Python 3.8+<\/li>\n<\/ul>\n3.2 \u5b89\u88c5NVIDIA\u9a71\u52a8\u548cDocker<\/h4>\n
\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5NVIDIA\u9a71\u52a8\u548cDocker，\u53ef\u4ee5\u6309\u4ee5\u4e0b\u6b65\u9aa4\u64cd\u4f5c：<\/p>\n
# 1. \u5b89\u88c5NVIDIA\u9a71\u52a8（\u4ee5Ubuntu 22.04\u4e3a\u4f8b）
\nsudo apt update
\nsudo apt install -y nvidia-driver-535<\/p>\n
# \u91cd\u542f\u7cfb\u7edf
\nsudo reboot<\/p>\n
# 2. \u9a8c\u8bc1\u9a71\u52a8\u5b89\u88c5
\nnvidia-smi<\/p>\n
# 3. \u5b89\u88c5Docker
\nsudo apt install -y docker.io
\nsudo systemctl start docker
\nsudo systemctl enable docker<\/p>\n
# 4. \u5b89\u88c5NVIDIA Container Toolkit
\ndistribution=$(. \/etc\/os-release;echo $ID$VERSION_ID)
\ncurl -s -L https:\/\/nvidia.github.io\/nvidia-docker\/gpgkey | sudo apt-key add –
\ncurl -s -L https:\/\/nvidia.github.io\/nvidia-docker\/$distribution\/nvidia-docker.list | sudo tee \/etc\/apt\/sources.list.d\/nvidia-docker.list
\nsudo apt update
\nsudo apt install -y nvidia-container-toolkit
\nsudo systemctl restart docker<\/p>\n
3.3 \u90e8\u7f72Triton\u63a8\u7406\u670d\u52a1\u5668<\/h4>\n
Triton\u63d0\u4f9b\u4e86\u5b98\u65b9\u7684Docker\u955c\u50cf，\u90e8\u7f72\u975e\u5e38\u7b80\u5355：<\/p>\n
# 1. \u521b\u5efaTriton\u5de5\u4f5c\u76ee\u5f55
\nmkdir -p ~\/triton\/models
\nmkdir -p ~\/triton\/logs<\/p>\n
# 2. \u62c9\u53d6Triton\u955c\u50cf
\ndocker pull nvcr.io\/nvidia\/tritonserver:23.10-py3<\/p>\n
# 3. \u8fd0\u884cTriton\u5bb9\u5668
\ndocker run -d –gpus=all \\\\
\n –name triton-server \\\\
\n –shm-size=1g \\\\
\n -p 8000:8000 -p 8001:8001 -p 8002:8002 \\\\
\n -v ~\/triton\/models:\/models \\\\
\n -v ~\/triton\/logs:\/logs \\\\
\n nvcr.io\/nvidia\/tritonserver:23.10-py3 \\\\
\n tritonserver –model-repository=\/models –log-verbose=1<\/p>\n
# 4. \u68c0\u67e5\u670d\u52a1\u72b6\u6001
\ndocker logs triton-server | grep "Ready"<\/p>\n
\u5982\u679c\u770b\u5230\u201cServer is ready to receive inference requests.\u201d，\u8bf4\u660eTriton\u670d\u52a1\u542f\u52a8\u6210\u529f\u3002<\/p>\n
3.4 \u9a8c\u8bc1Triton\u670d\u52a1<\/h4>\n
# \u67e5\u770bTriton\u5065\u5eb7\u72b6\u6001
\ncurl -v http:\/\/localhost:8000\/v2\/health\/ready<\/p>\n
# \u67e5\u770b\u6a21\u578b\u4ed3\u5e93\u72b6\u6001
\ncurl http:\/\/localhost:8000\/v2\/models<\/p>\n
4. \u6a21\u578b\u8f6c\u6362\u4e0e\u4f18\u5316<\/h3>\n4.1 \u6a21\u578b\u683c\u5f0f\u8f6c\u6362<\/h4>\n
AIGlasses_for_navigation\u76ee\u524d\u4f7f\u7528PyTorch\u7684.pt\u683c\u5f0f\u6a21\u578b，\u9700\u8981\u8f6c\u6362\u4e3aTriton\u652f\u6301\u7684\u683c\u5f0f\u3002Triton\u652f\u6301\u591a\u79cd\u540e\u7aef，\u5bf9\u4e8ePyTorch\u6a21\u578b，\u6211\u4eec\u9009\u62e9LibTorch\u540e\u7aef\u3002<\/p>\n
\u5b89\u88c5\u8f6c\u6362\u5de5\u5177<\/p>\n
pip install torch torchvision<\/p>\n
\u8f6c\u6362\u811a\u672c\u793a\u4f8b<\/p>\n
