内网加载 Docker 镜像以及使用 Flask 封装接口

Author: ACatSmiling

Since: 2024-11-23

内网机加载 Docker 镜像，并使用 Flask 封装接口。

  1. 此步骤针对内网机，首先，上传打包好的基础镜像文件到服务器，然后加载。

    		[root@zeloud ~]# docker load -i similar.tar 
    		9853575bc4f9: Loading layer [==================================================>]  77.83MB/77.83MB
    		1505ca8b8119: Loading layer [==================================================>]  9.539MB/9.539MB
    		3548b2e74780: Loading layer [==================================================>]  32.89MB/32.89MB
    		2b728766068e: Loading layer [==================================================>]  4.608kB/4.608kB
    		63d299102447: Loading layer [==================================================>]  12.08MB/12.08MB
    		9967d022da90: Loading layer [==================================================>]  2.048kB/2.048kB
    		e8f0676a25c7: Loading layer [==================================================>]   2.56kB/2.56kB
    		d495473888f3: Loading layer [==================================================>]  6.656kB/6.656kB
    		9ebdfa55c24e: Loading layer [==================================================>]  241.8MB/241.8MB
    		24ac2bd8b6a9: Loading layer [==================================================>]  15.67MB/15.67MB
    		77faf2b53f78: Loading layer [==================================================>]  1.025GB/1.025GB
    		b8e9b30bceef: Loading layer [==================================================>]  820.6MB/820.6MB
    		5a00afc95457: Loading layer [==================================================>]  829.3MB/829.3MB
    		Loaded image: 10.3.170.88:5000/user/admin/similar:latest

  2. 根据实际情况，确定是否需要重新打标签。

    		[root@zeloud ~]# docker images | grep /user/admin
    		10.3.164.57:5000/user/admin/similar                                 flask                         3d4e06e095fa   3 days ago      3.03GB
    		10.3.170.88:5000/user/admin/onnxruntime                             wenet3                        03298f22daff   5 days ago      4.83GB
    		registry.zeloud.com:5000/user/admin/onnxruntime                     wenet3                        03298f22daff   5 days ago      4.83GB
    		10.3.170.88:5000/user/admin/esr_train_img                           v1.1.0                        18d583b4bbaf   6 weeks ago     24.2GB
    		10.3.170.88:5000/user/admin/segan_2.2_docker                        v1.1                          b2118a02a24d   6 weeks ago     31.7GB
    		10.3.170.88:5000/user/admin/segan_2.2_docker                        v1.0                          040f7f705966   6 weeks ago     31.7GB
    		10.3.170.88:5000/user/admin/esr_train_img                           v1.0.0                        0212bfcc9ba6   6 months ago    24.2GB
    		10.3.170.88:5000/user/admin/pytorch-py38-cuda11                     base-centos7                  c806bde76f8c   7 months ago    5.95GB
    		10.3.170.88:5000/user/admin/fairseq-tools-202103                    v1.0                          ed750c76036b   13 months ago   21.3GB
    		10.3.170.88:5000/user/admin/obj_detect_v1                           latest                        d64244ed90a3   14 months ago   5.64GB
    		10.3.170.88:5000/user/admin/segan_2.1_docker                        latest                        d9565fdef5ad   17 months ago   31.7GB
    		[root@zeloud ~]# docker tag 10.3.164.57:5000/user/admin/similar:flask 10.3.170.88:5000/user/admin/image-similar:dinov2
    		[root@zeloud ~]# docker images | grep /user/admin
    		10.3.164.57:5000/user/admin/image-similar                           dinov2                        3d4e06e095fa   3 days ago      3.03GB
    		10.3.164.57:5000/user/admin/similar                                 flask                         3d4e06e095fa   3 days ago      3.03GB
    		10.3.170.88:5000/user/admin/onnxruntime                             wenet3                        03298f22daff   5 days ago      4.83GB
    		registry.zeloud.com:5000/user/admin/onnxruntime                     wenet3                        03298f22daff   5 days ago      4.83GB
    		10.3.170.88:5000/user/admin/esr_train_img                           v1.1.0                        18d583b4bbaf   6 weeks ago     24.2GB
    		10.3.170.88:5000/user/admin/segan_2.2_docker                        v1.1                          b2118a02a24d   6 weeks ago     31.7GB
    		10.3.170.88:5000/user/admin/segan_2.2_docker                        v1.0                          040f7f705966   6 weeks ago     31.7GB
    		10.3.170.88:5000/user/admin/esr_train_img                           v1.0.0                        0212bfcc9ba6   6 months ago    24.2GB
    		10.3.170.88:5000/user/admin/pytorch-py38-cuda11                     base-centos7                  c806bde76f8c   7 months ago    5.95GB
    		10.3.170.88:5000/user/admin/fairseq-tools-202103                    v1.0                          ed750c76036b   13 months ago   21.3GB
    		10.3.170.88:5000/user/admin/obj_detect_v1                           latest                        d64244ed90a3   14 months ago   5.64GB
    		10.3.170.88:5000/user/admin/segan_2.1_docker                        latest                        d9565fdef5ad   17 months ago   31.7GB
    		[root@zeloud ~]# docker push 10.3.170.88:5000/user/admin/image-similar:dinov2
    		The push refers to repository [10.3.170.88:5000/user/admin/image-similar]
    		Get "https://10.3.170.88:5000/v2/": http: server gave HTTP response to HTTPS client

