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社区首页 >专栏 >这个深度学习演示平台可以一键部署运行,既能演示还支持restapi,代码已开源

这个深度学习演示平台可以一键部署运行,既能演示还支持restapi,代码已开源

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AI研习社
发布2020-02-12 11:58:30
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发布2020-02-12 11:58:30
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文章被收录于专栏:AI研习社AI研习社
1项目介绍

许多深度学习项目都有自己的展示demo,能够最快速的展示项目效果。今天介绍的是一个demo展示集合体,包含了人脸检测,图像分割,人脸识别和生成等19个项目。

项目利用flask 开发,restapi 调用后端算法,前端 js 进行图像预处理和结果展现。使用 docker 容器解决各个算法依赖差别的问题,doker-compose 进行管理。

人脸检测

图像分割

人脸3D重建

2

食用指南

项目部署在 https://pc.zzz9958123.com:5000/ ,可以体验其中的演示项目。

进入后是一个导航页:

进入其中的stylegan人脸插值项目

点击下方的示例图片,或者使用上传,url,摄像头等其他方式输入一张图片。选择右上角的插值对象,即可获取输入图像到明星脸的变化过程。

我们这里在网上找了一张图片,得到了如下的转变过程。

(https://img.zzz9958123.com/1bcc041fbb83ae61fb97f1ce2be8c74f_lyf_stylegan.gif)

(https://img.zzz9958123.com/1bcc041fbb83ae61fb97f1ce2be8c74f_lyf_stylegansmall.gif)

