RAPIDS: Difference between revisions

* '''cuDF''', a Python GPU DataFrame library (built on the Apache Arrow columnar memory format) for loading, joining, aggregating, filtering, and otherwise manipulating data.

* '''cuML''', a suite of libraries that implement machine learning algorithms and mathematical primitive functions that share compatible APIs with other RAPIDS projects.

* '''cuGraph''', a GPU accelerated graph analytics library, with functionality like NetworkX, which is seamlessly integrated into the RAPIDS data science platform.

* '''Cyber Log Accelerators (CLX or ''clicks'')''', a collection of RAPIDS examples for security analysts, data scientists, and engineers to quickly get started applying RAPIDS and GPU acceleration to real-world cybersecurity use cases.

* '''cuxFilter''', a connector library, which provides the connections between different visualization libraries and a GPU dataframe without much hassle. This also allows you to use charts from different libraries in a single dashboard, while also providing the interaction.

* '''cuSpatial''', a GPU accelerated C++/Python library for accelerating GIS workflows including point-in-polygon, spatial join, coordinate systems, shape primitives, distances, and trajectory analysis.

* '''cuSignal''', which leverages CuPy, Numba, and the RAPIDS ecosystem for GPU accelerated signal processing. In some cases, cuSignal is a direct port of Scipy Signal to leverage GPU compute resources via CuPy but also contains Numba CUDA kernels for additional speedups for selected functions.

* '''cuCIM''', an extensible toolkit designed to provide GPU accelerated I/O, computer vision & image processing primitives for N-Dimensional images with a focus on biomedical imaging.

* '''RAPIDS Memory Manager (RMM)''', a central place for all device memory allocations in cuDF (C++ and Python) and other RAPIDS libraries. In addition, it is a replacement allocator for CUDA Device Memory (and CUDA Managed Memory) and a pool allocator to make CUDA device memory allocation / deallocation faster and asynchronous.  

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To build a Singularity image for RAPIDS, the first thing to do is to find and select a Docker image provided by NVIDIA.

   

There are three types of RAPIDS Docker images: ''base'', ''runtime'', and ''devel''. For each type, multiple images are provided for different combinations of RAPIDS and CUDA versions, either on Ubuntu or on CentOS. You can find the Docker <tt>pull</tt> command for a selected image under the '''Tags''' tab on each site.   

It usually takes from thirty to sixty minutes to complete the image-building process. Since the image size is relatively large, you need to have enough memory and disk space on the server to build such an image.

Once you have a Singularity image for RAPIDS ready in your account, you can request an interactive session on a GPU node or submit a batch job to Slurm if you have your RAPIDS code ready.

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As there is no direct Internet connection on a compute node on Graham, you would need to set up an SSH tunnel with port forwarding between your local computer and the GPU node. See [[Jupyter#Connecting_to_Jupyter_Notebook|detailed instructions for connecting to Jupyter Notebook]].

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