Jetson AGX Orin部署实战指南
引言
NVIDIA Jetson AGX Orin是最强大的嵌入式AI平台之一，提供275 TOPS的AI性能。本文介绍完整的模型部署流程。
Jetson AGX Orin规格
硬件配置
| 规格 |
数值 |
| AI性能 |
275 TOPS |
| GPU |
2048核 NVIDIA Ampere |
| CPU |
12核 ARM Cortex-A78 |
| 显存 |
64GB LPDDR5 |
| 功耗 |
15-60W |
开发环境配置
JetPack安装
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sudo apt install ./sdkmanager.deb
sudo ./flash.py jetson-agx-orin-devkit mmcblk0p1
sudo apt install nvidia-jetpack
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CUDA环境
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| import cupy as cp
print(f"CUDA Version: {cp.cuda.runtime.runtimeGetVersion()}")
print(f"Device Count: {cp.cuda.runtime.getDeviceCount()}")
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模型部署流程
完整部署示例
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| import torch
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
class OrinDeployer:
"""Jetson Orin模型部署器"""
def __init__(self, engine_path):
self.logger = trt.Logger(trt.Logger.WARNING)
self.runtime = trt.Runtime(self.logger)
self.engine = self.runtime.deserialize_cuda_engine(open(engine_path, 'rb').read())
self.context = self.engine.create_execution_context()
def allocate_buffers(self):
self.h_input = cuda.pagelocked_empty(1 * 3 * 640 * 640, dtype=np.float32)
self.h_output = cuda.pagelocked_empty(1 * 8400 * 85, dtype=np.float32)
self.d_input = cuda.mem_alloc(self.h_input.nbytes)
self.d_output = cuda.mem_alloc(self.h_output.nbytes)
self.stream = cuda.Stream()
def infer(self, input_data):
cuda.memcpy_htod_async(self.d_input, input_data, self.stream)
self.context.execute_async_v2(
bindings=[int(self.d_input), int(self.d_output)],
stream_handle=self.stream.handle
)
cuda.memcpy_dtoh_async(self.h_output, self.d_output, self.stream)
self.stream.synchronize()
return self.h_output
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性能优化
功率模式
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sudo nvpmodel -q
sudo nvpmodel -m 0
sudo nvpmodel -m 2
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风扇控制
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| import subprocess
def set_fan_speed(speed_percent):
"""设置风扇速度"""
subprocess.run(['jetson_clocks', '--fan'])
with open('/sys/devices/pwm-fan/target_pwm', 'w') as f:
f.write(str(int(speed_percent * 2.55)))
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多模型部署
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| class MultiModelDeployer:
"""多模型部署管理器"""
def __init__(self, models_config):
self.models = {}
for name, config in models_config.items():
self.models[name] = OrinDeployer(config['engine'])
self.stream_pool = [cuda.Stream() for _ in range(len(models_config))]
def parallel_infer(self, inputs_dict):
results = {}
for i, (name, input_data) in enumerate(inputs_dict.items()):
model = self.models[name]
stream = self.stream_pool[i]
results[name] = model.infer_async(input_data, stream)
return results
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总结
Jetson AGX Orin为边缘AI提供了强大的算力支持，配合TensorRT可以实现高效的模型推理。
参考：NVIDIA Jetson官方文档
