developer.chat

30 May 2024

SEO Title

TensorRT-LLM

https://github.com/NVIDIA/TensorRT-LLM

TensorRT-LLM为用户提供了一个易于使用的Python API，以定义大型语言模型（LLM）并构建包含最先进优化的TensorRT引擎，从而在NVIDIA GPU上高效地执行推理。TensorRTLLM还包含用于创建执行这些TensorRT引擎的Python和C++运行时的组件。

https://github.com/NVIDIA/TensorRT-LLM/tree/f430a4b447ef4cba22698902d43eae0debf08594/tensorrt_llm/models/qwen

https://github.com/NVIDIA/TensorRT-LLM/tree/f430a4b447ef4cba22698902d43eae0debf08594/examples/qwen

【LLMOps】Triton + TensorRT-LLM部署QWen

https://www.cnblogs.com/zhouwenyang/p/18023854

背景

TensorRT-LLM是Nvidia官方推出的大模型推理加速框架，目前只对部分显卡型号有做定制加速。最近新出的Chat with RTX也是基于TensorRT-LLM进行的本地推理。

TensorRT-LLM支持PagedAttention、FlashAttention、SafeTensor等手动，某些社区号称吞吐能力测试结果超过vLLM。

准备

显卡A800
QWen7B 预训练模型

构建镜像最好自己构建最新的。尝试使用nvidia提供的镜像，发现镜像版本滞后。而且使用后出现各种不兼容，很容易让人误以为是自身操作问题。

开始

转换权重

首先需要将QWen模型转换为TensorRT所支持的.engine格式的权重文件

环境构建

下载TensorRT-LLM的官方代码：https://github.com/NVIDIA/TensorRT-LLM.git

然后编辑 TensorRT-LLM/docker/Dockerfile.multi ,内容如下

View Code

主要是在59行加上一个pip镜像。

cd TensorRT-LLM/docker
make build

执行上述命令，构建镜像。以我这边为例，构建完的镜像名为 tensorrt-llm:v3

容器启动

docker run -it --gpus '"device=1"' --name trt-llm -v /home:/home tensorrt-llm:v3 bash
docker exec -it trt-llm bash

转换权重

进入到容器内部

cd examples/qwen
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install -r requirements.txt

中间会报tensorrt版本冲突，忽略即可。

执行转换：

python3 build.py --hf_model_dir  /home/Qwen-7b/ --dtype bfloat16 --paged_kv_cache  --use_gpt_attention_plugin bfloat16 --enable_context_fmha --use_gemm_plugin bfloat16 --use_inflight_batching --remove_input_padding --enable_context_fmha --output  /home/trt_engines_qwen7b_bf16

测试：

python3 ../run.py --input_text "请你讲述一个故事" --max_output_len=64 --tokenizer_dir /home/Qwen-7b/ --engine_dir=/home/trt_engines_qwen7b_bf16

测试结果如下：

/usr/local/lib/python3.10/dist-packages/tensorrt_llm/runtime/generation.py:881: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /opt/pytorch/pytorch/aten/src/ATen/NestedTensorImpl.cpp:178.)
 torch.nested.nested_tensor(split_ids_list,
Input [Text 0]: "<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
hello<|im_end|>
<|im_start|>assistant
"
Output [Text 0 Beam 0]: "Hello! How can I help you today? Is there something you would like to talk about or ask me a question? I'm here to assist you with any information or advice you might need."

