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Sign in[Submit an Article](https://write.insightmediagroup.io) [Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Data Science](https://towardsdatascience.com/category/data-science/) # Polars + NVIDIA GPU Tutorial Using Polars with NVIDIA GPU can speed up your data pipelines [Ivo Bernardo](https://towardsdatascience.com/author/ivopbernardo/) September 17, 20244 min read ![Image by noaa @ Unsplash.com](../images/000.jpg) Image by [noaa](https://unsplash.com/pt-br/@noaa) @ Unsplash.com Dealing with massive datasets inside Python has always been a challenge. The language is not tailored for handling huge amounts of data as native SQL systems or spark. The most famous library for handling 2-D datasets within Python is, without any question, _pandas_. Although easy to use and used by every data scientist, Pandas is written in Python and C, making it a bit combersume and slow to perform operations on large data. If you are a data scientist, you've dealt with the pain of waiting 200 years for a _group by_ to finish. One of the libraries that aims to solve this is _polars_ -an extremely efficient Python package that is able to handle large datasets, mostly for the following reasons: * It's written in Rust * It leverages multi-threading automatically * It defers most calculations by using lazy evaluation And.. after today, you can now leverage NVIDIA's hardware to maximize _polars_ ' GPU engine capabilities. In this blog post, we'll see how you can leverage _polars+GPU_ and speed up your data pipelines enormously. * * * Learn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps. ## Setting Up the Environment To follow along this blog post, you can check the accompanying notebook [here](https://colab.research.google.com/drive/1Ety47AG_s8JWJCsOUDYoiAwmeILsSG0x?usp=sharing). First, let's install `polars` with GPU capabilities by using: perl ``` !pip install polars[gpu] --extra-index-url=https://pypi.nvidia.com ``` We need to bring out the big guns to test our data pipelines. We'll be using a large dataset of [Simulated Transactions](https://www.kaggle.com/datasets/conorsully1/simulated-transactions) - a large dataset representing different monetary transactions of customers. As I'm assuming you are using Google Colab and we want to keep ourselves on the free tier, I'll use a sample of this dataset that only uses 20% of the size: ruby ``` !wget https://storage.googleapis.com/rapidsai/polars-demo/transactions-t4-20.parquet -O transactions.parquet ``` But, if you have your own system and resources that can handle the size of the total data, feel free to download the original file: ruby ``` !wget https://storage.googleapis.com/rapidsai/polars-demo/transactions.parquet -O transactions.parquet ``` ![Preview of Transactions Table - Image by Author](../images/001.png) Preview of Transactions Table - Image by Author Finally, let's load `polars` : javascript ``` import polars as pl from polars.testing import assert_frame_equal ``` Setup is done, time to play around with our dataset! * * * ## Simple Calculations We need to load our dataset into our `polars`engine by leveraging the `scan_parquet` method: ini ``` transactions = pl.scan_parquet("transactions.parquet") ``` We can take a peak at our first 5 transactions using `head` and `collect` : scss ``` transactions.head(5).collect() ``` ![Transactions Table in Pandas - Image by Author](../images/002.png) Transactions Table in Pandas - Image by Author The simples use case we can think of is to perform a simple aggregation of a numerical column. Let's sum the total amount of the transactions on our table using `polars`syntax: less ``` %%time transactions.select(pl.col("AMOUNT").sum()).collect() ``` ![Aggregate Execution - Image by Author](../images/003.png) Aggregate Execution - Image by Author CPU time for this operation was _1.36s_ on the 52 million rows. We'll keep track of the execution time of our code throughout our experiments for benchmarking purposes. Let's see how this operation works on the `gpu_engine` : graphql ``` gpu_engine = pl.GPUEngine( device=0, raise_on_fail=True, ) ``` less ``` %%time transactions.select(pl.col("AMOUNT").sum()).collect(engine=gpu_engine) ``` ![Aggregate Execution with gpu_engine - Image by Author](../images/004.png) Aggregate Execution with gpu_engine - Image by Author Nice improvement! CPU time was ~4 times faster. Will this