spark-environment-setup
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
Profile →npx skills add wshobson/agents --skill spark-environment-setup --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Spark Environment Setup DGX Spark ships a GB10 Grace Blackwell chip: aarch64 CPU, SM121 GPU, 128GB unified memory, CUDA 13. This is a narrower and younger platform than a standard x86 CUDA 12 box, so package selection and ABI matching matter more than usual — the wheel ecosystem for aarch64 + CUDA 13 is still filling in. ## When to Use This Skill - Setting up a fresh Spark box for training or inference. - Hitting an import error mentioning `libcudart`, a missing symbol, or a wheel that "installed fine but won't load." - A framework install (PyTorch, Unsloth, TRL, vLLM, xformers) fails, hangs, or silently falls back to CPU. - Deciding whether to use an NGC container or bare pip. - Restoring a working setup after an OS reinstall or a base-image update, needing to re-verify from scratch. Each of these accepts the same general fix: match the container/wheel combination to CUDA 13 and SM121, don't fight the ABI. ## Container-First Rule Quick decision, before the detail below: - Standard training/inference work → NGC PyTorch container. - Unsloth-centric fine-tuning → Unsloth container (it ships the pinned Triton/xformers/transformers combination already validated for that path). - Neit
- When to Use This Skill
- Container-First Rule
- The ABI Rule
- Component Quick Table
- Verification Commands
- Next Steps
docker run --runtime=nvidia --gpus all -it --rm \ pip install "transformers==5.13.1" "peft==0.19.1" "hf_transfer==0.1.9" "datasets==4.3.0" "trl==1.8.0" pip install --no-deps "unsloth==2026.7.2" "unsloth_zoo==2026.7.2" "bitsandbytes==0.49.2" pip install -U "torchao==0.17.0" python3 -c "import torch; print(torch.version.cuda)"
What does the spark-environment-setup skill do?
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
How do I install it?
Run `npx skills add wshobson/agents --skill spark-environment-setup --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From wshobson/agents, a repository with 38,479 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.