in ETL workflows. The sequential nature of UDFs makes parallelization on GPUs tricky. This blog post covers how to implement the same UDF logic using RAPIDS to parallelize computation on GPUs and unlock 100x speedups. Typically, sequential UDFs revolve around records with the same…
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