SSH into h100_sglang to attach to the sglang_bbuf container for GPU-enabled SGLang development.
npx skills add https://github.com/BBuf/AI-Infra-Auto-Driven-SKILLS --skill h100
Please help me install this Agent Skill. Skill: h100 Source: https://github.com/BBuf/AI-Infra-Auto-Driven-SKILLS/tree/main/skills/h100 Command: npx skills add https://github.com/BBuf/AI-Infra-Auto-Driven-SKILLS --skill h100
SSH into the H100 remote environment to enable GPU-backed SGLang development when local hardware lacks CUDA resources, using the preconfigured container and workspace for reproducible runs.
Connect to h100_sglang, attach to the sglang_bbuf container, and begin GPU-enabled development in /sgl-workspace/sglang.
High-intent search queries and answers about installing and using this skill.
Remote GPU-enabled SGLang development solves local CUDA hardware limitations by SSHing into an H100 host, attaching to a preconfigured Docker container, and accessing the mounted SGLang repository workspace for reproducible runs.
Yes, CUDA and diffusion validation can be executed on remote H100 hardware by connecting to the host via SSH, attaching to the sglang_bbuf Docker container, and running GPU-bound smoke tests safely in the isolated environment.
Remote GPU development requires SSH access to the h100_sglang host, a running Docker container named sglang_bbuf, and the SGLang repository mounted at /sgl-workspace/sglang to ensure a ready and reproducible environment.
Using a ready remote H100 environment ensures reproducible CUDA workflows across teams by providing a preconfigured Docker container and mounted SGLang repository, guaranteeing consistent results for GPU-bound tests and validation.
A remote Docker container provides H100 GPU access for SGLang validation when local hardware is insufficient, ensuring safe execution of diffusion checks and CUDA tests in a standardized, reproducible workspace.
Yes, remote SGLang development supports working directly in the repository by attaching to the sglang_bbuf container where the SGLang repository is mounted at /sgl-workspace/sglang, enabling immediate GPU-backed development.
BBufAutomate diffusion smoke tests and kernel validation on the H100 SGLang remote environment.
FutureMLS-LabGenerate end-to-end performance profiling traces for SGLang server runs.
amdmaxManage remote CUDA machines with GPU monitoring and vLLM server control.
hve4638Deploy and run ML experiments on local or remote GPU servers.
