Google Cloud Partners’ Post

Get started with RAPIDS cuDF in seconds to accelerate your pandas code - by up to 50x, with zero code changes!

This is huge! GPU accelerated Pandas is now standard on Google Colab.

RAPIDS cuDF is now in Colab’s default GPU runtime! Just run "%load_ext cudf.pandas" with a GPU runtime and get immediate acceleration for your dataframe analytics. If you’re at #PyCon, join my talk about how we built cudf.pandas this Saturday at 10:30: https://lnkd.in/dTFpUt-t

RAPIDS cuDF Instantly Accelerates pandas up to 50x on Google Colab cuDF is a Python GPU DataFrame library (built on the Apache Arrow columnar memory format) for loading, joining, aggregating, filtering, and otherwise manipulating tabular data using a DataFrame style API in the style of pandas. cuDF now provides a pandas accelerator mode (cudf.pandas), allowing you to bring accelerated computing to your pandas workflows without requiring any code change. #GoogleIO #nvidiaai #googlecolab #python #pandas

Workflows on Google Colab: #cuDF Now Offers Up to 50x Faster Pandas.  Google Colab integrates with #RAPIDS cuDF, a library that supercharges your pandas code. This translates to up to 50x faster performance on Google Colab GPU instances, all without rewriting your existing code. Get started in seconds on #Colab: ➡ Switch to GPU runtime. ➡ Add %load_ext cudf.pandas. ➡ Swap import pandas as pd for import cudf as pd.

I became fond of Google Colab. It offers you an online powerful machine, free of charge, where you can run even heavy load codes. 😍 I successfully ported some of my local script so now anyone on Be A Bear can run from their own workstations without having to install the libraries. :) #dataanalytics #datascience #machinelearning #socialmedia

🚀 Exciting News for Data Scientists! 🚀 RAPIDS cuDF now seamlessly accelerates pandas code by up to 50x on Google Colab! With just a single command, you can harness the power of GPUs to speed up your data processing workflows without changing any pandas code. This integration, announced at Google I/O 2024, makes Colab an even more powerful tool for data science. Start accelerating your pandas code today and explore the future of data processing! Learn more: [NVIDIA Blog](https://lnkd.in/gDiCGwSV) #DataScience #MachineLearning #GPUComputing #GoogleColab #Pandas

#day2 #practicePython Arrays are the ordered linear container that has a fixed size where it stores same type of elements and starts traversing from the zero index. Arrays have some regular implementations. I have coded some of them and acknowledged some corner cases that needs to be in the bucket list. 1. We cannot change the size of an array, in order to resize it we have to create a new array and multiply it with a new length. 2. If we want to insert a particular element in an array, we have to check if it has enough memory to store it. Otherwise, it will return error. 3. We have to work with the left side to remove an element from an array. 4. In order to insert an element, we have to traverse from the right side of an array. 5. Time complexity in arrays are very significant, the less the time complexity, more efficient the code is. #datastructures #arrays #pythonprogramming Linear Array Functions. https://lnkd.in/gt-hbZm9

TensorFlow: A Deep Dive into its Applications TensorFlow, an open-source software library developed by the Google Brain team, has been making waves in the world of machine learning and artificial intelligence. Its flexible architecture allows for the easy deployment of computation across a variety of platforms, from desktops to clusters of servers, and even mobile and edge devices. But what exactly is TensorFlow, and how is it being used in real-world applications? Let's dive in and explore. https://lnkd.in/eVnj9JbP