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triton入门实战_triton server 所需要的cuda版本

作者：知新_RL | 2024-07-26 13:20:00

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triton server 所需要的cuda版本

这篇文章主要讲的是基于官方镜像及， pytorch script 格式模型，构建tritonserver 服务

1、环境准备：

  • 1.1. 下载 tritonserver镜像： Triton Inference Server | NVIDIA NGC

    • a. 注意：tritonserver 镜像中的invdia驱动版本对应，否则后面会启动失败。

  • 1.2. 然后，拉取Pytorch官方镜像作为推理系统的客户端同时进行一些预处理操作（当然也可以直接拉取tritonserver客户端SDK镜像）。

    • a. docker pull pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel暂时无法提供下载链接，因为无法访问dockerhub。
    • b. tritonserver客户端SDK镜像 Triton Inference Server | NVIDIA NGC
      # nvcr.io/nvidia/tritonserver:<yy.mm>-py3-sdk
      # docker pull nvcr.io/nvidia/tritonserver:23.04-py3-sdk

  • 1.3. 接下来，基于官方Pytorch镜像创建一个容器客户端。

    • a. 本地创建共享目录， D:\chinasoft\shumei\triton\demo_first\pytorch_container\workspace
    • b. docker run -dt --name pytorch200_cu117_dev --restart=always --gpus all --network=host --shm-size 4G -v /D/chinasoft/shumei/triton/demo_first/pytorch_container/workspace:/workspace -w /workspace pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel /bin/bash
    • c. 进入容器，docker exec -it pytorch200_cu117_dev bash
    • d. pip install datasets transformers -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn
    • e. pip install tritonclient[all] -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn

2、模型准备

  • 2.1. 文将基于 PyTorch 后端使用 resnet50 模型来进行图片分类，因此，需预先下载 resnet50 模型，然后将其转换为torchscript格式。具体代码（resnet50_convert_torchscript.py ）如下所示：
import torch
import torchvision.models as models

resnet50 = models.resnet50(pretrained=True)
resnet50.eval()
image = torch.randn(1, 3, 244, 244)
resnet50_traced = torch.jit.trace(resnet50, image)
resnet50(image)
# resnet50_traced.save('/workspace/model/resnet50/model.pt')
torch.jit.save(resnet50_traced, "/workspace/model/resnet50/model.pt")

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  • 2 2. 最后，拉取Triton Server 代码库。

    git clone -b r23.04 https://github.com/triton-inference-server/server.git

    一些常见后端backend的配置都在server/docs/examples目录下。

tree docs/examples -L 2
docs/examples
|-- README.md
|-- fetch_models.sh
|-- jetson
|   |-- README.md
|   `-- concurrency_and_dynamic_batching
`-- model_repository
    |-- densenet_onnx
    |-- inception_graphdef
    |-- simple
    |-- simple_dyna_sequence
    |-- simple_identity
    |-- simple_int8
    |-- simple_sequence
    `-- simple_string

11 directories, 3 files

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  • 2.3. 拉取Triton Tutorials库，该仓库中包含Triton的教程和样例，本文使用Quick_Deploy/PyTorch下部署一个Pytorch模型进行讲解。

    git clone https://github.com/triton-inference-server/tutorials.git

3、开发实践

  • 3.1 首先，在宿主机构建一个模型仓库，仓库的目录结构如下所示：
model_repository/
`-- resnet50
    |-- 1
    |   `-- model.pt
    `-- config.pbtxt

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其中， config.pbtxt 是模型配置文件； 1表示模型版本号； resnet50表示模型名，需要与config.pbtxt文件中的name字段保存一致；model.pt为模型权重（即上面转换后的模型权重）。

  • 3.2. 编辑config.pbtxt文件，具体内容如下所示：
name: "resnet50"
platform: "pytorch_libtorch"
max_batch_size : 0
input [
  {
    name: "input__0"
    data_type: TYPE_FP32
    dims: [ 3, 224, 224 ]
    reshape { shape: [ 1, 3, 224, 224 ] }
  }
]
output [
  {
    name: "output__0"
    data_type: TYPE_FP32
    dims: [ 1, 1000 ,1, 1]
    reshape { shape: [ 1, 1000 ] }
  }
]

