title: How to access Spark DataFrame data in GPU from ML Libraries such as PyTorch or Tensorflow
body: <p>Currently I am studying the usage of Apache Spark 3.0 with Rapids GPU Acceleration. In the official <code>spark-rapids</code> docs I came across <a href="https://nvidia.github.io/spark-rapids/docs/ml-integration.html" rel="nofollow noreferrer">this page</a> which states:</p>
  <blockquote>
  <p>There are cases where you may want to get access to the raw data on the GPU, preferably without copying it. One use case for this is exporting the data to an ML framework after doing feature extraction.</p>
  </blockquote>
  <p>To me this sounds as if one could make data that is already available on the GPU from some upstream Spark ETL process directly available to a framework such as Tensorflow or PyTorch. If this is the case how can I access the data from within any of these frameworks? If I am misunderstanding something here, what is the quote exactly referring to?</p>