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/.
24 lines
1.9 KiB
Markdown
24 lines
1.9 KiB
Markdown
---
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name: dgx-spark-portfolio-optimization
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description: GPU-Accelerated portfolio optimization using cuOpt and cuML — on NVIDIA DGX Spark. Use when setting up portfolio-optimization on Spark hardware.
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---
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<!-- GENERATED:BEGIN from nvidia/portfolio-optimization/README.md -->
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# Portfolio Optimization
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> GPU-Accelerated portfolio optimization using cuOpt and cuML
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This playbook demonstrates an end-to-end GPU-accelerated workflow using NVIDIA cuOpt and NVIDIA cuML to solve large-scale portfolio optimization problems, using the Mean-CVaR (Conditional Value-at-Risk) model, in near real-time.
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Portfolio Optimization (PO) involves solving high-dimensional, non-linear numerical optimization problems to balance risk and return. Modern portfolios often contain thousands of assets, making traditional CPU-based solvers too slow for advanced workflows. By moving the computational heavy lifting to the GPU, this solution dramatically reduces computation time.
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**Outcome**: You will implement a pipeline that provides tools for performance evaluation, strategy backtesting, benchmarking, and visualization. The workflow includes:
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- **GPU-Accelerated Optimization:** Leveraging NVIDIA cuOpt LP/MILP solvers
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- **Data-Driven Risk Modeling:** Implementing CVaR as a scenario-based risk measure that models tail risks without making assumptions about asset return distributions.
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- **Scenario Generation:** Using GPU-accelerated Kernel Density Estimation (KDE) via NVIDIA cuML to model return distributions.
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- **Real-World Constraint Management:** Implementing constraints including concentration limits, leverage constraints, turnover limits, and cardinality constraints.
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- **Comprehensive Backtesting:** Evaluating portfolio performance with specific tools for testing rebalancing strategies.
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**Full playbook**: `/Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/portfolio-optimization/README.md`
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<!-- GENERATED:END -->
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