{ "source_file": "0930", "source_platform": "Medium", "author": "Ivo Bernardo", "title_listed": "Polars + NVIDIA GPU Tutorial", "keywords": "", "domain": "数据处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/polars-nvidia-gpu-tutorial-483b83d7f899/", "publish_date": "2024-09-17", "corpus_stage": "", "notes": "", "id": "2dab5d32f609", "collected_at": "2026-10-02T05:56:08", "title": "Polars + NVIDIA GPU Tutorial | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. 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The language is not tailored for handling huge amounts of data as native SQL systems or spark.\nThe 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.\nOne 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:\n * It's written in Rust\n * It leverages multi-threading automatically\n * It defers most calculations by using lazy evaluation\n\n\nAnd.. after today, you can now leverage NVIDIA's hardware to maximize _polars_ ' GPU engine capabilities.\nIn this blog post, we'll see how you can leverage _polars+GPU_ and speed up your data pipelines enormously.\n* * *\nLearn 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.\n## Setting Up the Environment\nTo follow along this blog post, you can check the accompanying notebook [here](https://colab.research.google.com/drive/1Ety47AG_s8JWJCsOUDYoiAwmeILsSG0x?usp=sharing).\nFirst, let's install `polars` with GPU capabilities by using:\nperl\n\n```\n!pip install polars[gpu] --extra-index-url=https://pypi.nvidia.com\n```\n\nWe 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.\nAs 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:\nruby\n\n```\n!wget https://storage.googleapis.com/rapidsai/polars-demo/transactions-t4-20.parquet -O transactions.parquet\n```\n\nBut, if you have your own system and resources that can handle the size of the total data, feel free to download the original file:\nruby\n\n```\n!wget https://storage.googleapis.com/rapidsai/polars-demo/transactions.parquet -O transactions.parquet\n```\n\n![Preview of Transactions Table - Image by Author](../images/001.png)\nPreview of Transactions Table - Image by Author\nFinally, let's load `polars` :\njavascript\n\n```\nimport polars as pl\nfrom polars.testing import assert_frame_equal\n```\n\nSetup is done, time to play around with our dataset!\n* * *\n## Simple Calculations\nWe need to load our dataset into our `polars`engine by leveraging the `scan_parquet` method:\nini\n\n```\ntransactions = pl.scan_parquet(\"transactions.parquet\")\n```\n\nWe can take a peak at our first 5 transactions using `head` and `collect` :\nscss\n\n```\ntransactions.head(5).collect()\n```\n\n![Transactions Table in Pandas - Image by Author](../images/002.png)\nTransactions Table in Pandas - Image by Author\nThe 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:\nless\n\n```\n%%time\ntransactions.select(pl.col(\"AMOUNT\").sum()).collect()\n```\n\n![Aggregate Execution - Image by Author](../images/003.png)\nAggregate Execution - Image by Author\nCPU 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.\nLet's see how this operation works on the `gpu_engine` :\ngraphql\n\n```\ngpu_engine = pl.GPUEngine(\n device=0,\n raise_on_fail=True,\n)\n```\n\nless\n\n```\n%%time\ntransactions.select(pl.col(\"AMOUNT\").sum()).collect(engine=gpu_engine)\n```\n\n![Aggregate Execution with gpu_engine - Image by Author](../images/004.png)\nAggregate Execution with gpu_engine - Image by Author\nNice improvement! CPU time was ~4 times faster. Will this stand for complex calculations?\n* * *\n## Using Group By + Aggregations\nOne 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.\nFor example, which customers are the highest spenders in our dataset? Let's calculate that using the CPU:\nsql\n\n```\n%%time\n\nhigh_spenders_cpu = (\n transactions\n .group_by(\"CUST_ID\")\n .agg(pl.col(\"AMOUNT\").sum())\n .sort(by=\"AMOUNT\", descending=True)\n .head()\n .collect()\n)\nhigh_spenders_cpu\n```\n\nThe `cpu_time` for this operation was (drumroll):\n![Group By Aggregation Operation - Image by Author](../images/005.png)\nGroup By Aggregation Operation - Image by Author\n4.27 seconds, not bad. But what if we do this operation on our _GPU_?