  3. 查看 Docker 基础镜像，是否已安装 Flask，如果没有，则 pip 安装。

    		[root@zeloud ~]# docker images | grep similar
    		registry.zeloud.com:5000/user/admin/similar                    latest                e7edddd7af1c   2 days ago      3.02GB
    		[root@zeloud ~]# docker run -it --rm --entrypoint /bin/bash e7edddd7af1c
    		root@bb3bfdda299e:/app# pip --version
    		pip 24.2 from /usr/local/lib/python3.10/site-packages/pip (python 3.10)
    		root@bb3bfdda299e:/app# python --version
    		Python 3.10.14
    		root@bb3bfdda299e:/app# pip list | grep Flask
    		root@bb3bfdda299e:/app# pip install flask -i https://depend.zeloud.com/artifactory/api/pypi/pypi-repo/simple
    		Looking in indexes: https://depend.zeloud.com/artifactory/api/pypi/pypi-repo/simple
    		Collecting flask
    		  Downloading https://depend.zeloud.com/artifactory/api/pypi/pypi-repo/packages/packages/af/47/93213ee66ef8fae3b93b3e29206f6b251e65c97bd91d8e1c5596ef15af0a/flask-3.1.0-py3-none-any.whl (102 kB)
    		Collecting Werkzeug>=3.1 (from flask)
    		  Downloading https://depend.zeloud.com/artifactory/api/pypi/pypi-repo/packages/packages/52/24/ab44c871b0f07f491e5d2ad12c9bd7358e527510618cb1b803a88e986db1/werkzeug-3.1.3-py3-none-any.whl (224 kB)
    		Requirement already satisfied: Jinja2>=3.1.2 in /usr/local/lib/python3.10/site-packages (from flask) (3.1.3)
    		Collecting itsdangerous>=2.2 (from flask)
    		  Downloading https://depend.zeloud.com/artifactory/api/pypi/pypi-repo/packages/packages/04/96/92447566d16df59b2a776c0fb82dbc4d9e07cd95062562af01e408583fc4/itsdangerous-2.2.0-py3-none-any.whl (16 kB)
    		Requirement already satisfied: click>=8.1.3 in /usr/local/lib/python3.10/site-packages (from flask) (8.1.7)
    		Collecting blinker>=1.9 (from flask)
    		  Downloading https://depend.zeloud.com/artifactory/api/pypi/pypi-repo/packages/packages/10/cb/f2ad4230dc2eb1a74edf38f1a38b9b52277f75bef262d8908e60d957e13c/blinker-1.9.0-py3-none-any.whl (8.5 kB)
    		Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/site-packages (from Jinja2>=3.1.2->flask) (2.1.5)
    		Installing collected packages: Werkzeug, itsdangerous, blinker, flask
    		Successfully installed Werkzeug-3.1.3 blinker-1.9.0 flask-3.1.0 itsdangerous-2.2.0
    		WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
    		 
    		[notice] A new release of pip is available: 24.2 -> 24.3.1
    		[notice] To update, run: pip install --upgrade pip
    		root@bb3bfdda299e:/app# pip list | grep Flask
    		Flask              3.1.0

    安装 Flask 如果碰到依赖冲突，可以强制安装：

    		root@bb3bfdda299e:/# pip install blinker==1.9 --ignore-installed -i https://depend.zeloud.com/artifactory/api/pypi/pypi-repo/simple

  4. 新打开一个终端，查看上一步生成的容器，commit 为新镜像，并 push 到仓库，然后，退出上一步生成的容器。

    		[root@zeloud ~]# docker ps | grep e7edddd7af1c
    		bb3bfdda299e   e7edddd7af1c                                         "bash"                   8 minutes ago   Up 8 minutes                                                                                                               dazzling_clarke
    		[root@zeloud ~]# docker commit bb3bfdda299e 10.3.170.88:5000/user/admin/similar:flask
    		sha256:3d4e06e095fa5e222ac2e3798ac0642221356fc1191a759f34bc0d451ad9985c
    		[root@zeloud ~]# docker push 10.3.170.88:5000/user/admin/similar:flask
    		The push refers to repository [10.3.170.88:5000/user/admin/similar]
    		96894b0a9bbe: Pushed 
    		5a00afc95457: Pushed 
    		b8e9b30bceef: Pushed 
    		77faf2b53f78: Pushed 
    		24ac2bd8b6a9: Pushed 
    		9ebdfa55c24e: Pushed 
    		d495473888f3: Pushed 
    		e8f0676a25c7: Pushed 
    		9967d022da90: Pushed 
    		63d299102447: Pushed 
    		2b728766068e: Pushed 
    		3548b2e74780: Pushed 
    		1505ca8b8119: Pushed 
    		9853575bc4f9: Pushed 
    		flask: digest: sha256:8822d2a1476f0b58852052074e9165de27db8a90e93d3dd0b2ab5cdde5a8e718 size: 3266
    		[root@zeloud onnx-wenet-predict]# docker images | grep similar
    		10.3.170.88:5000/user/admin/similar                    flask                 3d4e06e095fa   6 minutes ago   3.03GB
    		10.3.170.88:5000/user/admin/similar                    latest                e7edddd7af1c   2 days ago      3.02GB