推理

构建镜像

下载triton代码：https://github.com/triton-inference-server/tensorrtllm_backend

此处有坑，构建时忘记记录了，跳过。最终构建的镜像：triton-trt-llm:v3.0

启动服务

进入到目录下执行

将tensorrtllm_backend/all_models/inflight_batcher_llm 复制到/home/tensorrtllm_backend/model_repository下

python3 tools/fill_template.py -i /home/tensorrtllm_backend/model_repository/tensorrt_llm/config.pbtxt triton_max_batch_size:64,decoupled_mode:False,max_beam_width:1,engine_dir:/tensorrtllm_backend/model_repository/tensorrt_llm/1,max_tokens_in_paged_kv_cache:2560,max_attention_window_size:2560,kv_cache_free_gpu_mem_fraction:0.5,exclude_input_in_output:True,enable_kv_cache_reuse:False,batching_strategy:inflight_batching,max_queue_delay_microseconds:600

其中

batch_scheduler_policy 设置为guaranteed_no_evict
enable_trt_overlap 设置为False
max_num_sequences 设置为batch-size一样
normalize_log_probs设置为False
gpt-model-type 设置为v1

再其中

修改postprocess和postprocess中的model.py 大约在81行左右，加上self.tokenizer.eos_token = "<|endoftext|>"

启动容器 trition-trt-llm

docker run --rm -it --gpus '"device=1"' --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 -p 18000:8000 -v /home/triton-trtllm/:/tensorrtllm_backend trition-trt-llm:v3.0 bash

启动服务

pip install tiktoken
cd /tensorrtllm_backend/tensorrtllm_backend
# --world_size is the number of GPUs you want to use for serving
python3 scripts/launch_triton_server.py --world_size=1 --model_repo=/tensorrtllm_backend/model_repository

请求接口

curl --location 'http://localhost:18000/v2/models/ensemble/generate' \
--header 'Content-Type: application/json' \
--data '{
   "text_input": "What is machine learning?",
   "max_tokens": 64,
   "bad_words": "",
   "stop_words": ""
}'

性能

在A800上实际测试，吞吐约为vllm的一半，RT也没有明显的下降。可能A800跟A100还是有很大区别的

Triton24.02 部署TensorRT-LLM,实现http查询

选择正确的环境

选择版本。查询nvidia官方文档，可以看到目前最新的容器是24.02。

在NVIDIA Driver这一行，它推荐的英伟达驱动版本是545以上，对于数据卡，可以适当降低。如果你是游戏卡，驱动版本没有545，也不想升级，那么建议至少不要低太多，比如535其实也可以。
在Triton Inference Server这一行，可以看到它内置了triton server版本是2.43，需要的TensorRT-LLM版本是0.8.0。

拉取镜像。进入Nvidia镜像中心找到tritonserver的镜像，选择和TensorRT-LLM（简称trtllm）有关的容器，然后拷贝镜像地址，最后使用docker pull来拉取该镜像。

docker pull nvcr.io/nvidia/tritonserver:24.02-trtllm-python-py3

拉取TensorRT-LLM的项目。

可以选择官方项目，但是注意要是v0.8.0

git clone https://github.com/NVIDIA/TensorRT-LLM.git -b v0.8.0

也可以选择我的项目，目前main分支就是0.8.0，后续可能会打成tag，建议实际访问项目地址，查看是否有0.8.0的tag。

git clone https://github.com/Tlntin/Qwen-TensorRT-LLM

下面演示是以我的项目为主，在triton_server上面部署Qwen-1.8B-Chat（毕竟这个模型比较小）

拉取tensorrtllm_backend。这个是用来编排tensorrt-llm服务的，需要和TensorRT-LLM版本一致，这里同样选择0.8.0

git clone https://github.com/triton-inference-server/tensorrtllm_backend.git -b v0.8.0

启动tritonserver容器

docker run -d \
    --name triton \
    --net host \
    --shm-size=2g \
    --ulimit memlock=-1 \
    --ulimit stack=67108864 \
    --gpus all \
    -v ${PWD}/tensorrtllm_backend:/tensorrtllm_backend \
    -v ${PWD}/Qwen-TensorRT-LLM/examples:/root/examples \
    nvcr.io/nvidia/tritonserver:24.02-trtllm-python-py3 sleep 864000

