我正在部署一个 Flask 应用程序，该应用程序使用 PyTorch 提供机器学习模型（打包为 Docker 容器）到 Vertex AI 端点以进行在线预测。尽管
Flask 应用程序在容器内成功启动（如我的日志所示），但我尝试将模型部署到 Vertex AI 端点始终失败。
以下是我的设置的详细信息：
型号名称：
EnsembleFlaskClassifierV28528383931077099520asia-southeast1asia-southeast1-docker.pkg.dev/fyp-jx-416511/ensembleflask/ensembleflask-app:latestn1-standard-4我的 Flask 应用程序 (ensemble_deploy_1.py) 成功初始化并加载 PyTorch 模型，并设计为在收到 POST 请求时使用集成方法进行预测。 Flask 应用程序设置为在端口 5001 上运行，并且这已在 Dockerfile 中正确公开和指定。
Dockerfile配置：
# Use an official PyTorch image as the base
FROM pytorch/pytorch:1.7.1-cuda11.0-cudnn8-runtime
# Set the working directory in the container
WORKDIR /usr/src/app
# Install dependencies
RUN pip install numpy Pillow flask torch torchvision
# Copy the Flask script, model files, and entrypoint script into the container
COPY ensemble_deploy_1.py .
COPY Models/DenseNet_Optimal.pt .
COPY Models/ResNext_Optimal.pt .
COPY Models/MobileNetV2_Optimal.pt .
COPY entrypoint.sh .
# Make the entrypoint script executable
RUN chmod +x entrypoint.sh
# Set environment variable to specify the Flask application
ENV FLASK_APP=ensemble_deploy_1.py
# EXPOSE command is commented out because it's not necessary for Vertex AI
# EXPOSE 5001
# Use entrypoint.sh to start the service
ENTRYPOINT ["./entrypoint.sh"]
入口点.sh：
#!/bin/sh
# This script sets the FLASK_APP environment variable and starts the Flask server
export FLASK_APP=ensemble_deploy_1.py
flask run --host=0.0.0.0 --port=5001
ensemble_deploy_1.py：
from flask import Flask, request, jsonify
import torch
from torchvision import models, transforms
from PIL import Image
import io
import torch.nn.functional as F
import logging
from logging.handlers import RotatingFileHandler
app = Flask(__name__)
# Define global device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Define the preprocess function
def preprocess_image(image_bytes):
preprocess = transforms.Compose([
transforms.Resize((224, 224)),
transforms.Grayscale(num_output_channels=3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
image = preprocess(image).unsqueeze(0)
return image.to(device)
# Model initialization functions
def initialize_model(model_name):
if model_name == "densenet":
model = models.densenet121(pretrained=False)
num_ftrs = model.classifier.in_features
model.classifier = torch.nn.Linear(num_ftrs, 7)
elif model_name == "resnext":
model = models.resnext50_32x4d(pretrained=False)
num_ftrs = model.fc.in_features
model.fc = torch.nn.Linear(num_ftrs, 7)
elif model_name == "mobilenetv2":
model = models.mobilenet_v2(pretrained=False)
num_ftrs = model.classifier[1].in_features
model.classifier = torch.nn.Sequential(
torch.nn.Dropout(0.2),
torch.nn.Linear(num_ftrs, 7)
)
else:
raise ValueError("Invalid model name")
return model.to(device).eval()
# Initialize and load models outside the request handler
model_paths = {
'densenet': 'DenseNet_Optimal.pt',
'resnext': 'ResNext_Optimal.pt',
'mobilenetv2': 'MobileNetV2_Optimal.pt'
}
models = {name: initialize_model(name) for name in model_paths}
for name, model in models.items():
model.load_state_dict(torch.load(model_paths[name], map_location=device))
# Define your F1 scores
all_model_f1_scores = {
'Angry': {'DenseNet': 0.62, 'ResNext': 0.63, 'MobileNetV2': 0.60},
'Disgust': {'DenseNet': 0.57, 'ResNext': 0.73, 'MobileNetV2': 0.63},
'Fear': {'DenseNet': 0.51, 'ResNext': 0.54, 'MobileNetV2': 0.51},
'Happy': {'DenseNet': 0.89, 'ResNext': 0.89, 'MobileNetV2': 0.88},
'Neutral': {'DenseNet': 0.67, 'ResNext': 0.66, 'MobileNetV2': 0.66},
'Sad': {'DenseNet': 0.57, 'ResNext': 0.58, 'MobileNetV2': 0.58},
'Surprise': {'DenseNet': 0.81, 'ResNext': 0.81, 'MobileNetV2': 0.79},
}
class_names = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
def predict_with_ensemble(image_tensor, models, f1_scores, class_names):
# Ensure the image tensor is on the correct device
image_tensor = image_tensor.to(device)
weighted_preds = torch.zeros(1, len(class_names), device=device)
for model_name, model in models.items():
with torch.no_grad():
outputs = model(image_tensor)
probs = F.softmax(outputs, dim=1)
for i, class_name in enumerate(class_names):
# Multiply by F1 score if available, else default to 1 (no weighting)
f1_weight = f1_scores.get(class_name, {}).get(model_name, 1)
weighted_preds[:, i] += probs[:, i] * f1_weight
final_pred = torch.argmax(weighted_preds, dim=1)
predicted_class = class_names[final_pred.item()]
return predicted_class
# Setup logging
handler = RotatingFileHandler('app.log', maxBytes=10000, backupCount=3)
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
logger.addHandler(handler)
@app.route('/predict', methods=['POST'])
def predict():
logger.info("Received prediction request")
if request.method == 'POST':
# Convert string of image data to uint8
if 'file' not in request.files:
return jsonify({'error': 'No file part'})
file = request.files['file']
if file.filename == '':
return jsonify({'error': 'No selected file'})
if file:
image_bytes = file.read()
image_tensor = preprocess_image(image_bytes)
predicted_class = predict_with_ensemble(image_tensor, models, all_model_f1_scores, class_names)
return jsonify({'predicted_class': predicted_class})
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0', port=5001)
日志：
信息 * 服务 Flask 应用程序“ensemble_deploy_1.py”信息 * 调试模式： 关闭 错误 * CUDA 初始化：在您的计算机上找不到 NVIDIA 驱动程序 系统。警告：这是一个开发服务器。请勿将其用于 生产部署。
- 在所有地址上运行。
- 在 http://127.0.0.1:5001 上运行（按 CTRL+C 退出）
我尝试在其他区域而不是 asia-southeast1 上部署，尝试使用其他机器类型进行部署。我还尝试在本地测试容器并且它有效。
我已验证 FLASK_APP 环境变量已设置，并且本地运行容器按预期工作。但是，部署到 Vertex AI 失败。
不确定是不是因为将基于 Flask 的容器部署到 Vertex AI 端点时需要额外的配置？我现在有点迷失了。
您的问题解决了吗？我坚持同样的事情