Introduction to cuML
Are you stuck with the slow processing of your algorithms? Here is what you require! cuML has introduced and established various efficient and fast algorithms that have paced the work of the data scientists and all machine learning enthusiasts who are indulged in the art of algorithm framing. This has accelerated the analytical tasks, providing efficiency, accuracy, and fit-predict-transform with ease and accuracy.
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We all design and implement various algorithms in our daily life. These algorithms play an essential role and are the key factors based on which our model is deployed. These algorithms run on the CPU (Central Processing Unit) of our system. But you might have noticed that when the algorithms are quite big or the input data or the training data that we take, becomes very big then the processing becomes slow. This implies that the processing gradually decreases with the increase in the amount of data.
This inverse relationship between processing and amount of data defines the performance of our program that we want to implement. The performance decreases as the processing time increases, so this is a big issue at a higher level that needs attention.
With the increase in technical development, data growth has been accelerated. This has increased the complexity of the model as the data increase brings the chances of an increase in error percentage. This increases the variance of the data set and with such big data values, further calculations and computations become cumbersome.
Data size grows due to various factors. The three most common factors that increase the data size while training an algorithm are overfitting, optimum, and underfitting. To give the model a perfect input data set and train it using all the possible values, the data size increases rapidly. This increase in data reduces the bias value that makes computation less efficient.
As this problem continues, data scientists face a lot of issues in sampling the huge amount of data as the data contains errors due to which its categorization and further processing aren’t possible. Removal of outliers is also a cumbersome task. Then the dimensional reduction and feature selection for such a huge amount of data takes a lot of time and may also result in some errors.
The final data representation in the form of a histogram or for giving as input to the algorithm depends on the preprocessing steps, mentioned above. If any of those steps is inefficient or has errors then the final output will be incorrect. Moreover, the CPU can’t take the load for this preprocessing for such a huge amount of data. One of the solutions for this problem is to iterate, cross-validate, and grid search the data and repeat these steps until the result is error-free.
But this is again a cumbersome task and may involve errors as the data set is high. This is a big problem faced by data scientists and machine learning engineers. For this, a solution is made that consists of explorations and iterations. This has been laid by the domain of machine learning. It involves the steps: iteration, cross-validation, and grid search in a loop over the phases: feature engineering, model training, and tuning and selection.
It accelerates the model training and addresses the whole problem, involving end-to-end acceleration.
In machine learning, there are various like Sklearn which enables one to train his model. Sklearn is one of the advanced and great libraries that helps in training the model. But for a large amount of data it may take time. We can do some changes in the Sklearn library and enable it for a training model consisting of large among of datasets.
But for hyperparameters and such a large amount of data, Sklearn isn’t preferred as the speed isn’t that much high even after the changes. There is another library that machine learning has provided, i.e., cuML. This helps in training the model 150 times faster than the Sklearn library (after necessary minimal changes).
If we will compare the two libraries so, for an input where Sklearn will take 20 seconds for processing, cuML takes less than 2 seconds for the same. This is the power of cuML. In cuML, the algorithm is processed directly on the GPU (Graphics Processing Unit).
The cuML is a machine learning library that provides fast and effective processing. It is GPU- accelerated. The increase in the memory of the graphics unit increases the speed of processing while using cuML. This is due to the reason that the cuML library is GPU-accelerated, and it directly processes the algorithm on the GPU to ensure fast processing.
The cuML trains the model, i.e., using a large number of input datasets, on the graphics processing unit. So, if we will triple the memory of the graphics unit the processing speed of cuML will increase by 18 times its initial speed, i.e., by using the single graphics unit.
So, if we are using cuML or any GPU-accelerated libraries then it is recommended to have a good GPU (graphics processing unit) in our computer to ensure acceleration at the ends.
This all shows that how effective and efficient is the output when we use the GPU-accelerated cuML machine learning library which directly does processing on the GPU. This has made the work of data scientists easier as the iteration, cross-validation, grid-search repeatedly over various phases has been eliminated and the relevant functions are been controlled by the cuML library.
Random Forest Classifiers
For classification and regression, we use the random forest technique. In this, our main aim is to reduce the overfitting of the data by making various independent decision trees. This also takes a huge amount of data and generates a large data value. For this, the cuML library is used to increase the speed of processing.
The cuML’s Random forest classifiers are the efficient and accelerated way for random forest technique. The training of cuML’s random forest classifier takes less than a second! Isn’t it amazing? Within less than a second, i.e., less than the time required to blink your eyes!
In such a short span, your random forest classifier would be ready for further processing. The same thing, i.e., random forest classification takes 30 or more seconds while using Sklearn library. This shows the power of the cuML library.
The speed of random forest classifiers increases with the better graphics cards as the cuML is dependent on the graphics processing unit. So, a better graphics card like Dell Precision 7740 laptop will make the cuML’s random forest classifier 158 times faster. But there is no effect of a better graphics card while using other machine learning libraries like Sklearn (that are not GPU-accelerated).
The cuDF, cuML, cuGraphic mimic are well-known libraries in open GPU data science. These libraries use various high-level APIs. The cuML library uses Scikit-Learn-Like API. The Dask cuML uses Dask Multi-GPU ML (based on Python). The Scikit-Learn-Like interface is used by data scientists utilizing cuDF and Numpy. cuDA C++ API is used by developers to utilize accelerated machine learning algorithms. Various reusable building blocks are used for composting machine learning algorithms. These are of high level and provide high accuracy.
There are several mathematical operations or functions for feature matrices provided by GPU-accelerated libraries. These include Linear Algebra, Statistics, Matrix / Math, Random, Distance / Metrics, Objective Functions, Sparse Conversions, Cross-Validation, Hyper-parameter Tuning, Classification / Regression (random forest classifiers), Clustering, Decomposition, and Dimensionality Reduction, Timeseries Forecasting, Recommendations, and much more!
In brief, these include Decision Trees / Random Forests, Linear Regression, Logistic Regression, K-Nearest Neighbors, Kalman Filtering, Bayesian Inference, Gaussian Mixture Models, K-Means, DBSCAN, Spectral Clustering, Principal Components, Singular Value Decomposition, UMAP, Spectral Embedding, ARIAM, Holt-Winters, Implicit Matrix Factorization, Element-wise Operations, Matrix Multiply, Norms, Eigen Decomposition, SVD / RSVD, Transpose, QR Decomposition, and much more.
By all the above-mentioned mathematical operations and functions it is clear that the cuML library supports various high-level mathematical tasks in an easy and accelerated manner. These help the data scientists to perform the preprocessing tasks like sampling, clustering, error reduction, filtering, distribution of data, classification, representation, and al other tasks efficiently. This transforms the cumbersome task into a piece of cake!
Various tasks ranging from regression to matrix operations, tuning to forecasting, and recommendations, everything takes place smoothly and within 2 to 3 seconds. Isn’t it amazing? This computational speed has resolved the issue of taking a large dataset as input.
Till now, we learned about the cuML machine learning, GPU-accelerated library, its functions, and various features that it provides to make the task of data analysts and data scientists easy. It is better than various other machine learning libraries like Sklearn (that doesn’t depend on GPU directly). We have seen that how it increase the speed of the random forest classification technique 18 times and more than 100 times when we use a better graphics card.
Overall, we learned the benefits of using the cuML library and its advantages. Hope you have understood the concept thoroughly.
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