Unlocking the Power of GPUs in Data Processing with RAPIDS

Table of Contents

1. Introduction
2. Getting Started with Rapids
3. Setting up the Environment
4. Working with Pandas
5. Introducing cuDF
6. Building Models with cuML
7. Distributed Computing with Dask
8. Creating a Local Cluster
9. Executing Distributed Programs with Dask
10. Scaling Workflows with Dask and cuDF
11. Conclusion

Introduction

In this webinar, we will explore the open-source ecosystem developed by NVIDIA called Rapids. Rapids is a GPU acceleration learning system that allows data scientists to leverage the power of GPUs for faster and more efficient data processing and machine learning tasks.

Getting Started with Rapids

Before we begin, we need to ensure that we have access to a GPU and have set up the environment to use Rapids. In this webinar, we will be using a powerful supercomputer called dgx with 16 Tesla V100 GPUs. We will also need to create an environment to run Rapids, which can be built from scratch or pulled from the NVIDIA GPU cloud using Docker.

Setting up the Environment

Once we have the necessary GPU and environment set up, we can start a gpnotebook session to interact with Rapids via an internet browser. We will be using a notebook from the extended GitHub repository, which provides an introduction to Rapids. The link to this notebook and instructions to build and run the environment can be found in the video description.

Working with Pandas

Pandas is a widely used Package in the Python ecosystem for working with structured tabular data. It allows data scientists to easily manipulate data by performing operations such as filtering, transforming, aggregating, merging, and visualizing. We will explore how to create a Pandas DataFrame, perform operations on the data, and discuss its limitations when working with large datasets.

Introducing cuDF

To overcome the limitations of Pandas and leverage the power of GPUs, Rapids provides a package called cuDF. cuDF allows data scientists to migrate their existing Pandas workflows from CPU to GPU. The syntax for creating and manipulating cuDF DataFrames is identical to that of Pandas, minimizing the cognitive burden of switching to a GPU-based workflow. We will demonstrate how to create a cuDF DataFrame and perform operations using GPU acceleration.

Building Models with cuML

In addition to data manipulation, data scientists often need to build models to understand the relationships between different variables in the data. cuML is a powerful toolkit provided by Rapids that allows data scientists to quickly build models from their data. We will walk through an example of creating a linear regression model using cuML and compare it with the CPU-based scikit-learn model. The GPU-accelerated model allows us to take advantage of the parallelization offered by GPUs and significantly speed up model training.

Distributed Computing with Dask

As datasets grow larger and more complex, the need for distributed computing arises. Rapids integrates with Dask, a library that facilitates distributed computing in Python. Dask allows the composition of complex workflows using basic Python primitives and large data structures such as cuDF DataFrames. We will explore how to set up a local cluster of workers using Dask and execute distributed programs.

Creating a Local Cluster

To get started with Dask, we need to create a local cluster of workers and a client to interact with that cluster. The number of workers can be determined based on the available computing resources. For CPU-based workflows, this could be the number of cores or Threads on the machine. We will create a cluster with a client and inspect the client object to view the current task status.

Executing Distributed Programs with Dask

Once the client and cluster are set up, we can execute our first distributed program. We will define a function called "sleep_one" that sleeps for one Second and returns the STRING "success" when executed in serial. Using Dask's delayed function, we can delay the execution of function calls and build a graph of tasks. The computations will be executed in Parallel by the workers, and we can Collect the results using the client's Gather method.

Scaling Workflows with Dask and cuDF

Dask allows us to Scale ETL (Extract, Transform, Load) and machine learning workflows to handle gigabytes or terabytes of data. By combining Dask with cuDF, we can process large datasets and employ embarrassingly parallel algorithms. We will demonstrate how to process 100 million rows using cuDF and Dask, showcasing the scalability of these tools.

Conclusion

In this webinar, we have explored the different components of the Rapids ecosystem, including Rapids, cuDF, cuML, and Dask. We have seen how these tools enable data scientists to leverage GPU acceleration for faster and more efficient data processing and machine learning. With Rapids, data scientists can overcome the limitations of traditional CPU-based workflows and scale their work to handle massive datasets. Thank you for joining us on this journey through the Rapids ecosystem.

Highlights

• Rapids is an open-source ecosystem developed by NVIDIA for GPU acceleration in data processing and machine learning.
• cuDF allows data scientists to migrate their existing Pandas workflows from CPU to GPU, leveraging the parallelization offered by GPUs.
• cuML provides a powerful toolkit for building machine learning models on GPUs, offering significant speed improvements.
• Dask facilitates distributed computing in Python, allowing the scalability of workflows to handle large datasets.
• By combining Dask with cuDF, data scientists can scale ETL and machine learning workflows to gigabytes or terabytes of data.

FAQ

Q: Can I use Rapids without a GPU? A: No, Rapids requires access to a GPU to take advantage of its GPU acceleration capabilities.

Q: How can I set up an environment to use Rapids? A: You can either build the environment from scratch or pull a pre-built environment from the NVIDIA GPU cloud using Docker.

Q: Is the syntax for working with cuDF the same as working with Pandas? A: Yes, the syntax for creating and manipulating cuDF DataFrames is identical to that of Pandas, making it easy to migrate existing workflows.

Q: Can Dask and cuDF handle large datasets? A: Yes, Dask and cuDF can handle gigabytes or terabytes of data, allowing data scientists to scale their workflows.

Q: Can I use Rapids with other Python packages like scikit-learn and matplotlib? A: Yes, Rapids integrates with other popular Python packages, allowing seamless compatibility and interoperability.

Q: Is it possible to run distributed programs on multiple GPUs or nodes? A: Yes, Rapids and Dask support running distributed programs across multiple GPUs or even multiple nodes, enabling further parallelization.

Resources

• Rapids GitHub Repository
• NVIDIA GPU Cloud
• scikit-learn
• matplotlib
• Dask Documentation
• cuDF Documentation
• cuML Documentation