mirror of
https://github.com/NVIDIA/dgx-spark-playbooks.git
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a680d0472b
Adds a Claude Code plugin structure that exposes each NVIDIA DGX Spark
playbook as a triggerable skill, with an index skill ('dgx-spark') that
routes users to the right leaf based on intent and encodes the
relationship graph between playbooks (prerequisites, alternatives,
composes-with, upgrade paths).
Structure:
- overrides/*.md hand-curated frontmatter + Related sections
- scripts/generate.mjs zero-dep Node generator: nvidia + overrides → skills
- scripts/install.sh symlinks skills into ~/.claude/skills (--plugin mode available)
- skills/ committed, browsable, installable without Node
- .github/workflows/ auto-regenerates skills/ when playbooks/overrides change
Initial curated leaves: ollama, open-webui, vllm, connect-to-your-spark.
Remaining 37 leaves use generator fallback (title + tagline + summary
extracted from README) and can be curated incrementally via overrides/.
22 lines
1.6 KiB
Markdown
22 lines
1.6 KiB
Markdown
---
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name: dgx-spark-cuda-x-data-science
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description: Install and use NVIDIA cuML and NVIDIA cuDF to accelerate UMAP, HDBSCAN, pandas and more with zero code changes — on NVIDIA DGX Spark. Use when setting up cuda-x-data-science on Spark hardware.
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---
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<!-- GENERATED:BEGIN from nvidia/cuda-x-data-science/README.md -->
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# CUDA-X Data Science
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> Install and use NVIDIA cuML and NVIDIA cuDF to accelerate UMAP, HDBSCAN, pandas and more with zero code changes
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This playbook includes two example notebooks that demonstrate the acceleration of key machine learning algorithms and core pandas operations using CUDA-X Data Science libraries:
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- **NVIDIA cuDF:** Accelerates operations for data preparation and core data processing of 8GB of strings data, with no code changes.
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- **NVIDIA cuML:** Accelerates popular, compute intensive machine learning algorithms in sci-kit learn (LinearSVC), UMAP, and HDBSCAN, with no code changes.
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CUDA-X Data Science (formally RAPIDS) is an open-source library collection that accelerates the data science and data processing ecosystem. These libraries accelerate popular Python tools like scikit-learn and pandas with zero code changes. On DGX Spark, these libraries maximize performance at your desk with your existing code.
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**Outcome**: You will accelerate popular machine learning algorithms and data analytics operations GPU. You will understand how to accelerate popular Python tools, and the value of running data science workflows on your DGX Spark.
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**Full playbook**: `/Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/cuda-x-data-science/README.md`
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<!-- GENERATED:END -->
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