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I often find myself having conversations around deploying RAPIDS where folks ask questions like:

  • What does deploying RAPIDS really mean?
  • Which packages make up RAPIDS?
  • Who is in the RAPIDS community?
  • How do Dask and Spark relate to deploying RAPIDS?
  • Are Dask RAPIDS and Spark RAPIDS the same thing?

I thought I'd draft up some general thoughts on answering some of these questions. Title, location, etc all TBC but thought I'd just throw something here for feedback.


To make things clearer let's start by answering questions like _"Which packages make up RAPIDS?"_ and _"Who is in the RAPIDS community?"_.

## The many projects of RAPIDS
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This seems like a really useful way to convey to first-time users the world of RAPIDS (thinking of that PyData ecosystem image that gets passed around a lot), and I'm wondering if any of this can be applied to the about section of RAPIDS' main page, where some of these packages aren't mentioned?

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Thanks @charlesbluca.

What do you think @exactlyallan? The goal of this doc is to help explain some backround about RAPIDS from a deployment perspective, but perhaps some of this copy would be useful to put on https://rapids.ai?


## Multi-node and multi-GPU deployments

As you move to larger datasets you will want to scale out to multiple GPUs and **multi-node deployments**.
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What's the difference between mult-GPU and multi-node?

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Good point, does this make things clearer?

Suggested change
As you move to larger datasets you will want to scale out to multiple GPUs and **multi-node deployments**.
As you move to larger datasets you will want to scale out to multiple GPUs which may be spread over multiple nodes with **multi-node deployments**.

Co-authored-by: Mike McCarty <mmccarty@nvidia.com>
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3 participants