ProfessorGPTProfessorGPT
NVIDIA
NVIDIANVIDIA· v1.0.0
devopsOfficial
Official Provider SkillView repo

Systematic approach to exploring the TensorRT-LLM codebase before implementing new features or optimizations. Teaches how to discover existing infrastructure, trace code paths, and avoid reimplementing what already exists. Derived from real mistakes where ~250 lines of code were written and deleted because existing forward methods weren't discovered upfront. Use when starting any new feature, optimization, or code modification in TRT-LLM.

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Skill details

Versionv1.0.0
AuthorNVIDIA
Categorydevops
Skill IDNVIDIA/TensorRT-LLM/.claude/skills/trtllm-codebase-exploration

Related skills

Exec Env CheckCheck the local execution environment for GPU availability, Docker support, and Slurm access. Returns the execution scenario (`satisfied, local, docker`, `satisfied, local, direct`, `satisfied, slurm, local`, or `not_satisfied`), the number of available GPUs, and the GPU type. On Slurm login nodes without local GPUs, the cluster is identified by delegating the hostname to internal-env-info (hostname-based mode), which owns the hostname → cluster_name patterns; GPU type and gpus_per_node then comExec Local CompileCompile TensorRT-LLM on a compute node inside a Docker container. Use this when already on a compute node with GPUs visible.Exec Local DockerExecute a TensorRT-LLM workload locally in Docker. Runs a fully-resolved Docker command in background, monitors completion, reads logs, and reports results. Workflow-agnostic — does not need to know if the workload is pytest, eval, benchmark, or a custom script.Exec Local SlurmSubmit and monitor a Slurm job on a local cluster. Supports two modes: (1) Persistent allocation (default) — allocates nodes once via nohup salloc, imports the container once, installs once, and reuses across runs by setting SLURM env vars and running the sbatch script via bash. (2) One-shot sbatch — submits a fully-generated Slurm script via sbatch, polls job status, reads logs on completion, and reports results. Workflow-agnostic — handles pytest, eval, benchmark, and custom scripts identicallExec Remote SlurmRemote SLURM cluster development via SSH. Use when running jobs, profiling, or developing on a remote SLURM cluster with pyxis/enroot containers. Covers SSH connection management, srun/sbatch/salloc job patterns, tmux-based allocation persistence, file transfer, and safe remote file access. Works with any SLURM cluster accessible via SSH.Exec Slurm CompileCompile TensorRT-LLM on a SLURM cluster. Covers submitting a batch job with a container image, monitoring the job, and verifying the build. Use when the user wants to compile TRT-LLM remotely via SLURM rather than on a local compute node.