4.2 KiB
4.2 KiB
Multi-Node-DP (Qwen3-VL-235B-A22B)
Verify Multi-Node Communication Environment
referring to multi_node.md
Run with docker
Assume you have an Atlas 800 A3(64G*16) nodes(or 2 * A2), and want to deploy the Qwen3-VL-235B-A22B-Instruct model across multi-node.
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--net=host \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci8 \
--device /dev/davinci9 \
--device /dev/davinci10 \
--device /dev/davinci11 \
--device /dev/davinci12 \
--device /dev/davinci13 \
--device /dev/davinci14 \
--device /dev/davinci15 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
Run the following scripts on two nodes respectively
:::{note} Before launch the inference server, ensure the following environment variables are set for multi node communication :::
node0
#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip
nic_name="xxxx"
local_ip="xxxx"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=100
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=1024
vllm serve Qwen/Qwen3-VL-235B-A22B-Instruct \
--host 0.0.0.0 \
--port 8000 \
--data-parallel-size 2 \
--api-server-count 2 \
--data-parallel-size-local 1 \
--data-parallel-address $local_ip \
--data-parallel-rpc-port 13389 \
--seed 1024 \
--served-model-name qwen3vl \
--tensor-parallel-size 8 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 32768 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.8 \
node1
#!/bin/sh
nic_name="xxxx"
local_ip="xxxx"
node0_ip="xxxx"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=100
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=1024
vllm serve Qwen/Qwen3-VL-235B-A22B-Instruct \
--host 0.0.0.0 \
--port 8000 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--seed 1024 \
--tensor-parallel-size 8 \
--served-model-name qwen3vl \
--max-num-seqs 16 \
--max-model-len 32768 \
--max-num-batched-tokens 4096 \
--enable-expert-parallel \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.8 \
If the service starts successfully, the following information will be displayed on node0:
INFO: Started server process [44610]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Started server process [44611]
INFO: Waiting for application startup.
INFO: Application startup complete.
Once your server is started, you can query the model with input prompts:
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3vl",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png"}},
{"type": "text", "text": "What is the text in the illustrate?"}
]}
]
}'