Speed Up Data Analytics on GPUs
Learn how to accelerate analytics for large data sets on Google Cloud by tapping into NVIDIA GPUs – no code changes required. Go back
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Optimize your analytics workflows with GPUs
Slow data processing times can be a big bottleneck, especially when dealing with the large datasets required to train modern AI models. This video introduces GPU-accelerated data analytics workflows on Google Cloud, allowing you to quickly transform large datasets and generate faster insights.
Get started with accelerated data analytics using NVIDIA cuDF
This hands-on lab demonstrates how to use NVIDIA cuDF within a Google Cloud Colab Enterprise environment to dramatically accelerate `pandas` data analytics workflows with zero code changes.
- Set up a cloud environment: Configure and connect to a GPU-accelerated runtime using Colab Enterprise runtime templates.
- Accelerate pandas instantly : Use cuDF to accelerate pandas code without modification.
- Benchmark performance : Run a data analytics pipeline on both CPU and GPU to quantify and visualize the speedups.
- Integrate with cloud storage : Practice reading and writing large Parquet datasets at high speed directly from and to Google Cloud Storage (GCS).
Fixing pandas memory errors: 3 easy solutions
"Out of Memory" (OOM) errors are a common roadblock; they happen when pandas tries to load a file that consumes more RAM than your system has available. This blog covers three solutions to the issue: Swap Space is a simple fix that uses your slow hard drive as extra memory, which allows you to load the full dataset, but impacts performance. Sampling is a very fast, memory-light option for quick analysis, but it only uses a portion of your data, sacrificing full fidelity. NVIDIA cuDF with UVM uses the GPU and CPU RAM together to process the entire dataset at high speed without data loss.