  5. 如果需要迁移到其他内网机，可以将新镜像保存到本地，拷贝之后，使用 docker load 命令加载。

    		# 打包保存
    		[root@zeloud ~]# docker save -o similar_flask.tar 10.3.170.88:5000/user/admin/similar:flask
    		# 或者打包之后进行压缩，docker load 之前，需要先 gunzip 解压
    		[root@zeloud ~]# docker save 10.3.170.88:5000/user/admin/similar:flask | gzip > ./similar_flask.tar.gz

  6. 使用新镜像启动容器，挂载 Flask 封装接口的路径。如果镜像没有默认启动 bash，则在 -c 参数前面加上 /bin/bash。如果希望后台启动，则添加 -d 参数。

    		[root@xd-dev2 admin]# docker run -it --rm -p 31700:7000 -v /zeloud/volume/turing/gv1/admin/similar-predict/:/zeloud/similar-predict 3d4e06e095fa -c "cd /zeloud/similar-predict/ && python flask-app.py" 
    		 * Serving Flask app 'flask-app'
    		 * Debug mode: off
    		WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
    		 * Running on all addresses (0.0.0.0)
    		 * Running on http://127.0.0.1:7000
    		 * Running on http://172.17.0.7:7000
    		Press CTRL+C to quit

    查看镜像的信息：

    		$ docker inspect <imageId>

  7. postman 请求调用示例：

    image-20241122142150598

  8. Flask 封装接口示例：

    		from flask import Flask, request, jsonify
    		import subprocess
    		import os
    		import io
    		import torch
    		from transformers import AutoImageProcessor, AutoModel
    		from PIL import Image
    		import torch.nn as nn
    		 
    		app = Flask(__name__)
    		 
    		def convert(image_data):
    		    image_stream = io.BytesIO(image_data)
    		    return Image.open(image_stream)
    		 
    		@app.route("/predict", methods=["POST"])
    		def predict():
    		    try:
    		        # 获取请求中的图片数据
    		        if 'image' not in request.files:
    		            return jsonify({"error": "No image file in the request"}), 400
    		        if 'image2' not in request.files:
    		            return jsonify({"error": "No pk image file in the request"}), 400
    		 
    		        image_data = request.files['image'].read()
    		        image2_data = request.files['image2'].read()
    		 
    		        # 将图片数据转换为 PIL 的 Image 格式
    		        image1 = convert(image_data)
    		        image2 = convert(image2_data)
    		 
    		        # 设置设备为GPU，如果不可用则使用CPU
    		        device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
    		 
    		        # 加载预训练的图像处理器
    		        processor = AutoImageProcessor.from_pretrained('./models/dinov2-base',
    		                                                        local_files_only=True)
    		 
    		        # 加载预训练的模型，并将其移至相应的设备（GPU或CPU）
    		        model = AutoModel.from_pretrained('./models/dinov2-base',
    		                                        local_files_only=True).to(device)
    		 
    		        # 打开第一张图片
    		        # image1 = Image.open('5.jpg')
    		 
    		        # 不计算梯度，用于推断
    		        with torch.no_grad():
    		            # 使用处理器处理图像，转换为PyTorch张量，并移至相应设备
    		            inputs1 = processor(images=image1, return_tensors="pt").to(device)
    		            # 通过模型获取输出
    		            outputs1 = model(**inputs1)
    		            # 获取最后一层的隐藏状态
    		            image_features1 = outputs1.last_hidden_state
    		            # 对特征取平均，以得到单个向量表示
    		            image_features1 = image_features1.mean(dim=1)
    		            print(f"outputs1 is {image_features1.shape}")
    		 
    		        # 重复上述过程，处理第二张图片
    		        # image2 = Image.open('3.jpg')
    		        with torch.no_grad():
    		            inputs2 = processor(images=image2, return_tensors="pt").to(device)
    		            outputs2 = model(**inputs2)
    		            image_features2 = outputs2.last_hidden_state
    		            image_features2 = image_features2.mean(dim=1)
    		            print(f"outputs2 is {image_features2.shape}")
    		 
    		        # 使用余弦相似度计算两个向量的相似度
    		        cos = nn.CosineSimilarity(dim=0)
    		        sim = cos(image_features1[0],image_features2[0]).item()
    		        # 将相似度值调整到[0,1]范围内
    		        sim = (sim+1)/2
    		        print('similarity:', sim)
    		        return jsonify({'status': 'success', 'similarity': sim})
    		 
    		    except Exception as e:
    		        return jsonify({"error": str(e)})
    		 
    		if __name__ == "__main__":
    		    app.run(host="0.0.0.0", port=7000)
posted @   ACatSmiling  阅读(48)  评论(0)  编辑  收藏  举报
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