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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

RAPIDS: Use GPU to Accelerate ML Models Easily

Soumya Sasmal
Soumya Sasmal Last Updated : 07 Jul, 2023
6 min read

Introduction

As Artificial Intelligence (AI) grows continuously, the demand for faster and more efficient computing power is increasing. Machine learning (ML) models can be computationally intensive, and training the models can take longer. However, by using GPU parallel processing capabilities, it is possible to accelerate the training process significantly. Data scientists can iterate faster, experiment with more models, and build better-performing models in less time.

RAPIDS | GPU | ML Models

There are several libraries available to use. Today we will learn about RAPIDS, an easy solution to use our GPU to accelerate ML models without any knowledge of GPU programming.

Learning Objectives

In this article, we will learn about:

  • A high-level overview of how RAPIDS.ai works
  • Libraries found in RAPIDS.ai
  • Using these libraries
  • Installation and System Requirements

This article was published as a part of the Data Science Blogathon.

Table of contents

RAPIDS.AI

RAPIDS is a suite of open-source software libraries and APIs for executing data science pipelines entirely on GPUs. RAPIDS provides exceptional performance and speed with familiar APIs that match the most popular PyData libraries. It is developed on NVIDIA CUDA and Apache Arrow, which is the reason behind its unparalleled performance.

How does RAPIDS.AI work?

RAPIDS  uses GPU-accelerated machine learning to speed up data science and analytics workflows. It has a GPU-optimized core data frame that helps build databases and machine learning applications and is designed to be similar to Python. RAPIDS offers a collection of libraries for running a data science pipeline entirely on GPUs. It was created in 2017 by the GPU Open Analytics Initiative (GoAI) and partners in the machine learning community to accelerate end-to-end data science and analytics pipelines on GPUs using a GPU Dataframe based on the Apache Arrow columnar memory platform. RAPIDS also includes a Dataframe API that integrates with machine learning algorithms.

Faster Data Access with Less Data Movement

Hadoop had limitations in handling complex data pipelines efficiently. Apache Spark addressed this issue by keeping all data in memory, allowing for more flexible and complex data pipelines. However, this introduced new bottlenecks, and analyzing even a few hundred gigabytes of data could take a long time on Spark clusters with hundreds of CPU nodes. To fully realize the potential of data science, GPUs must be at the core of data center design, including five elements: computing, networking, storage, deployment, and software. In general, end-to-end data science workflows on GPUs are 10 times faster than on CPUs.

Source: https://www.nvidia.com/en-in/deep-learning-ai/software/rapids

Libraries

We will learn about 3 libraries in the RAPIDS ecosystem.

  • cuDF
  • cuML
  • cuGraph

cuDF: A Faster Pandas Alternative

cuDF is a GPU DataFrame library alternative to the pandas’ data frame. It is built on the Apache Arrow columnar memory format and offers a similar API to pandas for manipulating data on the GPU. cuDF can be used to speed up pandas’ workflows by using the parallel computation capabilities of GPUs. It can be used for tasks such as loading, joining, aggregating, filtering, and manipulating data.

cuDF is an easy alternative to Pandas DataFrame in terms of programming also.

import cudf

# Create a cuDF DataFrame
df = cudf.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})

# Perform some basic operations
df['c'] = df['a'] + df['b']
df = df.query('c > 4')

# Convert to a pandas DataFrame
pdf = df.to_pandas()

Using cuDF is also easy, as you must replace your Pandas DataFrame object with a cuDF object. To use it, we just have to replace “pandas” with “cudf” and that’s it. Here’s an example of how you might use cuDF to create a DataFrame object and perform some operations on it:

cuML: A Faster Scikit Learn Alternative

cuML is a collection of fast machine learning algorithms accelerated by GPUs and designed for data science and analytical tasks. It offers an API similar to sci-kit-learn’s, allowing users to use the familiar fit-predict-transform approach without knowing how to program GPUs.

Like cuDF, using cuML is also very easy for anyone to understand. A code snippet is provided for example.

import cudf
from cuml import LinearRegression

# Create some example data
X = cudf.DataFrame({'x': [1, 2, 3, 4, 5]})
y = cudf.Series([2, 4, 6, 8, 10])

# Initialize and fit the model
model = LinearRegression()
model.fit(X, y)

# Make predictions
predictions = model.predict(X)
print(predictions)

You can see I have replaced the “sklearn” with “cuml” and “pandas” with “cudf” and that’s it. Now this code will use GPU, and the operations will be much faster.

cuGRAPH: A Faster Networkx alternative

cuGraph is a library of graph algorithms that seamlessly integrates into the RAPIDS data science ecosystem. It allows us to easily call graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or even CuPy or SciPy sparse Matrices. It offers scalable performance for 30+ standard algorithms, such as PageRank, breadth-first search, and uniform neighbor sampling.