# convert_to_torchscript.py
\nimport torch
\nimport torchvision<\/p>\n
def convert_yolo_to_torchscript():
\n # \u52a0\u8f7d\u539f\u59cb\u6a21\u578b
\n model = torch.hub.load('ultralytics\/yolov5', 'custom',
\n path='model\/yolo-seg.pt')
\n model.eval()<\/p>\n
# \u521b\u5efa\u793a\u4f8b\u8f93\u5165
\n example_input = torch.randn(1, 3, 640, 640)<\/p>\n
# \u8f6c\u6362\u4e3aTorchScript
\n traced_script_module = torch.jit.trace(model, example_input)<\/p>\n
# \u4fdd\u5b58\u4e3aTorchScript\u683c\u5f0f
\n traced_script_module.save("model\/yolo-seg-torchscript.pt")<\/p>\n
print("\u8f6c\u6362\u5b8c\u6210！")<\/p>\n
if __name__ == "__main__":
\n convert_yolo_to_torchscript()<\/p>\n
4.2 \u521b\u5efaTriton\u6a21\u578b\u914d\u7f6e<\/h4>\n
Triton\u9700\u8981\u4e3a\u6bcf\u4e2a\u6a21\u578b\u521b\u5efa\u914d\u7f6e\u6587\u4ef6，\u5b9a\u4e49\u8f93\u5165\u8f93\u51fa\u3001\u540e\u7aef\u7c7b\u578b\u7b49\u3002<\/p>\n
\u76f2\u9053\u68c0\u6d4b\u6a21\u578b\u914d\u7f6e<\/p>\n
# ~\/triton\/models\/blindway_detection\/config.pbtxt<\/p>\n
name: "blindway_detection"
\nplatform: "pytorch_libtorch"
\nmax_batch_size: 8<\/p>\n
input [
\n {
\n name: "input__0"
\n data_type: TYPE_FP32
\n dims: [3, 640, 640]
\n }
\n]<\/p>\n
output [
\n {
\n name: "output__0"
\n data_type: TYPE_FP32
\n dims: [-1, 6] # \u68c0\u6d4b\u7ed3\u679c：[batch, x1, y1, x2, y2, conf, class]
\n },
\n {
\n name: "output__1"
\n data_type: TYPE_FP32
\n dims: [-1, 32, 160, 160] # \u5206\u5272\u63a9\u7801
\n }
\n]<\/p>\n
instance_group [
\n {
\n kind: KIND_GPU
\n count: 1
\n }
\n]<\/p>\n
dynamic_batching {
\n preferred_batch_size: [1, 2, 4, 8]
\n max_queue_delay_microseconds: 1000
\n}<\/p>\n
4.3 \u90e8\u7f72\u6a21\u578b\u5230Triton<\/h4>\n
# 1. \u521b\u5efa\u6a21\u578b\u76ee\u5f55\u7ed3\u6784
\nmkdir -p ~\/triton\/models\/blindway_detection\/1<\/p>\n
# 2. \u590d\u5236\u6a21\u578b\u6587\u4ef6
\ncp model\/yolo-seg-torchscript.pt ~\/triton\/models\/blindway_detection\/1\/model.pt<\/p>\n
# 3. \u590d\u5236\u914d\u7f6e\u6587\u4ef6
\ncp config.pbtxt ~\/triton\/models\/blindway_detection\/<\/p>\n
# 4. \u91cd\u542fTriton\u670d\u52a1\u52a0\u8f7d\u65b0\u6a21\u578b
\ndocker restart triton-server<\/p>\n
# 5. \u9a8c\u8bc1\u6a21\u578b\u52a0\u8f7d
\ncurl http:\/\/localhost:8000\/v2\/models\/blindway_detection<\/p>\n
5. AIGlasses_for_navigation\u96c6\u6210Triton<\/h3>\n5.1 \u4fee\u6539\u63a8\u7406\u5ba2\u6237\u7aef<\/h4>\n
\u539f\u6765\u7684\u63a8\u7406\u4ee3\u7801\u662f\u5728\u672c\u5730\u76f4\u63a5\u8c03\u7528\u6a21\u578b，\u73b0\u5728\u9700\u8981\u6539\u4e3a\u8c03\u7528Triton\u670d\u52a1\u3002<\/p>\n
\u521b\u5efaTriton\u5ba2\u6237\u7aef\u7c7b<\/p>\n
# triton_client.py
\nimport tritonclient.http as httpclient
\nimport numpy as np
\nimport cv2<\/p>\n
class TritonInferenceClient:
\n def __init__(self, url="localhost:8000"):
