The engine of modern businesses is fuelled by data science: every field, from retail to financial services to healthcare, extracts insight from data to enhance productivity and organizational effectiveness. To reduce the cost of surplus inventory, retailers are enhancing forecasting. Financial services institutions are detecting fraudulent transactions. The risk of illness is expected more quickly by healthcare providers. Also, minor changes in the accuracy of predictive machine learning models for the bottom line will translate into billions. NVIDIA’s RAPIDS-accelerated data science solution helps data science developers tap into GPU-accelerated machine learning ( ML) with quicker model iteration, improved prediction accuracy, and the lowest total ownership ( TCO) for data science.
Let’s discuss more about rapids.
Blog Contents
- What is Rapids?
- The limitations of traditional workflow in machine learning
- GPU Acceleration with Rapids
- Getting super speed with Rapids GPU
- Breakthrough performance for machine learning workflows
- Accelerated Data Science Solution
- Features of Rapids
- Boosting Data Science Performance
- Conclusion
What is Rapids?
RAPIDS, a suite of open-source software libraries used entirely on GPUs to execute end-to-end data science and analytics pipelines. RAPIDS seeks to speed up the entire data science pipeline, including data loading, ETL, model training, and inference. This will allow workflows that are more productive, collaborative, and exploratory.
RAPIDS is the machine learning community’s product and GPU Open Analytics Initiative (GOAI) partners’ contributions. GOAI developed the GPU DataFrame based on Apache Arrow data structures built-in 2017 to accelerate end-to-end analytics and data science pipelines on GPUs. Without standard serialization and deserialization penalties, the GPU DataFrame made it possible to integrate GPU-accelerated data processing and machine learning libraries. RAPIDS builds on the earlier GOAI work and expands it.
The limitations of traditional workflow in machine learning
Creating ML models also included days spent on ingesting and preparing data, weeks spent on data-based engineering functionality. Potentially months spent assessing effectiveness and model selection for output inference in scoring ML pipelines. Using parallel production pipelines to achieve even simple results, this iterative process must be carried out repeatedly. The inefficient workflow generates a continuous downtime period for data science experts as they wait for insufficient, underpowered CPU-based resources to ingest and prepare data, train models, and evaluate outcomes.
GPU Acceleration with Rapids
Rapids is a set of software libraries created to use GPUs to accelerate data science. For fast, GPU-optimized algorithm implementations, it uses low-level CUDA code while still having a Python layer on top that is simple to use.
Rapids’ uniqueness is that it is entirely incorporated with Data Science libraries, and stuff like Pandas data frames are quickly passed through for GPU acceleration to Rapids.
Rapids leverage some Python libraries:
- Python GPU DataFrames, cuDF: In terms of data handling and manipulation, it can do almost all that Pandas can.
- CuML: Machine Learning Python GPU. It contains several of the ML algorithms, all in a very similar format, that Scikit-Learn has.
- CuGraph: Python processing of GPU graphs. It includes several popular algorithms for graph analytics, including PageRank and different metrics of similarity.
Getting super speed with Rapids GPU
The amount of speed we receive from Rapids depends on the amount of data we process. A strict rule of thumb is that GPU acceleration would benefit from more massive datasets. There is some overhead time associated with data transfer between the CPU and GPU. With more massive datasets, overhead time becomes more ‘worth it’.
Breakthrough performance for machine learning workflows
NVIDIA’s accelerated data science solution with RAPIDS greatly surpasses CPU-based ML environments in comparative testing of real-world data sets through the ML workflow, from data loading to model training, providing substantially better performance than hundreds of CPU nodes, using the power of only one NVIDIA DGX-2.
Accelerated Data Science Solution
A new, growing library of GPU-accelerated ML algorithms (cuML) is also being introduced by RAPIDS, including the most common algorithms such as XGBoost (a gradient boosted decision tree), as well as Kalman, K-means, KNN, DBScan, PCA, TSVD, OLS Linear Regression, Kalman Filtering, and more. ML algorithms incur a large amount of data movement that has been difficult to parallelize until now. Model training can now easily be spread through multiple GPUs and multiple nodes (systems) with negligible latency, bypassing the IO bottleneck between CPU and memory with the advent of GPU-accelerated ML and NVIDIA NVLinkTM and NVSwitch architectures found in NVIDIA ® DGX and HGX systems.
Features of Rapids
- Hassle-Free Integration: With minimal code updates and no new tools to learn, accelerate your Python data science toolchain.
- Top Model Accuracy: Increase the machine learning algorithm’s precision by iterating more rapidly on models and deploying them more regularly.
- Reduced Training time: With near-interactive data science, dramatically increase your productivity.
- Source Accessible: Customizable, extensible, interoperable-NVIDIA supports open-source software and is based on Apache Arrow.
Boosting Data Science Performance
On standard end-to-end data science workflows, RAPIDS achieves speed-up factors of 50x or more. For high-performance GPU execution, RAPIDS utilizes NVIDIA CUDA, exposing the GPU’s parallelism and high memory bandwidth through user-friendly Python interfaces. RAPIDS focuses on popular analytics and data science data preparation activities, providing a robust and familiar DataFrame API. This API integrates with a range of machine learning algorithms without paying for traditional serialization costs, allowing end-to-end pipelines to accelerate. RAPIDS also supports multi-node, multi-GPU deployments, allowing much larger sizes of datasets to be scaled up and out. A notebook and code showing a standard end-to-end ETL and ML workflow are included in the RAPIDS container.
Conclusion
From data ingestion and manipulation to machine learning training, RAPIDS accelerates the full data science pipeline. RAPIDS is for everyone to use: consumers, adopters, and contributors. RAPIDS projects are almost growing in replacements that can speed up the end-to-end workflow up to 50x, whether you are a data scientist, researcher, engineer, or AI developer using pandas, Dask, sci-kit-learn, or Spark on CPUs.