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重要字段说明如下：

  • name：模型名
  • platform：用于指定模型对应的后端（backend），比如：pytorch_libtorch、onnxruntime_onnx、tensorrt_plan等
  • max_batch_size：模型推理在batch模式下支持的最大batch数
  • input：模型输入属性配置。
  • output：模型输出属性配置。

模型仓库构建好之后，接下来启动Triton推理服务端。

4、启动tritonserver推理服务

启动推理服务启动服务的方法有两种：一种是用 docker 启动并执行命令，一种是进入 docker 中然后手动调用命令。

我们在这里使用docker启动并执行命令：

docker run --gpus all --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 -v /D/chinasoft/shumei/triton/demo_first/model_repository:/models nvcr.io/nvidia/tritonserver:22.12-py3 tritonserver --model-repository=/models

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参数说明：

  • p：宿主机与容器内端口映射
  • v：将宿主机存储挂载进容器，这里将模型仓库挂载进容器
  • -model-repository：指定Triton服务模型仓库的地址
  • 这里注意指定的model_repository路径必须正确且模型文件已经配置无误，具体参考：模型准备章节。
(base) PS C:\Users\lenovo> docker run --gpus all --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 -v /D/chinasoft/shumei/triton/demo_first/model_repository:/models nvcr.io/nvidia/tritonserver:22.12-py3 tritonserver --model-repository=/models

=============================
== Triton Inference Server ==
=============================

NVIDIA Release 22.12 (build 50109463)
Triton Server Version 2.29.0

Copyright (c) 2018-2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

WARNING: CUDA Minor Version Compatibility mode ENABLED.
  Using driver version 516.94 which has support for CUDA 11.7.  This container
  was built with CUDA 11.8 and will be run in Minor Version Compatibility mode.
  CUDA Forward Compatibility is preferred over Minor Version Compatibility for use
  with this container but was unavailable:
  [[]]
  See https://docs.nvidia.com/deploy/cuda-compatibility/ for details.

I0804 01:46:15.003883 1 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x304800000' with size 268435456
I0804 01:46:15.004050 1 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 67108864
I0804 01:46:15.322720 1 model_lifecycle.cc:459] loading: resnet50:1
I0804 01:46:17.472054 1 libtorch.cc:1985] TRITONBACKEND_Initialize: pytorch
I0804 01:46:17.472105 1 libtorch.cc:1995] Triton TRITONBACKEND API version: 1.10
I0804 01:46:17.472587 1 libtorch.cc:2001] 'pytorch' TRITONBACKEND API version: 1.10
I0804 01:46:17.472634 1 libtorch.cc:2034] TRITONBACKEND_ModelInitialize: resnet50 (version 1)
W0804 01:46:17.473291 1 libtorch.cc:284] skipping model configuration auto-complete for 'resnet50': not supported for pytorch backend
I0804 01:46:17.473618 1 libtorch.cc:313] Optimized execution is enabled for model instance 'resnet50'
I0804 01:46:17.473624 1 libtorch.cc:332] Cache Cleaning is disabled for model instance 'resnet50'
I0804 01:46:17.473626 1 libtorch.cc:349] Inference Mode is disabled for model instance 'resnet50'
I0804 01:46:17.473640 1 libtorch.cc:444] NvFuser is not specified for model instance 'resnet50'
I0804 01:46:17.473699 1 libtorch.cc:2078] TRITONBACKEND_ModelInstanceInitialize: resnet50 (GPU device 0)
I0804 01:46:22.750763 1 model_lifecycle.cc:694] successfully loaded 'resnet50' version 1
I0804 01:46:22.750870 1 server.cc:563]
+------------------+------+
| Repository Agent | Path |
+------------------+------+
+------------------+------+

I0804 01:46:22.750917 1 server.cc:590]
+---------+---------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Backend | Path                                                    | Config                                                                                                                                                        |
+---------+---------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
| pytorch | /opt/tritonserver/backends/pytorch/libtriton_pytorch.so | {"cmdline":{"auto-complete-config":"true","min-compute-capability":"6.000000","backend-directory":"/opt/tritonserver/backends","default-max-batch-size":"4"}} |
+---------+---------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+

I0804 01:46:22.750948 1 server.cc:633]
+----------+---------+--------+
| Model    | Version | Status |
+----------+---------+--------+
| resnet50 | 1       | READY  |
+----------+---------+--------+

