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exec-local-compile

Compile TensorRT-LLM on a compute node inside a Docker container. Use this when already on a compute node with GPUs visible.

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Dépôt
NVIDIA/TensorRT-LLM
Dernière activité de la source
22 juillet 2026 à 02:20
Langue détectée de SKILL.md
anglais
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14 944
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2 867

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SKILL.md
Instructions source · Aperçu en lecture seule
name
exec-local-compile
description
Compile TensorRT-LLM on a compute node inside a Docker container. Use this when already on a compute node with GPUs visible.
license
Apache-2.0
metadata
{"author":"NVIDIA Corporation"}
# Compile TensorRT-LLM (Local / Compute Node) Compile TensorRT-LLM from source on a compute node inside a Docker container. ## When to Use | Scenario | Use This Skill? | |----------|----------------| | On a compute node with GPUs visible (`nvidia-smi` works) | Yes | | On a SLURM login node (no GPUs) | No — use `exec-slurm-compile` instead | ## Prerequisites - You are inside a Docker/enroot container on a compute node - `nvidia-smi` succeeds (GPUs visible) - `/usr/local/tensorrt` exists (TensorRT installation in the container) ## Instructions ### Step 1: Verify Environment Run `nvidia-smi` to confirm you are on a compute node with GPU access. ### Step 2: Locate the Codebase `cd` to the TensorRT-LLM repository. If the path is not provided by the user, ask for it. ### Step 3: (Optional) Checkout Branch If the user specifies a branch (e.g., "compile ToT"), checkout and pull: ```bash git checkout main && git pull ``` ### Step 4: Build Run the build command (**incremental by default** — omit `-c`/`--clean` unless explicitly requested or the incremental build fails): ```bash ./scripts/build_wheel.py --use_ccache -a "<arch>" -f --nvtx ``` Replace `<arch>` with the target GPU architecture (see Architecture Reference below). If not specified by the user, auto-detect from `nvidia-smi`. ### Step 5: Install ```bash pip install -e .[devel] ``` ### Step 6: Verify ```bash python3 -c "import tensorrt_llm; print(tensorrt_llm.__version__)" ``` ## Build Flags | Flag | Description | |------|-------------| | `-a "<arch>"` | Target GPU architecture(s) | | `--nvtx` | Enable NVTX markers for profiling | | `--use_ccache` | Use ccache for faster recompilation | | `-f` / `--fast_build` | Skip some kernels for faster dev compilation. **Always use for dev builds.** | | `-c` / `--clean` | Clean build directory before building. Only when needed (see below). | | `--skip_building_wheel` | Build in-place without creating a wheel file | | `--no-venv` | Skip virtual environment creation | ## Architecture Reference | Value | GPU Family | |-------|-----------| | `"100-real"` | Blackwell (B200, GB200) | | `"90-real"` | Hopper (H100, H200) | | `"89-real"` | Ada Lovelace (L40S) | | `"80-real"` | Ampere (A100) | | `"90;100-real"` | Multiple architectures | ## Incremental vs. Clean Builds **Default to incremental builds** — CMake only recompiles changed files, saving significant time. Use a **clean build** (`-c`) only when: - The user explicitly requests a clean/fresh build - An incremental build fails with linker errors, stale object files, or CMake cache issues - Major branch changes (e.g., rebasing across many commits) that may invalidate the build cache - Build system files changed (`CMakeLists.txt`, `*.cmake`)
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