TensorRT-LLMs/examples/llm-api/llm_mgmn_trtllm_serve.sh
Yan Chunwei a5eff139f1
[TRTLLM-5277] chore: refine llmapi examples for 1.0 (part1) (#5431)
Signed-off-by: Superjomn <328693+Superjomn@users.noreply.github.com>
Signed-off-by: Erin Ho <14718778+hchings@users.noreply.github.com>
Co-authored-by: Erin Ho <14718778+hchings@users.noreply.github.com>
2025-07-01 19:06:41 +08:00

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Bash

#!/bin/bash
#SBATCH -A <account>
#SBATCH -p <partition>
#SBATCH -t 01:00:00
#SBATCH -N 2
#SBATCH --ntasks-per-node=8
#SBATCH -o logs/trtllm-serve.out
#SBATCH -e logs/trtllm-serve.err
#SBATCH -J trtllm-serve
### :title Run trtllm-serve with pytorch backend on Slurm
### :order 2
### :section Slurm
# NOTE, this feature is experimental and may not work on all systems.
# The trtllm-llmapi-launch is a script that launches the LLM-API code on
# Slurm-like systems, and can support multi-node and multi-GPU setups.
# Note that, the number of MPI processes should be the same as the model world
# size. e.g. For tensor_parallel_size=16, you may use 2 nodes with 8 gpus for
# each, or 4 nodes with 4 gpus for each or other combinations.
# This docker image should have tensorrt_llm installed, or you need to install
# it in the task.
# The following variables are expected to be set in the environment:
# You can set them via --export in the srun/sbatch command.
# CONTAINER_IMAGE: the docker image to use, you'd better install tensorrt_llm in it, or install it in the task.
# MOUNT_DIR: the directory to mount in the container
# MOUNT_DEST: the destination directory in the container
# WORKDIR: the working directory in the container
# SOURCE_ROOT: the path to the TensorRT-LLM source
# PROLOGUE: the prologue to run before the script
# LOCAL_MODEL: the local model directory to use, NOTE: downloading from HF is
# not supported in Slurm mode, you need to download the model and put it in
# the LOCAL_MODEL directory.
echo "Starting trtllm-serve..."
# Just launch trtllm-serve job with trtllm-llmapi-launch command.
srun -l \
--container-image=${CONTAINER_IMAGE} \
--container-mounts=${MOUNT_DIR}:${MOUNT_DEST} \
--container-workdir=${WORKDIR} \
--export=ALL,PYTHONPATH=${SOURCE_ROOT} \
--mpi=pmix \
bash -c "
set -ex
$PROLOGUE
export PATH=$PATH:~/.local/bin
trtllm-llmapi-launch \
trtllm-serve $LOCAL_MODEL \
--tp_size 16 \
--backend pytorch \
--host 0.0.0.0 \
${ADDITIONAL_OPTIONS}
"