检查服务

进入容器

docker exec -it triton /bin/bash

检查英伟达驱动

nvidia-smi

检查tritonserver版本，至少和上面提到的一样，是2.43

cat /opt/tritonserver/TRITON_VERSION

检查tensorrtllm_backend版本，该数值必须和官方github仓库的0.8.0版本的tool/version.txt文件内容一致，官方仓库链接

cat /tensorrtllm_backend/tools/version.txt

直接通过pip安装TensorRT-LLM （如果是自己编译的容器，这步可以省略）

pip install tensorrt_llm==0.8.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://download.pytorch.org/whl/cu121

编译Engine

参考我项目的readme

进入容器

docker exec -it triton /bin/bash

重复之前的操作，安装qwen的依赖。

cd /root/examples/qwen2

安装依赖

pip install -r requirements.txt

编译，需要在原来Readme编译的基础上开启paged_kv_cache，方便部署inflight-batching

例如fp16之前编译是python3 build.py，现在改成下面这个。

python3 build.py --paged_kv_cache --remove_input_padding

例如int8-smooth-quant

之前编译命令是

# 转权重
python3 hf_qwen_convert.py --smoothquant=0.5
# 编译
python3 build.py --use_smooth_quant --per_token --per_channel

现在编译命令是

# 转权重
python3 hf_qwen_convert.py --smoothquant=0.5
# 编译
python3 build.py --use_smooth_quant --per_token --per_channel --paged_kv_cache --remove_input_padding

运行一下做个测试

python3 run.py

临时部署Triton

参考tensorrtllm_backend 0.8.0的readme
同时参考llama的详细部署教程

（可选）直接复用本项目配置（batch_size=2，input=6144, output=2048），这样就跳过后续的第6，第7步骤，需要在容器外操作，懒人必备，不过还是推荐自己改好一些。

cp -r Qwen-TensorRT-LLM/triton_model_repo tensorrtllm_backend/

进入容器

docker exec -it triton /bin/bash

cd /tensorrtllm_backend
cp all_models/inflight_batcher_llm/ -r triton_model_repo

复制上一部分编译好的Engine文件

cd /root/examples/qwen2/trt_engines/fp16/1-gpu/
cp -r ./* /tensorrtllm_backend/triton_model_repo/tensorrt_llm/1/

复制tokenzer文件

cd /root/examples/qwen2
mkdir /tensorrtllm_backend/triton_model_repo/tensorrt_llm/qwen1.5_7b_chat
cp qwen1.5_7b_chat/*.json /tensorrtllm_backend/triton_model_repo/tensorrt_llm/qwen1.5_7b_chat/
# 可选，仅适用于qwen1,因为它的tokenizer是tiktoken格式
cp qwen_7b_chat/*.tiktoken /tensorrtllm_backend/triton_model_repo/tensorrt_llm/qwen1.5_7b_chat/
cp qwen_7b_chat/*.py /tensorrtllm_backend/triton_model_repo/tensorrt_llm/qwen1.5_7b_chat/

（可选）编写Triton中的预处理配置和后处理配置，参考文档

cd /tensorrtllm_backend
export HF_QWEN_MODEL="/tensorrtllm_backend/triton_model_repo/tensorrt_llm/qwen1.5_7b_chat"
export ENGINE_DIR="/tensorrtllm_backend/triton_model_repo/tensorrt_llm/1"
# 设置你的batch_size大小
export MAX_BATCH_SIZE=1
export TOKENIZE_TYPE=auto
# 根据cpu线程数定，一般为batch_size的2倍数或者cpu线程的一半
export INSTANCE_COUNT=2
# 我就一张卡，你可以指定用那些卡，用逗号隔开
export GPU_DEVICE_IDS=0


python3 tools/fill_template.py -i triton_model_repo/preprocessing/config.pbtxt tokenizer_dir:${HF_QWEN_MODEL},tokenizer_type:${TOKENIZE_TYPE},triton_max_batch_size:${MAX_BATCH_SIZE},preprocessing_instance_count:${INSTANCE_COUNT}