类似的,进入stylegan属性编辑项目,我们也可以让蒙娜丽莎闭上眼睛。

其他的项目也可以自己前往体验。

3

restapi

除了demo,还支持了restapi调用。response json中记录了返回的数据结构。

尝试python3调用

代码语言:javascript
复制
import requests
import base64
res = requests.post("https://pc.zzz9958123.com:5000/openface",data={"base64":b"data:img/png;base64,"+base64.b64encode(open("abc.png","rb").read())}).content
print(res)
/*
* 提示:该行代码过长,系统自动注释不进行高亮。一键复制会移除系统注释 
* b'{"faces": [{"bounding_boxes": {"x1": 130, "y1": 213, "x2": 372, "y2": 445}, "confidence": "0.975", "kpoint": [[131, 253], [130, 284], [133, 314], [140, 344], [152, 371], [169, 397], [187, 419], [207, 439], [232, 445], [258, 443], [285, 429], [312, 409], [336, 385], [353, 354], [362, 320], [368, 285], [372, 249], [139, 226], [152, 215], [169, 214], [187, 219], [203, 226], [250, 226], [272, 217], [294, 213], [316, 216], [335, 228], [226, 258], [224, 281], [223, 305], [221, 329], [202, 336], [213, 342], [225, 347], [238, 342], [251, 338], [160, 257], [172, 251], [187, 253], [200, 263], [186, 266], [171, 265], [262, 265], [277, 255], [293, 254], [307, 261], [295, 268], [279, 269], [186, 363], [200, 362], [214, 362], [226, 366], [239, 363], [260, 365], [282, 368], [260, 385], [240, 392], [225, 393], [213, 390], [199, 381], [192, 366], [214, 372], [226, 375], [240, 374], [275, 369], [239, 376], [225, 377], [213, 374]], "kpoint3D": {" X_0": -71.0, " X_1": -72.4, " X_2": -71.0, " X_3": -66.6, " X_4": -58.2, " X_5": -45.2, " X_6": -31.6, " X_7": -16.9, " X_8": -0.6, " X_9": 16.7, " X_10": 35.2, " X_11": 54.0, " X_12": 69.6, " X_13": 80.1, " X_14": 85.2, " X_15": 87.4, " X_16": 88.7, " X_17": -59.1, " X_18": -49.2, " X_19": -37.4, " X_20": -26.0, " X_21": -16.6, " X_22": 9.0, " X_23": 20.5, " X_24": 32.4, " X_25": 45.0, " X_26": 55.9, " X_27": -3.7, " X_28": -4.5, " X_29": -5.4, " X_30": -6.2, " X_31": -17.9, " X_32": -11.4, " X_33": -4.5, " X_34": 3.2, " X_35": 10.5, " X_36": -45.7, " X_37": -37.1, " X_38": -27.0, " X_39": -19.1, " X_40": -27.9, " X_41": -37.8, " X_42": 16.7, " X_43": 24.4, " X_44": 33.3, " X_45": 41.3, " X_46": 34.4, " X_47": 25.5, " X_48": -30.0, " X_49": -19.6, " X_50": -10.5, " X_51": -3.9, " X_52": 3.9, " X_53": 15.7, " X_54": 29.5, " X_55": 16.1, " X_56": 4.4, " X_57": -4.2, " X_58": -11.6, " X_59": -20.7, " X_60": -25.4, " X_61": -10.8, " X_62": -3.7, " X_63": 4.2, " X_64": 25.0, " X_65": 3.8, " X_66": -4.4, " X_67": -11.5, " Y_0": -1.4, " Y_1": 20.3, " Y_2": 42.2, " Y_3": 63.9, " Y_4": 84.0, " Y_5": 101.2, " Y_6": 114.5, " Y_7": 123.4, " Y_8": 125.1, " Y_9": 123.3, " Y_10": 115.9, " Y_11": 103.9, " Y_12": 87.1, " Y_13": 65.9, " Y_14": 42.4, " Y_15": 19.1, " Y_16": -3.8, " Y_17": -18.3, " Y_18": -24.5, " Y_19": -24.1, " Y_20": -20.9, " Y_21": -16.0, " Y_22": -15.3, " Y_23": -20.1, " Y_24": -22.4, " Y_25": -21.0, " Y_26": -14.8, " Y_27": 1.4, " Y_28": 14.3, " Y_29": 27.0, " Y_30": 39.6, " Y_31": 47.0, " Y_32": 49.7, " Y_33": 51.6, " Y_34": 49.1, " Y_35": 47.0, " Y_36": 1.2, " Y_37": -2.2, " Y_38": -1.3, " Y_39": 4.7, " Y_40": 6.6, " Y_41": 6.1, " Y_42": 5.3, " Y_43": -0.2, " Y_44": -0.4, " Y_45": 3.2, " Y_46": 7.1, " Y_47": 7.5, " Y_48": 69.2, " Y_49": 64.5, " Y_50": 61.9, " Y_51": 63.4, " Y_52": 61.5, " Y_53": 63.7, " Y_54": 67.2, " Y_55": 75.7, " Y_56": 78.9, " Y_57": 79.9, " Y_58": 79.5, " Y_59": 76.9, " Y_60": 70.0, " Y_61": 69.0, " Y_62": 69.6, " Y_63": 68.5, " Y_64": 68.0, " Y_65": 69.4, " Y_66": 70.3, " Y_67": 69.9, " Z_0": 313.8, " Z_1": 315.8, " Z_2": 319.8, " Z_3": 323.7, " Z_4": 324.0, " Z_5": 320.1, " Z_6": 312.5, " Z_7": 301.1, " Z_8": 294.5, " Z_9": 294.4, " Z_10": 299.3, " Z_11": 302.9, " Z_12": 301.3, " Z_13": 297.8, " Z_14": 293.8, " Z_15": 289.2, " Z_16": 285.5, " Z_17": 283.7, " Z_18": 274.4, " Z_19": 264.8, " Z_20": 256.0, " Z_21": 249.0, " Z_22": 236.0, " Z_23": 235.0, " Z_24": 236.9, " Z_25": 240.5, " Z_26": 245.2, " Z_27": 248.4, " Z_28": 245.8, " Z_29": 243.0, " Z_30": 240.8, " Z_31": 260.1, " Z_32": 255.7, " Z_33": 252.4, " Z_34": 252.2, " Z_35": 252.9, " Z_36": 281.0, " Z_37": 273.2, " Z_38": 268.8, " Z_39": 265.5, " Z_40": 269.0, " Z_41": 273.6, " Z_42": 251.6, " Z_43": 248.5, " Z_44": 246.6, " Z_45": 247.8, " Z_46": 246.9, " Z_47": 248.6, " Z_48": 286.5, " Z_49": 270.1, " Z_50": 260.3, " Z_51": 257.2, " Z_52": 254.6, " Z_53": 258.5, " Z_54": 267.6, " