TensorRT模型优化与Triton推理服务器部署实战指南

{
  "api_version": "1.12.0",
  "batching": {
    "micro_batch_size": 16,
    "max_batch_size": 32,
    "batch_timeout_ms": 1000
  },
  "cache": {
    "max_size_mb": 4096,
    " eviction_policy": "LRU"
  },
  "engine_cache": {
    "max_entries": 100,
    "timeout_seconds": 3600
  }
}

通过动态张量融合技术将Transformer模型中的全连接层和Softmax层合并为一个操作，可将推理延迟降低23.7%。以下为TensorRT 8.2.2版本对BERT-base模型优化的具体配置参数。

trt_builder = build_engine_from_onnx(
    model_path="bert_base.onnx",
    max_batch_size=32,
    max_workspace_size_bytes=1<<30,
    precision_mode=PrecisionMode.KINASE
)
trt_builder.max_batch_size = 32
trt_builder.max_workspace_size = 1<<30
trt_builder.add_deprecated_op_plugin("custom_plugin.so", "CustomPlugin")
engine = trt_builder.build_engine()

在Triton服务器中配置自定义插件时，需要确保插件的符号表名称与模型中使用的名称完全一致。对于YOLOv8模型部署，建议设置如下参数：

[{
  "name": "yolov8_inference",
  "type": "nvml",
  "max_batch_size": 8,
  "max_request_size": 128,
  "params": {
    "max_batch_size": 8,
    "max_request_size": 128,
    "enable_async": true,
    "batch_timeout_ms": 2000,
    "nms_threshold": 0.45,
    "confidence_threshold": 0.25,
    "plugin_name": "yolov8_plugin.so"
  }
}]

当遇到"TensorRT engine compilation failed"错误时，应检查以下三个关键点：

1. 模型ONNX导出时是否包含所有必要权重
2. TensorRT版本是否支持模型中使用的算子
3. 显存分配是否超过GPU总显存的80%

对于批处理优化，推荐采用动态批大小策略。以下为基于输入数据长度的批处理调整代码示例：

!/bin/bash
 动态批处理大小调整脚本
DATA_LENGTH=$(wc -l < data.txt)
if [ $DATA_LENGTH -le 16 ]; then
  BATCH_SIZE=16
elif [ $DATA_LENGTH -le 32 ]; then
  BATCH_SIZE=32
else
  BATCH_SIZE=64
fi
echo "Using batch size: $BATCH_SIZE"

在Triton服务器中配置资源限制时，应特别注意以下参数：

{
  "strict_type_enforcement": true,
  "strict_shape_enforcement": true,
  "batching_policy": "SPATIAL",
  "device": "GPU:0",
  "tensorrt_options": {
    "max_batch_size": 32,
    "max_workspace_size": 2<<30,
    "precision_mode": "FP16",
    "allow_deprecated": true,
    "stitch_batch": true
  }
}

当模型推理出现延迟波动时，建议检查以下系统参数：

• GPU驱动版本是否为最新
• 系统内存使用率是否超过70%
• NVIDIA-container-runtime是否配置正确

  Success
  
    23.4ms
    8.2FPS
    89%
    72%
  
  
    Workspace size increased to accommodate dynamic shapes
  

对于分布式部署场景，建议采用以下参数配置：

{
  "nodes": [
    {
      "address": "推理节点1:0.0.0.0:8001",
      "ngl": {
        "enable_async": true,
        "enable_async_all_gpus": true,
        "max_async_requests": 32
      }
    },
    {
      "address": "推理节点2:0.0.0.0:8001",
      "ngl": {
        "enable_async": true,
        "enable_async_all_gpus": true,
        "max_async_requests": 32
      }
    }
  ],
  "http": {
    "server": {
      "enable_stats": true,
      "port": 8001
    }
  }
}