stand for complex calculations? * * * ## Using Group By + Aggregations One common operation we do with 2 dimensional structures is performing _group by_ + any type of aggregation. These types of aggregations typically show up when quick answers are needed for specific business questions or during particular steps in the data wrangling process. For example, which customers are the highest spenders in our dataset? Let's calculate that using the CPU: sql ``` %%time high_spenders_cpu = ( transactions .group_by("CUST_ID") .agg(pl.col("AMOUNT").sum()) .sort(by="AMOUNT", descending=True) .head() .collect() ) high_spenders_cpu ``` The `cpu_time` for this operation was (drumroll): ![Group By Aggregation Operation - Image by Author](../images/005.png) Group By Aggregation Operation - Image by Author 4.27 seconds, not bad. But what if we do this operation on our _GPU_? sql ``` %%time high_spenders_gpu = ( transactions .group_by("CUST_ID") .agg(pl.col("AMOUNT").sum()) .sort(by="AMOUNT", descending=True) .head() .collect(engine=gpu_engine) ) high_spenders_gpu ``` ![Group By Aggregation Operation on GPU Engine— Image by Author](../images/006.png) Group By Aggregation Operation on GPU Engine— Image by Author _345 ms_ for an aggregation on a 52 million row dataframe. That's actually a great result! And what's nice is that we can also rely on polars `SQL` interface to achieve similar results: python ``` sql_query = """ SELECT CUST_ID, SUM(AMOUNT) as sum_amt FROM transactions GROUP BY CUST_ID ORDER BY sum_amt desc LIMIT 5 """ %time pl.sql(sql_query).collect() %time pl.sql(sql_query).collect(engine=gpu_engine) ``` ![Group By Aggregation Operation with Polars SQL interface— Image by Author](../images/007.png) Group By Aggregation Operation with Polars SQL interface— Image by Author * * * In conclusion, we were able to speed up our execution time in 5x (up to 10x) in aggregation operations by using the GPU capabilities of polars and it was awesome. Standard `polars` already shows [extremely good results in other benchmarks](https://medium.com/cuenex/pandas-2-0-vs-polars-the-ultimate-battle-a378eb75d6d1), but prepping it up with GPU capabilities is totally a new game. In a nutshell: * The fastest performance for Polars is on NVIDIA GPUs, up to 10x speed up over CPUs * The Polars GPU engine enables users to process 100s of millions of rows of data in seconds * Access to the Polars GPU engine requires almost zero code changes to existing Polars code You can also check more examples and experiments by visiting the official [repo](https://github.com/rapidsai-community/showcase/blob/main/accelerated_data_processing_examples/polars_gpu_engine_demo.ipynb). Last, but not least, thank you to NVIDIA for giving me early access to the package and being able to experiment with this new accelerated version of polars. Looking forward to new developments! If you want to read / see more content related to AI and DS, subscribe my youtube Channel "_The Data Journey_ ": ![](../images/body-31d2ad1a8620f4bd994d.png) [_[The dataset used in this blog post is under licence CC0: Public Domain]_](https://www.kaggle.com/datasets/conorsully1/simulated-transactions) ## Register Now to Continue Reading ### Unlock Towards Data Science with a Free Account All articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more. Register for freeSign in to your account * * * ![](../images/body-6f351be10c9e83aa9f9d.jpeg) Written By ## [Ivo Bernardo](https://towardsdatascience.com/author/ivopbernardo/) [See all from Ivo Bernardo](https://towardsdatascience.com/author/ivopbernardo/) [Data Science](https://towardsdatascience.com/tag/data-science/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Nvidia](https://towardsdatascience.com/tag/nvidia/)[Polars Dataframe](https://towardsdatascience.com/tag/polars-dataframe/)[Python](https://towardsdatascience.com/tag/python/) Share This Article * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fpolars-nvidia-gpu-tutorial-483b83d7f899%2F&title=Polars%20%2B%20NVIDIA%20GPU%20Tutorial) * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fpolars-nvidia-gpu-tutorial-483b83d7f899%2F&title=Polars%20%2B%20NVIDIA%20GPU%20Tutorial) * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fpolars-nvidia-gpu-tutorial-483b83d7f899%2F&text=Polars%20%2B%20NVIDIA%20GPU%20Tutorial) Towards Data Science is a community publication. 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