---

name: setup-and-backends description: "Install, inspect, and troubleshoot verl environments and accelerator backend stacks without constructing training commands." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

setup-and-backends

Use this sub-skill when a user needs verl installation guidance, environment inspection, accelerator/backend compatibility checks, Docker-vs-custom setup choices, or setup troubleshooting. Do not use it to build PPO/GRPO/SFT launch commands, rollout configs, checkpoint operations, or contribution-policy advice.

Route by setup question

• For base installs, Python/CUDA requirements, optional extras, Docker images, or CPU-only package inspection, read installation-and-backends.md.
• For vLLM, SGLang, Megatron-LM/MCore, TensorRT-LLM, GPU math/test extras, or platform plugin selection, read installation-and-backends.md.
• For failed imports, tensordict/torch/backend version mismatches, CUDA not detected, Ray visible-device behavior, NPU/ROCm notes, or stale optional stacks, read troubleshooting.md.
• To gather safe local evidence from the active Python environment, run check_verl_environment.py and inspect its JSON output.

Safe diagnostic helper

python sub-skills/setup-and-backends/scripts/check_verl_environment.py --pretty
python sub-skills/setup-and-backends/scripts/check_verl_environment.py --include-cuda --check-pip

The helper performs imports and package metadata checks only. It does not access the network, download models, launch Ray jobs, run Docker, or print local package file paths.

Boundaries

• Keep installation advice self-contained; do not point future agents to source checkout docs, examples, scripts, or tests as runtime dependencies.
• Treat CPU-only environments as valid for import/config inspection but not as evidence that vLLM, SGLang, Megatron, flash-attn, TensorRT-LLM, CUDA kernels, ROCm, or NPU runtime jobs work.
• Prefer Docker images for accelerator runtime bring-up; use custom Python environments only when Docker is incompatible with the target system.
• Escalate hardware-specific failures to targeted checks before changing training configs: CUDA/ROCm/NPU visibility, torch build, backend package versions, Ray resource environment variables, and optional extras.