\n self.client = httpclient.InferenceServerClient(url=url)
\n self.model_name = "blindway_detection"<\/p>\n
def preprocess_image(self, image):
\n """\u9884\u5904\u7406\u56fe\u50cf，\u9002\u914d\u6a21\u578b\u8f93\u5165"""
\n # \u8c03\u6574\u5927\u5c0f
\n img_resized = cv2.resize(image, (640, 640))
\n # \u5f52\u4e00\u5316
\n img_normalized = img_resized.astype(np.float32) \/ 255.0
\n # \u8f6c\u6362\u901a\u9053\u987a\u5e8f HWC -> CHW
\n img_chw = np.transpose(img_normalized, (2, 0, 1))
\n # \u6dfb\u52a0batch\u7ef4\u5ea6
\n img_batch = np.expand_dims(img_chw, axis=0)
\n return img_batch<\/p>\n
def detect_blindway(self, image):
\n """\u8c03\u7528Triton\u8fdb\u884c\u76f2\u9053\u68c0\u6d4b"""
\n # \u9884\u5904\u7406
\n input_data = self.preprocess_image(image)<\/p>\n
# \u521b\u5efa\u8f93\u5165tensor
\n inputs = [
\n httpclient.InferInput(
\n "input__0",
\n input_data.shape,
\n "FP32"
\n )
\n ]
\n inputs[0].set_data_from_numpy(input_data)<\/p>\n
# \u8bbe\u7f6e\u8f93\u51fa
\n outputs = [
\n httpclient.InferRequestedOutput("output__0"),
\n httpclient.InferRequestedOutput("output__1")
\n ]<\/p>\n
# \u53d1\u9001\u63a8\u7406\u8bf7\u6c42
\n response = self.client.infer(
\n model_name=self.model_name,
\n inputs=inputs,
\n outputs=outputs
\n )<\/p>\n
# \u89e3\u6790\u7ed3\u679c
\n detections = response.as_numpy("output__0")
\n masks = response.as_numpy("output__1")<\/p>\n
return detections, masks<\/p>\n
def batch_detect(self, image_list):
\n """\u6279\u91cf\u68c0\u6d4b，\u63d0\u9ad8\u6548\u7387"""
\n batch_data = np.concatenate([
\n self.preprocess_image(img) for img in image_list
\n ], axis=0)<\/p>\n
inputs = [
\n httpclient.InferInput(
\n "input__0",
\n batch_data.shape,
\n "FP32"
\n )
\n ]
\n inputs[0].set_data_from_numpy(batch_data)<\/p>\n
outputs = [
\n httpclient.InferRequestedOutput("output__0"),
\n httpclient.InferRequestedOutput("output__1")
\n ]<\/p>\n
response = self.client.infer(
\n model_name=self.model_name,
\n inputs=inputs,
\n outputs=outputs
\n )<\/p>\n
return response<\/p>\n
5.2 \u4fee\u6539\u4e3b\u7a0b\u5e8f<\/h4>\n
\u5728app_main.py\u4e2d，\u66ff\u6362\u539f\u6765\u7684\u672c\u5730\u63a8\u7406\u4e3aTriton\u5ba2\u6237\u7aef\u8c03\u7528：<\/p>\n
# \u5728\u539f\u6709\u4ee3\u7801\u57fa\u7840\u4e0a\u4fee\u6539
\nimport triton_client<\/p>\n
class AIGlassesSystem:
\n def __init__(self):
\n # \u521d\u59cb\u5316Triton\u5ba2\u6237\u7aef
\n self.triton_client = triton_client.TritonInferenceClient()<\/p>\n
# \u5176\u4ed6\u521d\u59cb\u5316\u4ee3\u7801\u4fdd\u6301\u4e0d\u53d8
\n self.api_key = self.load_api_key()
\n self.models_loaded = False<\/p>\n
def process_frame(self, frame):
\n """\u5904\u7406\u89c6\u9891\u5e27"""
\n try:
\n # \u4f7f\u7528Triton\u8fdb\u884c\u63a8\u7406
\n detections, masks = self.triton_client.detect_blindway(frame)<\/p>\n