I0804 01:46:22.810861 1 metrics.cc:864] Collecting metrics for GPU 0: NVIDIA GeForce GTX 1650
I0804 01:46:22.811494 1 metrics.cc:757] Collecting CPU metrics
I0804 01:46:22.811657 1 tritonserver.cc:2264]
+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Option                           | Value                                                                                                                                                                                                |
+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| server_id                        | triton                                                                                                                                                                                               |
| server_version                   | 2.29.0                                                                                                                                                                                               |
| server_extensions                | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor_data statistics trace logging |
| model_repository_path[0]         | /models                                                                                                                                                                                              |
| model_control_mode               | MODE_NONE                                                                                                                                                                                            |
| strict_model_config              | 0                                                                                                                                                                                                    |
| rate_limit                       | OFF                                                                                                                                                                                                  |
| pinned_memory_pool_byte_size     | 268435456                                                                                                                                                                                            |
| cuda_memory_pool_byte_size{0}    | 67108864                                                                                                                                                                                             |
| response_cache_byte_size         | 0                                                                                                                                                                                                    |
| min_supported_compute_capability | 6.0                                                                                                                                                                                                  |
| strict_readiness                 | 1                                                                                                                                                                                                    |
| exit_timeout                     | 30                                                                                                                                                                                                   |
+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

I0804 01:46:22.813086 1 grpc_server.cc:4819] Started GRPCInferenceService at 0.0.0.0:8001
I0804 01:46:22.813243 1 http_server.cc:3477] Started HTTPService at 0.0.0.0:8000
I0804 01:46:22.890915 1 http_server.cc:184] Started Metrics Service at 0.0.0.0:8002
W0804 01:46:23.822499 1 metrics.cc:603] Unable to get power limit for GPU 0. Status:Success, value:0.000000
W0804 01:46:24.822769 1 metrics.cc:603] Unable to get power limit for GPU 0. Status:Success, value:0.000000
W0804 01:46:25.831221 1 metrics.cc:603] Unable to get power limit for GPU 0. Status:Success, value:0.000000

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可以看到resnet50模型已经 READY状态了，但是显卡没有用到，因为上面报警我宿主 机驱动版本和镜像驱动版本不匹配

WARNING: CUDA Minor Version Compatibility mode ENABLED.
  Using driver version 516.94 which has support for CUDA 11.7.  This container
  was built with CUDA 11.8 and will be run in Minor Version Compatibility mode.
  CUDA Forward Compatibility is preferred over Minor Version Compatibility for use
  with this container but was unavailable:
  [[]]
  See https://docs.nvidia.com/deploy/cuda-compatibility/ for details.


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5、发送推理请求

  • 5.1 首先，创建客户端脚本client.py，放到：
import numpy as np
from torchvision import transforms
from PIL import Image
import tritonclient.http as httpclient
from tritonclient.utils import triton_to_np_dtype

# 图片预处理
# preprocessing function
def rn50_preprocess(img_path="img1.jpg"):
    img = Image.open(img_path)
    preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    return preprocess(img).numpy()

transformed_img = rn50_preprocess()

# 设置连接到Triton服务端
# Setting up client
client = httpclient.InferenceServerClient(url="localhost:8000")

# 指定resnet50模型的输入和输出
inputs = httpclient.InferInput("input__0", transformed_img.shape, datatype="FP32")
inputs.set_data_from_numpy(transformed_img, binary_data=True)

# class_count表示获取 TopK 分类预测结果。如果没有设置这个选项，默认值为0，那么将会得到一个 1000 维的向量。
outputs = httpclient.InferRequestedOutput("output__0", binary_data=True, class_count=1000)

# 发送一个推理请求到Triton服务端
# Querying the server
results = client.infer(model_name="resnet50", inputs=[inputs], outputs=[outputs])
inference_output = results.as_numpy('output__0')
print(inference_output[:5])

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  • 5.2. 进入客户端容器：docker exec -it pytorch200_cu117_dev bash
  • 5.3. 预先下载好，用于推理请求的图片：
    wget -O img1.jpg "https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg"
  • 5.4. 执行客户端脚本发送请求：
python client.py
[b'12.474869:90' b'11.527128:92' b'9.659309:14' b'8.408504:136'
 b'8.216769:11']

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输出的格式为<confidence_score>:<classification_index>。

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