\nsql\n\n```\n%%time\n\nhigh_spenders_gpu = (\n transactions\n .group_by(\"CUST_ID\")\n .agg(pl.col(\"AMOUNT\").sum())\n .sort(by=\"AMOUNT\", descending=True)\n .head()\n .collect(engine=gpu_engine)\n)\nhigh_spenders_gpu\n```\n\n![Group By Aggregation Operation on GPU Engine— Image by Author](../images/006.png)\nGroup By Aggregation Operation on GPU Engine— Image by Author\n _345 ms_ for an aggregation on a 52 million row dataframe. That's actually a great result!\nAnd what's nice is that we can also rely on polars `SQL` interface to achieve similar results:\npython\n\n```\nsql_query = \"\"\"\nSELECT CUST_ID, SUM(AMOUNT) as sum_amt\nFROM transactions\nGROUP BY CUST_ID\nORDER BY sum_amt desc\nLIMIT 5\n\"\"\"\n\n%time pl.sql(sql_query).collect()\n%time pl.sql(sql_query).collect(engine=gpu_engine)\n```\n\n![Group By Aggregation Operation with Polars SQL interface— Image by Author](../images/007.png)\nGroup By Aggregation Operation with Polars SQL interface— Image by Author\n* * *\nIn 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.\nStandard `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:\n * The fastest performance for Polars is on NVIDIA GPUs, up to 10x speed up over CPUs\n * The Polars GPU engine enables users to process 100s of millions of rows of data in seconds\n * Access to the Polars GPU engine requires almost zero code changes to existing Polars code\n\n\nYou 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).\nLast, 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!\nIf you want to read / see more content related to AI and DS, subscribe my youtube Channel \"_The Data Journey_ \":\n![](../images/body-31d2ad1a8620f4bd994d.png)\n\n[_[The dataset used in this blog post is under licence CC0: Public Domain]_](https://www.kaggle.com/datasets/conorsully1/simulated-transactions)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[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/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Polars + NVIDIA GPU Tutorial\nUsing Polars with NVIDIA GPU can speed up your data pipelines\n[Ivo Bernardo](https://towardsdatascience.com/author/ivopbernardo/)\nSeptember 17, 20244 min read\n![Image by noaa @ Unsplash.com](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/09/0o53uAJaEv4EVDQ0u-scaled.jpg)\nImage by [noaa](https://unsplash.com/pt-br/@noaa) @ Unsplash.com\nDealing 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.\nThe 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.\nOne 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:\n * It's written in Rust\n * It leverages multi-threading automatically\n * It defers most calculations by using lazy evaluation\n\n\nAnd.. after today, you can now leverage NVIDIA's hardware to maximize _polars_ ' GPU engine capabilities.\nIn this blog post, we'll see how you can leverage _polars+GPU_ and speed up your data pipelines enormously.\n* * *\nLearn 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.\n## Setting Up the Environment\nTo follow along this blog post, you can check the accompanying notebook [here](https://colab.research.google.com/drive/1Ety47AG_s8JWJCsOUDYoiAwmeILsSG0x?usp=sharing).\nFirst, let's install `polars` with GPU capabilities by using:\nperl\n\n```\n!pip install polars[gpu] --extra-index-url=https://pypi.nvidia.com\n```\n\nWe 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.\nAs 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:\nruby\n\n```\n!wget https://storage.googleapis.com/rapidsai/polars-demo/transactions-t4-20.parquet -O transactions.parquet\n```\n\nBut, if you have your own system and resources that can handle the size of the total data, feel free to download the original file:\nruby\n\n```\n!wget https://storage.googleapis.com/rapidsai/polars-demo/transactions.parquet -O transactions.parquet\n```\n\n![Preview of Transactions Table - Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/09/1aDGUbeAn_lkGFIdgYDSPpw.png)\nPreview of Transactions Table - Image by Author\nFinally, let's load `polars` :\njavascript\n\n```\nimport polars as pl\nfrom polars.testing import assert_frame_equal\n```\n\nSetup is done, time to play around with our dataset!