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/.
1.1 KiB
| name | description |
|---|---|
| dgx-spark-nccl | Install and test NCCL on two Sparks — on NVIDIA DGX Spark. Use when setting up nccl on Spark hardware. |
NCCL for Two Sparks
Install and test NCCL on two Sparks
NCCL (NVIDIA Collective Communication Library) enables high-performance GPU-to-GPU communication across multiple nodes. This walkthrough sets up NCCL for multi-node distributed training on DGX Spark systems with Blackwell architecture. You'll configure networking, build NCCL from source with Blackwell support, and validate communication between nodes.
Outcome: You'll have a working multi-node NCCL environment that enables high-bandwidth GPU communication across DGX Spark systems for distributed training workloads, with validated network performance and proper GPU topology detection.
Duration: 30 minutes for setup and validation · Risk: Medium - involves network configuration changes
Full playbook: /Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/nccl/README.md