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/.
1.9 KiB
| name | description |
|---|---|
| dgx-spark-portfolio-optimization | GPU-Accelerated portfolio optimization using cuOpt and cuML — on NVIDIA DGX Spark. Use when setting up portfolio-optimization on Spark hardware. |
Portfolio Optimization
GPU-Accelerated portfolio optimization using cuOpt and cuML
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.
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.
Outcome: You will implement a pipeline that provides tools for performance evaluation, strategy backtesting, benchmarking, and visualization. The workflow includes:
- GPU-Accelerated Optimization: Leveraging NVIDIA cuOpt LP/MILP solvers
- Data-Driven Risk Modeling: Implementing CVaR as a scenario-based risk measure that models tail risks without making assumptions about asset return distributions.
- Scenario Generation: Using GPU-accelerated Kernel Density Estimation (KDE) via NVIDIA cuML to model return distributions.
- Real-World Constraint Management: Implementing constraints including concentration limits, leverage constraints, turnover limits, and cardinality constraints.
- Comprehensive Backtesting: Evaluating portfolio performance with specific tools for testing rebalancing strategies.
Full playbook: /Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/portfolio-optimization/README.md