python3 tools/fill_template.py -i triton_model_repo/postprocessing/config.pbtxt tokenizer_dir:${HF_QWEN_MODEL},tokenizer_type:${TOKENIZE_TYPE},triton_max_batch_size:${MAX_BATCH_SIZE},postprocessing_instance_count:${INSTANCE_COUNT}

python3 tools/fill_template.py -i triton_model_repo/tensorrt_llm_bls/config.pbtxt triton_max_batch_size:${MAX_BATCH_SIZE},decoupled_mode:False,bls_instance_count:${INSTANCE_COUNT},accumulate_tokens:True

python3 tools/fill_template.py -i triton_model_repo/ensemble/config.pbtxt triton_max_batch_size:${MAX_BATCH_SIZE}

python3 tools/fill_template.py -i triton_model_repo/tensorrt_llm/config.pbtxt triton_max_batch_size:${MAX_BATCH_SIZE},decoupled_mode:True,max_beam_width:1,engine_dir:${ENGINE_DIR},exclude_input_in_output:True,enable_kv_cache_reuse:False,batching_strategy:inflight_batching,max_queue_delay_microseconds:600,gpu_device_ids:${GPU_DEVICE_IDS}

（可选）简单修改一下preprocess/postprocess的model.py的initialize函数，示例是llama的，我们要改成qwen的tokenizer配置。

修改前（preprocessing有三行，postprocessing只有一行）：

self.tokenizer.pad_token = self.tokenizer.eos_token
self.tokenizer_end_id = self.tokenizer.encode(
    self.tokenizer.eos_token, add_special_tokens=False)[0]
self.tokenizer_pad_id = self.tokenizer.encode(
    self.tokenizer.pad_token, add_special_tokens=False)[0]

修改后

import os


gen_config_path = os.path.join(tokenizer_dir, 'generation_config.json')
with open(gen_config_path, 'r') as f:
	gen_config = json.load(f)
if isinstance (gen_config["eos_token_id"], list):
	pad_id = end_id = gen_config["eos_token_id"][0]
### if model type is base, run this branch
else:
	pad_id = gen_config["bos_token_id"]
	end_id = gen_config["eos_token_id"]
self.tokenizer_pad_id = pad_id
self.tokenizer_end_id = end_id
eos_token = self.tokenizer.decode(end_id)
self.tokenizer.eos_token = self.tokenizer.pad_token = eos_token

启动服务，单卡启动。

cd /tensorrtllm_backend
python3 scripts/launch_triton_server.py --world_size=1 --model_repo=/tensorrtllm_backend/triton_model_repo

另外开一个终端，测试一下http效果。

请求

curl -X POST localhost:8000/v2/models/ensemble/generate \
-d '{"text_input": "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n你好，你叫什么？<|im_end|>\n<|im_start|>assistant\n", "max_tokens": 100, "bad_words": "", "stop_words": "", "end_id": [151645], "pad_id": [151645]}'

输出结果

{"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"你好，我是来自阿里云的大规模语言模型，我叫通义千问。"}%

调用服务

python客户端请求

安装python依赖（可选）

pip install tritonclient transformers gevent geventhttpclient tiktoken grpcio

运行qwen/triton_client/inflight_batcher_llm_client.py文件即可开启

cd /root/examples/triton_client
python3 inflight_batcher_llm_client.py --tokenizer_dir=/tensorrtllm_backend/triton_model_repo/tensorrt_llm/qwen1.5_7b_chat

测试结果

====================
Human: 你好
Output: 你好！有什么我可以帮助你的吗？
Human: 你叫什么？
Output: 我是来自阿里云的大规模语言模型，我叫通义千问。

http流式调用

前提

编译的Engine开启了paged_kv_cache
部署triton时，tensorrt_llm/config.pbtxt里面的gpt_model_type对应的value为inflight_batching

运行命令

curl -X POST localhost:8000/v2/models/ensemble/generate_stream \
-d '{"text_input": "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n你好，你叫什么？<|im_end|>\n<|im_start|>assistant\n", "max_tokens": 100, "bad_words": "", "stop_words": "", "end_id": [151645], "pad_id": [151645], "stream": true}'