\n* * *\n## Simple Calculations\nWe need to load our dataset into our `polars`engine by leveraging the `scan_parquet` method:\nini\n\n```\ntransactions = pl.scan_parquet(\"transactions.parquet\")\n```\n\nWe can take a peak at our first 5 transactions using `head` and `collect` :\nscss\n\n```\ntransactions.head(5).collect()\n```\n\n![Transactions Table in Pandas - Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/09/1Ey7DF2gkMdwgiBlTRj0Ibw.png)\nTransactions Table in Pandas - Image by Author\nThe 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:\nless\n\n```\n%%time\ntransactions.select(pl.col(\"AMOUNT\").sum()).collect()\n```\n\n![Aggregate Execution - Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/09/1GXbd6vHAA5AkIti2UHXpBw.png)\nAggregate Execution - Image by Author\nCPU 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.\nLet's see how this operation works on the `gpu_engine` :\ngraphql\n\n```\ngpu_engine = pl.GPUEngine(\n device=0,\n raise_on_fail=True,\n)\n```\n\nless\n\n```\n%%time\ntransactions.select(pl.col(\"AMOUNT\").sum()).collect(engine=gpu_engine)\n```\n\n![Aggregate Execution with gpu_engine - Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/09/1zxizLTfO7upyyb1IeAQ23A.png)\nAggregate Execution with gpu_engine - Image by Author\nNice improvement! CPU time was ~4 times faster. Will this stand for complex calculations?\n* * *\n## Using Group By + Aggregations\nOne 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.\nFor example, which customers are the highest spenders in our dataset? Let's calculate that using the CPU:\nsql\n\n```\n%%time\n\nhigh_spenders_cpu = (\n transactions\n .group_by(\"CUST_ID\")\n .agg(pl.col(\"AMOUNT\").sum())\n .sort(by=\"AMOUNT\", descending=True)\n .head()\n .collect()\n)\nhigh_spenders_cpu\n```\n\nThe `cpu_time` for this operation was (drumroll):\n![Group By Aggregation Operation - Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/09/1ovU51hw3vMi0iC_fmqT7SQ.png)\nGroup By Aggregation Operation - Image by Author\n4.27 seconds, not bad. But what if we do this operation on our _GPU_?\nsql\n\n```\n%%time\n\nhigh_spenders_gpu = (\n transactions\n .group_by(\"CUST_ID\")\n .agg(pl.col(\"AMOUNT\").sum())\n .sort(by=\"AMOUNT\", descending=True)\n .head()\n .collect(engine=gpu_engine)\n)\nhigh_spenders_gpu\n```\n\n![Group By Aggregation Operation on GPU Engine— Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/09/1iNfYaMWY53AgrAzT6Wn0og.png)\nGroup By Aggregation Operation on GPU Engine— Image by Author\n _345 ms_ for an aggregation on a 52 million row dataframe. That's actually a great result!\nAnd what's nice is that we can also rely on polars `SQL` interface to achieve similar results:\npython\n\n```\nsql_query = \"\"\"\nSELECT CUST_ID, SUM(AMOUNT) as sum_amt\nFROM transactions\nGROUP BY CUST_ID\nORDER BY sum_amt desc\nLIMIT 5\n\"\"\"\n\n%time pl.sql(sql_query).collect()\n%time pl.sql(sql_query).collect(engine=gpu_engine)\n```\n\n![Group By Aggregation Operation with Polars SQL interface— Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/09/15OUbniGzw1FflcPRhYEfkg.png)\nGroup By Aggregation Operation with Polars SQL interface— Image by Author\n* * *\nIn 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.\nStandard `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:\n * The fastest performance for Polars is on NVIDIA GPUs, up to 10x speed up over CPUs\n * The Polars GPU engine enables users to process 100s of millions of rows of data in seconds\n * Access to the Polars GPU engine requires almost zero code changes to existing Polars code\n\n\nYou 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).\nLast, 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!\nIf you want to read / see more content related to AI and DS, subscribe my youtube Channel \"_The Data Journey_ \":\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/09/0zBWFEtepUfMpdOYj.png)\n\n[_[The dataset used in this blog post is under licence CC0: Public Domain]_](https://www.kaggle.com/datasets/conorsully1/simulated-transactions)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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