feat: gpu accelerated pandas with cudf example

[jhcipar]

No description provided.

[pandyamarut]

.

[pandyamarut]

Thanks for adding this example, I am not much aware of the Nvidia Rapid APIs. Just to confirm- Everything we have considered here has latest version and is relevant. correct>

[jhcipar]

It's definitely the latest version, but after making this I do wonder if this is something we're really targeting. I'm not sure how often Pandas/dataframe ops are used for llm workloads 🤷

I'm fine with not including it if we don't think it's something people would be interested in!

[pandyamarut]

I will let you make a call here. Thanks

[Copilot]

Pull Request Overview

This PR introduces a new example demonstrating GPU-accelerated data processing using NVIDIA's cuDF library with pandas for analyzing NYC taxi data. The example showcases how to leverage GPU acceleration for common data analytics operations like groupby aggregations.

Key changes:

• Creates a complete cuDF/pandas integration example with GPU acceleration
• Implements persistent storage using network volumes to cache downloaded taxi data
• Demonstrates performance-optimized data analytics workflows on GPU infrastructure

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[Copilot]

Corrected spelling of 'RAPIDs' to 'RAPIDS'. The official project name is 'RAPIDS' not 'RAPIDs'.

Suggested change

	# RAPIDs provides simple APIs to accelerate common Python data analytics functions with GPUs.
	# RAPIDS provides simple APIs to accelerate common Python data analytics functions with GPUs.

[Copilot]

Empty init method with explicit return statement is unnecessary. Either implement initialization logic or remove the method entirely.

Suggested change

	def __init__(self):
	return