# \u540e\u5904\u7406\u903b\u8f91\u4fdd\u6301\u4e0d\u53d8
\n processed_frame = self.postprocess_detections(frame, detections, masks)<\/p>\n
# \u8bed\u97f3\u5f15\u5bfc\u903b\u8f91
\n if self.navigation_active:
\n guidance = self.generate_guidance(detections)
\n self.speak_guidance(guidance)<\/p>\n
return processed_frame<\/p>\n
except Exception as e:
\n print(f"\u63a8\u7406\u9519\u8bef: {e}")
\n return frame<\/p>\n
def batch_process_frames(self, frames):
\n """\u6279\u91cf\u5904\u7406\u591a\u5e27，\u7528\u4e8e\u591a\u6444\u50cf\u5934\u573a\u666f"""
\n if len(frames) == 0:
\n return []<\/p>\n
# \u4f7f\u7528Triton\u7684\u6279\u91cf\u63a8\u7406
\n response = self.triton_client.batch_detect(frames)<\/p>\n
results = []
\n for i in range(len(frames)):
\n detections = response.as_numpy("output__0")[i]
\n masks = response.as_numpy("output__1")[i]<\/p>\n
processed_frame = self.postprocess_detections(
\n frames[i], detections, masks
\n )
\n results.append(processed_frame)<\/p>\n
return results<\/p>\n
5.3 \u914d\u7f6e\u7ba1\u7406\u4f18\u5316<\/h4>\n
\u4e3a\u4e86\u652f\u6301Triton\u914d\u7f6e，\u9700\u8981\u6269\u5c55\u914d\u7f6e\u7cfb\u7edf：<\/p>\n
# config_manager.py
\nimport json
\nimport os<\/p>\n
class ConfigManager:
\n def __init__(self):
\n self.config_file = ".triton_config.json"
\n self.default_config = {
\n "triton_server": "localhost:8000",
\n "models": {
\n "blindway_detection": "blindway_detection",
\n "traffic_light": "traffic_light_detection",
\n "object_detection": "shopping_detection",
\n "hand_detection": "hand_landmarker"
\n },
\n "batch_size": 4,
\n "timeout": 10.0,
\n "retry_count": 3
\n }<\/p>\n
def load_config(self):
\n """\u52a0\u8f7dTriton\u914d\u7f6e"""
\n if os.path.exists(self.config_file):
\n with open(self.config_file, 'r') as f:
\n config = json.load(f)
\n # \u5408\u5e76\u9ed8\u8ba4\u914d\u7f6e
\n return {**self.default_config, **config}
\n return self.default_config<\/p>\n
def save_config(self, config):
\n """\u4fdd\u5b58Triton\u914d\u7f6e"""
\n with open(self.config_file, 'w') as f:
\n json.dump(config, f, indent=2)<\/p>\n
def validate_connection(self):
\n """\u9a8c\u8bc1Triton\u8fde\u63a5"""
\n import tritonclient.http as httpclient<\/p>\n
try:
\n client = httpclient.InferenceServerClient(
\n url=self.config["triton_server"]
\n )
\n return client.is_server_live()
\n except:
\n return False<\/p>\n
6. \u6027\u80fd\u6d4b\u8bd5\u4e0e\u4f18\u5316<\/h3>\n6.1 \u57fa\u51c6\u6d4b\u8bd5<\/h4>\n
\u90e8\u7f72\u5b8c\u6210\u540e，\u6211\u4eec\u9700\u8981\u9a8c\u8bc1\u6027\u80fd\u63d0\u5347\u662f\u5426\u8fbe\u5230\u9884\u671f\u3002\u6211\u8bbe\u8ba1\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u6d4b\u8bd5\u811a\u672c：<\/p>\n
# benchmark.py
\nimport time
\nimport cv2
\nimport numpy as np
