[Docs] Add Tutorials for Online Serving on Multi Machine (#120)

Add Tutorials for Online Serving on Multi Machine

---------

Signed-off-by: SidaoY <1024863041@qq.com>
Co-authored-by: yx0716 <jinyx1007@foxmail.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>

docs/source/tutorials.md

@@ -207,3 +207,105 @@ If you run this script successfully, you can see the info shown below:
 Prompt: 'Hello, my name is', Generated text: ' Daniel and I am an 8th grade student at York Middle School. I'
 Prompt: 'The future of AI is', Generated text: ' following you. As the technology advances, a new report from the Institute for the'
 ```
+
+## Online Serving on Multi Machine
+
+Run docker container on each machine:
+
+```shell
+docker run \
+--name vllm-ascend \
+--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/davinci_manager \
+--device /dev/devmm_svm \
+--device /dev/hisi_hdc \
+-v /usr/local/dcmi:/usr/local/dcmi \
+-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 quay.io/ascend/vllm-ascend:v0.7.1rc1 bash
+```
+
+Choose one machine as head node, the other are worker nodes, then start ray on each machine:
+:::{note} Check out your `nic_name` by command `ip addr`  :::
+
+```shell
+# Head node
+export HCCL_IF_IP={local_ip}
+export GLOO_SOCKET_IFNAME={nic_name}
+export TP_SOCKET_IFNAME={nic_name}
+export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+ray start --head --num-gpus=8
+
+# Worker node
+export HCCL_IF_IP={local_ip}
+export ASCEND_PROCESS_LOG_PATH={plog_save_path}
+export GLOO_SOCKET_IFNAME={nic_name}
+export TP_SOCKET_IFNAME={nic_name}
+export RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES=1 
+export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+ray start --address='{head_node_ip}:{port_num}' --num-gpus=8 --node-ip-address={local_ip}
+```
+
+Start the vLLM server on head node:
+
+```shell
+export VLLM_HOST_IP={head_node_ip}
+export HCCL_CONNECT_TIMEOUT=120
+export ASCEND_PROCESS_LOG_PATH={plog_save_path}
+export HCCL_IF_IP={head_node_ip}
+
+if [ -d "{plog_save_path}" ]; then
+    rm -rf {plog_save_path}
+    echo ">>> remove {plog_save_path}"
+fi
+
+LOG_FILE="multinode_$(date +%Y%m%d_%H%M).log"
+VLLM_TORCH_PROFILER_DIR=./vllm_profile
+python -m vllm.entrypoints.openai.api_server  \
+       --model="Deepseek/DeepSeek-V2-Lite-Chat" \
+       --trust-remote-code \
+       --enforce-eager \
+       --max-model-len {max_model_len} \
+       --distributed_executor_backend "ray" \
+       --tensor-parallel-size 16 \
+       --disable-log-requests \
+       --disable-log-stats \
+       --disable-frontend-multiprocessing \
+       --port {port_num} \
+```
+
+Once your server is started, you can query the model with input prompts:
+
+```shell
+curl -X POST http://127.0.0.1:{prot_num}/v1/completions  \
+     -H "Content-Type: application/json" \
+     -d '{
+         "model": "Deepseek/DeepSeek-V2-Lite-Chat",
+         "prompt": "The future of AI is",
+         "max_tokens": 24
+     }'
+```
+
+If you query the server successfully, you can see the info shown below (client):
+
+```
+{"id":"cmpl-6dfb5a8d8be54d748f0783285dd52303","object":"text_completion","created":1739957835,"model":"/home/data/DeepSeek-V2-Lite-Chat/","choices":[{"index":0,"text":" heavily influenced by neuroscience and cognitiveGuionistes. The goalochondria is to combine the efforts of researchers, technologists,","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":6,"total_tokens":30,"completion_tokens":24,"prompt_tokens_details":null}}
+```
+
+Logs of the vllm server:
+
+```
+INFO:     127.0.0.1:59384 - "POST /v1/completions HTTP/1.1" 200 OK
+INFO 02-19 17:37:35 metrics.py:453] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 1.9 tokens/s, Running: 0 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.
+```