Like cuDf and cuML, cuGraph is also very easy to use.

import cugraph
import cudf

# Create a DataFrame with edge information
edge_data = cudf.DataFrame({
    'src': [0, 1, 2, 2, 3],
    'dst': [1, 2, 0, 3, 0]
})

# Create a Graph using the edge data
G = cugraph.Graph()
G.from_cudf_edgelist(edge_data, source='src', destination='dst')

# Compute the PageRank of the graph
pagerank_df = cugraph.pagerank(G)

# Print the result
print(pagerank_df)#

Yes, using cuGraph is this simple. Just replace “networkx” with “cugraph” and that’s all.

Requirements

Now the best part of using RAPIDS is, you don’t need to own a professional GPU. You can use your gaming or notebook GPU if it matches the system requirements.

To use RAPIDS, it is necessary to have the minimum system requirements.

Installation

Now, coming to installation, please check the system requirements, and if it matches, you are good to go.

Go to this link, select your system, choose your configuration, and install it.

Download link: https://docs.rapids.ai/install

Performance Benchmarks

The below picture contains a performance benchmark of cuDF and Pandas for Data Loading and  Manipulation of the “California road network dataset.” You can check out more about the code from this website: https://arshovon.com/blog/cudf-vs-df.

Performance benchmarks | RAPIDS | GPU | ML Models

You can check all the benchmarks by visiting the official website: https://rapids.ai.

Experience Rapids in Online Notebooks

Rapids has provided several online notebooks to check out these libraries. Go to https://rapids.ai to check all these notebooks.

Advantages

Some benefits of RAPIDS are :

  • Minimal code changes
  • Acceleration using GPU
  • Faster model deployment
  • Iterations to increase machine learning model accuracy
  • Improve data science productivity

Conclusion

RAPIDS is a collection of open-source software libraries and APIs that allows you to execute end-to-end data science and analytics pipelines entirely on NVIDIA GPUs using familiar PyData APIs. It can be used without any hassles or need for GPU programming, making it much easier and faster.

Here is a summary of what we have learned so far:

  • How can we significantly use our GPU to accelerate ML models without GPU programming?
  • It is a perfect alternative to various widely available libraries like Pandas, Scikit-Learn, etc.
  • To use RAPIDS.ai, we just have to change some minimal code.
  • It is faster than traditional CPU-based ML model training.
  • How to install RAPIDS.ai in our system.

For any questions or feedback, you can email me at: [email protected]

Frequently Asked Questions

Q1. What is RAPIDS.ai?

A. RAPIDS.ai is a suite of open-source software libraries that enables end-to-end data science and analytics pipelines to be executed entirely on NVIDIA GPUs using familiar PyData APIs.

Q2. What are the features of RAPIDS.ai?

A. RAPIDS.ai offers a collection of libraries for running a data science pipeline entirely on GPUs. These libraries include cuDF for DataFrame processing, cuML for machine learning, cuGraph for graph processing, cuSpatial for spatial analytics, and more.

Q3. How does RAPIDS.ai compare to other data science tools?

A. RAPIDS.ai offers significant speed improvements over traditional CPU-based data science tools by leveraging the parallel computation capabilities of GPUs. It also offers seamless integration with minimal code changes and familiar APIs that match the most popular PyData libraries.

Q4. Is RAPIDS.ai easy to learn?

A. Yes, It is very easy and very similar to other libraries. You just need to make some minimal changes in your Python code.

Q5. Can RAPIDS.ai be used in AMD GPU?

A. No, As AMD GPU doesn’t have CUDA cores, we can’t use Rapids.AI in AMD GPU.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

AIAI modelsblogathonGPULibrariesML ModelsModelspandasRAPIDS
Soumya Sasmal
Soumya Sasmal

Soumya Sasmal is an experienced data analytics consultant with 1 year of experience in developing data insights and strategies. He is skilled in data analysis, data science, and machine learning. Soumya has a Bachelor of Engineering in Instrumentation and Electronics Engineering from Jadavpur University.

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