\nfrom triton_client import TritonInferenceClient<\/p>\n
def benchmark_triton():
\n """\u6d4b\u8bd5Triton\u63a8\u7406\u6027\u80fd"""
\n client = TritonInferenceClient()<\/p>\n
# \u51c6\u5907\u6d4b\u8bd5\u56fe\u50cf
\n test_image = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)<\/p>\n
# \u9884\u70ed
\n for _ in range(10):
\n client.detect_blindway(test_image)<\/p>\n
# \u6b63\u5f0f\u6d4b\u8bd5
\n num_iterations = 100
\n latencies = []<\/p>\n
for i in range(num_iterations):
\n start_time = time.time()
\n detections, masks = client.detect_blindway(test_image)
\n latency = (time.time() – start_time) * 1000 # \u8f6c\u6362\u4e3a\u6beb\u79d2
\n latencies.append(latency)<\/p>\n
if (i + 1) % 10 == 0:
\n print(f"\u5df2\u5b8c\u6210 {i+1}\/{num_iterations} \u6b21\u63a8\u7406")<\/p>\n
# \u7edf\u8ba1\u7ed3\u679c
\n avg_latency = np.mean(latencies)
\n p95_latency = np.percentile(latencies, 95)
\n fps = 1000 \/ avg_latency<\/p>\n
print(f"\\\\n\u6027\u80fd\u6d4b\u8bd5\u7ed3\u679c:")
\n print(f"\u5e73\u5747\u5ef6\u8fdf: {avg_latency:.2f} ms")
\n print(f"P95\u5ef6\u8fdf: {p95_latency:.2f} ms")
\n print(f"\u7406\u8bbaFPS: {fps:.2f}")
\n print(f"\u6700\u5c0f\u5ef6\u8fdf: {np.min(latencies):.2f} ms")
\n print(f"\u6700\u5927\u5ef6\u8fdf: {np.max(latencies):.2f} ms")<\/p>\n
return latencies<\/p>\n
def benchmark_batch():
\n """\u6d4b\u8bd5\u6279\u91cf\u63a8\u7406\u6027\u80fd"""
\n client = TritonInferenceClient()<\/p>\n
# \u51c6\u5907\u6279\u91cf\u6570\u636e
\n batch_size = 4
\n batch_images = [
\n np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
\n for _ in range(batch_size)
\n ]<\/p>\n
# \u6d4b\u8bd5\u6279\u91cf\u63a8\u7406
\n start_time = time.time()
\n response = client.batch_detect(batch_images)
\n batch_time = (time.time() – start_time) * 1000<\/p>\n
print(f"\\\\n\u6279\u91cf\u63a8\u7406\u6d4b\u8bd5 (batch_size={batch_size}):")
\n print(f"\u603b\u65f6\u95f4: {batch_time:.2f} ms")
\n print(f"\u5e73\u5747\u6bcf\u5e27: {batch_time\/batch_size:.2f} ms")
\n print(f"\u541e\u5410\u91cf: {1000\/(batch_time\/batch_size):.2f} FPS")<\/p>\n
if __name__ == "__main__":
\n print("\u5f00\u59cbTriton\u63a8\u7406\u6027\u80fd\u6d4b\u8bd5…")
\n latencies = benchmark_triton()
\n benchmark_batch()<\/p>\n
6.2 \u6027\u80fd\u5bf9\u6bd4\u7ed3\u679c<\/h4>\n
\u5728\u6211\u7684\u6d4b\u8bd5\u73af\u5883\u4e2d（RTX 3060 + i7-12700），\u5f97\u5230\u4e86\u4ee5\u4e0b\u7ed3\u679c：<\/p>\n
\u5355\u5e27\u63a8\u7406\u6027\u80fd\u5bf9\u6bd4<\/p>\n
\n\u6d4b\u8bd5\u573a\u666fCPU\u63a8\u7406GPU+Triton\u63d0\u5347<\/tr>\n
\n\n| \u76f2\u9053\u68c0\u6d4b<\/td>\n | 320ms<\/td>\n | 28ms<\/td>\n | 11.4\u500d<\/td>\n<\/tr>\n |
\n| \u7ea2\u7eff\u706f\u8bc6\u522b<\/td>\n | 180ms<\/td>\n | 15ms<\/td>\n | 12\u500d<\/td>\n<\/tr>\n |
\n| \u7269\u54c1\u68c0\u6d4b<\/td>\n | 250ms<\/td>\n | 22ms<\/td>\n | 11.4\u500d<\/td>\n<\/tr>\n |
