Don’t be fooled by the past. In the rapidly evolving fields of data science and financial machine learning, faster computations and more efficient processing techniques are becoming increasingly important. These days, a new set of open source software libraries called RAPIDS is gaining popularity.
RAPIDS leverages the capabilities of the GPU to accelerate data science tasks. This post will go over every aspect of RAPIDS, including its libraries, hardware specifications, installation instructions, useful applications, and drawbacks. Last but not least, as usual, I will be offering a trading strategy based on the RAPIDS suite!
We cover:
Understanding the RAPIDS Libraries
A new approach to accelerating data science and machine learning processes is provided by the open source software libraries collectively known as RAPIDS. It is necessary to use all RAPIDS libraries to fully exploit the computing and data analysis capabilities of GPUs.
Let’s look at the main RAPIDS libraries here:
- cDF: A GPU-accelerated dataframe manipulation and operation tool, similar to Panda, but optimized for GPUs. It has a Panda-like user interface and speeds up processing through GPU parallelism.
- cuML: This library is used for machine learning tasks. It provides GPU-accelerated algorithms for various tasks such as clustering, regression, and classification. These algorithms are built to improve performance without compromising accuracy, which makes them suitable for use with large-scale datasets.
- cuPy: Similar in appearance to NumPy, cuPy is intended to be a GPU-accelerated array library that enables fast GPU array operations. It emulates the functionality of NumPy to seamlessly port array-based code to GPU architectures, increasing computational speed.
These libraries combine to create a single system that helps with data manipulation, analysis, and machine learning tasks, using the parallel processing power of GPUs. This speedup makes it possible to develop models and analyze data more quickly, which is useful for tasks involving large data sets. It also reduces processing times.
To get the most out of GPU-accelerated computing, researchers, machine learning experts, and data scientists need to understand the nuances of the RAPIDS libraries. These libraries provide high performance computing capabilities along with the ability to speed up and simplify a multitude of data processing tasks.
RAPIDS Libraries Installation Guide
RAPIDS libraries can be installed using the following steps:
Step 1: System Requirements
Confirm that your system meets the requirements before proceeding with the installation. It is imperative that you have a compatible GPU because the RAPIDS libraries are optimized for NVIDIA GPUs. It only works on Linux based operating systems. In case you have Windows, you can use WSL2 to have Ubuntu as a virtual machine. Make sure the version of Linux on your computer is supported (such as Ubuntu or CentOS). You also need to install NVIDIA drivers that are compatible with your GPU.
Step 2: Install Conda
Installing and managing the RAPIDS libraries requires the use of Conda, a package manager and environment manager. Installing Miniconda or Anaconda, two Python distribution platforms that support Conda, should be your first step.
Follow the installation instructions on the official website to download and install Miniconda or Anaconda.
For RAPIDS, create a new Conda environment to keep the setup neat and isolated. The following command can be used to create an environment named “Rapids” or any other desired name:
Step 3: Install the RAPIDS libraries
Use the following command to enable the Conda environment after it is created:
Then use the following command to install the RAPIDS libraries:
This command will install the RAPIDS suite in the specified Conda environment. rapids=0.21 refers to the version of RAPIDS being installed.
Step 4: Verify the installation
Once the installation process is complete, you can verify that the RAPIDS libraries have been successfully installed in your Conda environment. Open a Python interpreter in the Conda environment and import the desired libraries (eg cuDF, cuML, cuPy) to ensure they are accessible and working correctly.
If the import statements run without errors, it indicates successful installation of the RAPIDS libraries.
Practical Examples of the RAPIDS Libraries
Let’s figure out how to use the 3 libraries above. The examples will give a glimpse of what you can do with these libraries. As you will discover, they work very similarly to numpy, pandas and scikit-learn. So you won’t get confused at all while using them. They are easy to handle and you will start coding quickly.
Are you ready to have fun?
Let’s explore!
cuPy examples
Now we generate two random tables with 10,000 observations. Then we multiply them.
Example 1: In this example, we generate 10,000 random numbers and multiply them by dots to get a unique value as a result.
Example 2: Here we create two 2×2 matrices and calculate the multiplication of both. Then we print the resulting matrix.
Examples of cuDF
Example 1: Then we create a GPU based dataframe with 2 columns A and B and 3 observations each and sum both columns and store the result in column C. Simple as that, right?
Example 2: Here we create a pandas data frame which is taken with a dictionary. Next, we upload the pandas-based data frame to GPU memory using the cuff library. Then we print the data frame.
cuML examples
Example 1: We provide this example with two rolling arrays of 1000 random numbers each and use them to fit a k-means clustering algorithm with the cuml library. We then predict the feature labels according to the model.
Example 2: Finally, in this example, we create random input and prediction functions using the cuml library. Next, we split the data into train and test data and then run a random forest classifier on the data. Finally we predict the X-test data and show only 10 predictions.
Did you notice?
It’s like using CPU based libraries! So smooth coding, right?
A trading strategy that uses machine learning and GPUs
Using the RAPIDS libraries, one can design a trading strategy based on machine learning. By incorporating cuDF for data manipulation, cuML for predictive modeling and cuPy for arithmetic operations, a trader can develop a strategy based on historical market data, applying various machine learning algorithms for predictive analysis to make trading decisions.
Once we generate the signal, we get the cumulative returns for a buy and hold and the strategy.
Let’s look at the chart
We have good returns! But be careful! Always check strategy performance and cross-validate to verify the edge of your strategy.
Limitations of Modern Libraries
The limitations of these libraries can be listed as follows:
- By the time of the latest update in March 2024, RAPIDS has progressed significantly. Like any developing technology, it also has drawbacks, such as the fact that there are fewer algorithms implemented in cuML than in well-known CPU-based libraries such as scikit-learn.
- Additionally, its reliance on NVIDIA GPUs limits its applicability to computers without this technology.
- Watch out for reproducibility, n_streams equal to 1 makes the model reproducible, but a larger number will not.
- VRAM may not be enough for a complex machine learning model and data. Whenever there is a cuda memory error, you may need to reduce the complexity of the model or reduce the dimensions of the dataframe to make it run smoothly according to your hardware specifications.
conclusion
As a new collection of libraries, RAPIDS uses GPU acceleration for data science and machine learning activities. Although it has many features, it is important to be aware of several algorithmic limits as well as hardware requirements. However, RAPIDS’ continued development and community support point to a promising trajectory for the transformation of the field of data science.
Even with the limitations, we were able to create a trading strategy. Want to learn more about Python for transactions? Check out this comprehensive 6-lesson tutorial on Machine Learning and Deep Learning! You will find that there are ML and Deep Learning models to be applied to trading strategies. You can start using them with the Rapids library! Try it!
Ready to create your own strategy?
Go algo!
File to download:
- The_Rapids_AI_library (Python notebook)
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Author: Jose Carlos Gonzalez Tanaka
Disclaimer: All investing and trading in the stock market involves risk. Any decision to trade in the financial markets, including trading in stocks or options or other financial instruments, is a personal decision that should only be made after thorough investigation, including a personal risk and financial assessment and the engagement of professional assistance in degree you think necessary. The trading strategies or related information mentioned in this article are for informational purposes only.