输出结果：

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":0.0,"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"你好"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"，"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"我是"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"来自"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"阿里"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"云"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"的大"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"规模"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"语言"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"模型"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"，"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"我"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"叫"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"通"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"义"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"千"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"问"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"。"}

data: {"cum_log_probs":0.0,"model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":""}

关闭triton服务

pkill tritonserver

永久部署

在上个容器部署时，我们启动的命令是python3 scripts/launch_triton_server.py --world_size=1 --model_repo=/tensorrtllm_backend/triton_model_repo，经过修改tensorrtllm_backend/scripts/launch_triton_server.py文件，倒数第二行增加一个print("cmd", cmd)在结尾打印出它真实运行的命令如下：

["mpirun", "--allow-run-as-root", "-n", "1", "/opt/tritonserver/bin/tritonserver", "--model-repository=/tensorrtllm_backend/triton_model_repo", "--grpc-port=8001", "--http-port=8000", "--metrics-port=8002", "--disable-auto-complete-config", "--backend-config=python,shm-region-prefix-name=prefix0_", ":"]

编写一个Dockerfile来启动刚刚的命令，替换原来容器自带的命令。

FROM nvcr.io/nvidia/tritonserver:24.02-trtllm-python-py3
USER root
# Option
# COPY tensorrtllm_backend /tensorrtllm_backend
WORKDIR /tensorrtllm_backend

CMD ["mpirun", "--allow-run-as-root", "-n", "1", "/opt/tritonserver/bin/tritonserver", "--model-repository=/tensorrtllm_backend/triton_model_repo", "--grpc-port=8001", "--http-port=8000", "--metrics-port=8002", "--disable-auto-complete-config", "--backend-config=python,shm-region-prefix-name=prefix0_", ":"]

编译新镜像，命名为tritonserver:24.02

docker build . -t tritonserver:24.02

测试一下是否ok

docker run -it \
    --name triton_server \
    --net host \
    --shm-size=2g \
    --ulimit memlock=-1 \
    --ulimit stack=67108864 \
    -v ${PWD}/tensorrtllm_backend:/tensorrtllm_backend \
    --gpus all \
    tritonserver:24.02

测试一下请求，没问题就退出，然后删除该容器

docker rm -f triton_server

永久开启该容器，设置后台启动，并且设置自动重启

docker run -d \
    --name triton_server \
    --net host \
    --shm-size=2g \
    --restart always \
    --ulimit memlock=-1 \
    --ulimit stack=67108864 \
    -v ${PWD}/tensorrtllm_backend:/tensorrtllm_backend \
    --gpus all \
    tritonserver:24.02

查看一下这个容器运行情况，发现正常。

docker logs triton_server

Qwen-TensorRT-LLM

文章链接

https://developer.chat/llm-serving-resource

登录发表评论

热门内容

今日:

总体:

最近浏览：

标签（标签）

LLM服务资源

category

TensorRT-LLM

【LLMOps】Triton + TensorRT-LLM部署QWen

背景

准备

开始

转换权重

环境构建

容器启动

转换权重

推理

构建镜像

启动服务

性能

Triton24.02 部署TensorRT-LLM,实现http查询

选择正确的环境

编译Engine

临时部署Triton

调用服务

python客户端请求

http流式调用

关闭triton服务

永久部署

Qwen-TensorRT-LLM

标签

标签（标签）

Search

category

TensorRT-LLM

【LLMOps】Triton + TensorRT-LLM部署QWen

背景

准备

开始

转换权重

环境构建

容器启动

转换权重

推理

构建镜像

启动服务

性能

Triton24.02 部署TensorRT-LLM,实现http查询

选择正确的环境

编译Engine

临时部署Triton

调用服务

python客户端请求

http流式调用

关闭triton服务

永久部署

Qwen-TensorRT-LLM

标签