\n| \u624b\u90e8\u68c0\u6d4b<\/td>\n | 150ms<\/td>\n | 12ms<\/td>\n | 12.5\u500d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u6279\u91cf\u63a8\u7406\u6027\u80fd（batch_size=4）<\/p>\n \n\u6a21\u578b\u603b\u65f6\u95f4\u5e73\u5747\u6bcf\u5e27\u541e\u5410\u91cf<\/tr>\n \n\n| \u76f2\u9053\u68c0\u6d4b<\/td>\n | 65ms<\/td>\n | 16.25ms<\/td>\n | 61.5 FPS<\/td>\n<\/tr>\n | \n| \u7ea2\u7eff\u706f\u8bc6\u522b<\/td>\n | 42ms<\/td>\n | 10.5ms<\/td>\n | 95.2 FPS<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u53ef\u4ee5\u770b\u5230，\u6279\u91cf\u63a8\u7406\u8fdb\u4e00\u6b65\u63d0\u5347\u4e86\u541e\u5410\u91cf，\u8fd9\u5bf9\u4e8e\u591a\u6444\u50cf\u5934\u573a\u666f\u7279\u522b\u6709\u7528\u3002<\/p>\n 6.3 \u4f18\u5316\u5efa\u8bae<\/h4>\n\u6839\u636e\u6d4b\u8bd5\u7ed3\u679c，\u6211\u603b\u7ed3\u4e86\u51e0\u6761\u4f18\u5316\u5efa\u8bae：<\/p>\n 1. \u8c03\u6574\u6279\u91cf\u5927\u5c0f<\/p>\n # \u6839\u636e\u5b9e\u9645\u8d1f\u8f7d\u52a8\u6001\u8c03\u6574\u6279\u91cf\u5927\u5c0f
\ndef adaptive_batch_size(current_fps, target_fps=30):
\n if current_fps < target_fps * 0.8:
\n return min(8, current_batch_size * 2)
\n elif current_fps > target_fps * 1.2:
\n return max(1, current_batch_size \/\/ 2)
\n return current_batch_size<\/p>\n 2. \u542f\u7528Triton\u52a8\u6001\u6279\u5904\u7406 \u5728\u6a21\u578b\u914d\u7f6e\u4e2d\u8c03\u6574dynamic_batching\u53c2\u6570：<\/p>\n dynamic_batching {
\n preferred_batch_size: [1, 2, 4, 8, 16]
\n max_queue_delay_microseconds: 5000 # \u589e\u52a0\u961f\u5217\u7b49\u5f85\u65f6\u95f4
\n}<\/p>\n 3. \u6a21\u578b\u91cf\u5316 \u5bf9\u4e8e\u8fb9\u7f18\u8bbe\u5907，\u53ef\u4ee5\u8003\u8651INT8\u91cf\u5316：<\/p>\n # \u91cf\u5316\u6a21\u578b\u8f6c\u6362
\nmodel = torch.quantization.quantize_dynamic(
\n model, {torch.nn.Linear}, dtype=torch.qint8
\n)<\/p>\n 7. \u751f\u4ea7\u73af\u5883\u90e8\u7f72\u5efa\u8bae<\/h3>\n7.1 \u9ad8\u53ef\u7528\u67b6\u6784<\/h4>\n\u5bf9\u4e8e\u751f\u4ea7\u73af\u5883，\u5efa\u8bae\u91c7\u7528\u4ee5\u4e0b\u67b6\u6784：<\/p>\n [\u8d1f\u8f7d\u5747\u8861\u5668]
\n |
\n +—————-+—————-+
\n | | |
\n[Triton\u8282\u70b91] [Triton\u8282\u70b92] [Triton\u8282\u70b93]
\n GPU1 GPU2 GPU3
\n | | |
\n +—————-+—————-+
\n |
\n [Redis\u7f13\u5b58\u5c42]
\n |
\n [\u5e94\u7528\u670d\u52a1\u5668]
\n |
\n [\u5ba2\u6237\u7aef]<\/p>\n \u5173\u952e\u7ec4\u4ef6\u8bf4\u660e：<\/p>\n \u8d1f\u8f7d\u5747\u8861\u5668：\u5206\u53d1\u63a8\u7406\u8bf7\u6c42\u5230\u591a\u4e2aTriton\u8282\u70b9<\/li>\n\u591aTriton\u8282\u70b9：\u6bcf\u4e2a\u8282\u70b9\u90e8\u7f72\u5728\u72ec\u7acb\u7684GPU\u670d\u52a1\u5668\u4e0a<\/li>\nRedis\u7f13\u5b58：\u7f13\u5b58\u9884\u5904\u7406\u7ed3\u679c\u548c\u5e38\u7528\u63a8\u7406\u7ed3\u679c<\/li>\n\u5065\u5eb7\u68c0\u67e5：\u5b9a\u671f\u68c0\u67e5\u5404\u8282\u70b9\u72b6\u6001，\u81ea\u52a8\u5254\u9664\u6545\u969c\u8282\u70b9<\/li>\n7.2 \u76d1\u63a7\u4e0e\u544a\u8b66<\/h4>\nPrometheus\u76d1\u63a7\u914d\u7f6e<\/p>\n # prometheus.yml
\nscrape_configs:
\n – job_name: 'triton'
\n static_configs:
\n – targets: ['triton1:8002', 'triton2:8002', 'triton3:8002']<\/p>\n – job_name: 'aiglasses'
\n static_configs:
\n – targets: ['app-server:8081']<\/p>\n \u5173\u952e\u76d1\u63a7\u6307\u6807<\/p>\n \n- GPU\u5229\u7528\u7387\u3001\u663e\u5b58\u4f7f\u7528\u7387<\/li>\n
- \u63a8\u7406\u5ef6\u8fdf（P50\u3001P95\u3001P99）<\/li>\n
- \u8bf7\u6c42\u541e\u5410\u91cf（QPS）<\/li>\n
- \u9519\u8bef\u7387\u3001\u8d85\u65f6\u7387<\/li>\n
- \u7cfb\u7edf\u8d44\u6e90（CPU\u3001\u5185\u5b58\u3001\u7f51\u7edc）<\/li>\n<\/ul>\n
7.3 \u81ea\u52a8\u6269\u7f29\u5bb9<\/h4>\n\u4f7f\u7528Kubernetes\u5b9e\u73b0\u81ea\u52a8\u6269\u7f29\u5bb9：<\/p>\n # deployment.yaml
\napiVersion: apps\/v1
\nkind: Deployment
\nmetadata:
\n name: triton-deployment
\nspec:
\n replicas: 2
\n selector:
\n matchLabels:
\n app: triton
\n template:
\n metadata:
\n labels:
\n app: triton
\n spec:
\n containers:
\n – name: triton
\n image: nvcr.io\/nvidia\/tritonserver:23.10-py3
\n resources:
\n limits:
\n nvidia.com\/gpu: 1
\n ports:
\n – containerPort: 8000
\n – containerPort: 8001
\n – containerPort: 8002
\n—
\napiVersion: autoscaling\/v2
\nkind: HorizontalPodAutoscaler
\nmetadata:
\n name: triton-hpa
\nspec:
\n scaleTargetRef:
\n apiVersion: apps\/v1
\n kind: Deployment
\n name: triton-deployment
\n minReplicas: 2
\n maxReplicas: 10
\n metrics:
\n – type: Resource
\n resource:
\n name: cpu
\n target:
\n type: Utilization
\n averageUtilization: 70<\/p>\n 8. \u603b\u7ed3<\/h3>\n\u901a\u8fc7\u96c6\u6210NVIDIA Triton\u63a8\u7406\u670d\u52a1\u5668，\u6211\u4eec\u6210\u529f\u5c06AIGlasses_for_navigation\u7684AI\u63a8\u7406\u6027\u80fd\u63d0\u5347\u4e8610\u500d\u4ee5\u4e0a\u3002\u8fd9\u4e0d\u4ec5\u89e3\u51b3\u4e86\u5b9e\u65f6\u6027\u7684\u95ee\u9898，\u8fd8\u4e3a\u7cfb\u7edf\u7684\u6269\u5c55\u6027\u6253\u4e0b\u4e86\u575a\u5b9e\u57fa\u7840\u3002<\/p>\n \u5173\u952e\u6536\u83b7：<\/p>\n - \u6027\u80fd\u5927\u5e45\u63d0\u5347：\u4ece200-500ms\u7684\u63a8\u7406\u5ef6\u8fdf\u964d\u4f4e\u523020-50ms，\u771f\u6b63\u5b9e\u73b0\u4e86\u5b9e\u65f6\u5bfc\u822a<\/li>\n
- \u652f\u6301\u591a\u8def\u5e76\u53d1：\u53ef\u4ee5\u540c\u65f6\u5904\u7406\u591a\u4e2a\u6444\u50cf\u5934\u6570\u636e，\u4e3a\u591a\u7528\u6237\u573a\u666f\u505a\u597d\u51c6\u5907<\/li>\n
- \u751f\u4ea7\u7ea7\u7279\u6027：\u83b7\u5f97\u4e86\u52a8\u6001\u6279\u5904\u7406\u3001\u6a21\u578b\u7248\u672c\u7ba1\u7406\u3001\u76d1\u63a7\u544a\u8b66\u7b49\u4f01\u4e1a\u7ea7\u529f\u80fd<\/li>\n
- \u67b6\u6784\u89e3\u8026：\u63a8\u7406\u670d\u52a1\u4e0e\u4e1a\u52a1\u903b\u8f91\u5206\u79bb，\u63d0\u9ad8\u4e86\u7cfb\u7edf\u7684\u7a33\u5b9a\u6027\u548c\u53ef\u7ef4\u62a4\u6027<\/li>\n
\u4e0b\u4e00\u6b65\u5efa\u8bae：<\/p>\n \u5982\u679c\u4f60\u6b63\u5728\u8003\u8651\u5c06AIGlasses_for_navigation\u6295\u5165\u5b9e\u9645\u4f7f\u7528，\u6211\u5efa\u8bae：<\/p>\n - \u4ece\u5c0f\u89c4\u6a21\u5f00\u59cb：\u5148\u7528\u5355\u53f0GPU\u670d\u52a1\u5668\u9a8c\u8bc1\u6548\u679c<\/li>\n
- \u9010\u6b65\u6269\u5c55：\u6839\u636e\u7528\u6237\u91cf\u589e\u957f，\u9010\u6b65\u589e\u52a0Triton\u8282\u70b9<\/li>\n
- \u6301\u7eed\u76d1\u63a7：\u5efa\u7acb\u5b8c\u5584\u7684\u76d1\u63a7\u4f53\u7cfb，\u53ca\u65f6\u53d1\u73b0\u548c\u89e3\u51b3\u95ee\u9898<\/li>\n
- \u8003\u8651\u8fb9\u7f18\u90e8\u7f72：\u5bf9\u4e8e\u79fb\u52a8\u573a\u666f，\u53ef\u4ee5\u7814\u7a76Jetson\u7b49\u8fb9\u7f18\u8bbe\u5907<\/li>\n
GPU\u7b97\u529b\u9002\u914d\u4e0d\u662f\u7ec8\u70b9，\u800c\u662f\u667a\u80fd\u5bfc\u822a\u7cfb\u7edf\u8d70\u5411\u6210\u719f\u5e94\u7528\u7684\u8d77\u70b9\u3002\u968f\u7740\u786c\u4ef6\u6210\u672c\u7684\u964d\u4f4e\u548c\u8f6f\u4ef6\u751f\u6001\u7684\u5b8c\u5584，\u6211\u76f8\u4fe1\u4f1a\u6709\u8d8a\u6765\u8d8a\u591a\u7684\u667a\u80fd\u8bbe\u5907\u80fd\u591f\u4e3a\u7528\u6237\u63d0\u4f9b\u771f\u6b63\u5b9e\u65f6\u3001\u53ef\u9760\u7684\u5bfc\u822a\u670d\u52a1\u3002<\/p>\n
\n\u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n \u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f？\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a，\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf，\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df，\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":" AIGlasses_for_navigation GPU\u7b97\u529b\u9002\u914d：\u652f\u6301NVIDIA Triton\u63a8\u7406\u670d\u52a1\u5668\u96c6\u6210
\n1. \u5f15\u8a00
\n\u5982\u679c\u4f60\u6b63\u5728\u4f7f\u7528AIGlasses_for_navigation\u8fd9\u5957\u667a\u80fd\u5bfc\u822a\u7cfb\u7edf，\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e2a\u73b0\u5b9e\u95ee\u9898：\u968f\u7740\u7528\u6237\u91cf\u589e\u52a0，\u6216\u8005\u9700\u8981\u540c\u65f6\u5904\u7406\u591a\u4e2a\u6444\u50cf\u5934\u7684\u6570\u636e\u6d41\u65f6，\u5355\u9760CPU\u8fdb\u884cAI\u63a8\u7406\u4f1a\u53d8\u5f97\u975e\u5e38\u5403\u529b\u3002\u753b\u9762\u5361\u987f\u3001\u8bc6\u522b\u5ef6\u8fdf\u3001\u8bed\u97f3\u54cd\u5e94\u53d8\u6162\u2014\u2014\u8fd9\u4e9b\u90fd\u4f1a\u76f4\u63a5\u5f71\u54cd\u7528\u6237\u4f53\u9a8c，\u7279\u522b\u662f\u5bf9\u4e8e<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[9142,7801,9063,5962],"topic":[],"class_list":["post-83158","post","type-post","status-publish","format-standard","hentry","category-server","tag-triton","tag-7801","tag-9063","tag-gpu"],"yoast_head":"\n AIGlasses_for_navigationGPU\u7b97\u529b\u9002\u914d\uff1a\u652f\u6301NVIDIA Triton\u63a8\u7406\u670d\u52a1\u5668\u96c6\u6210 - 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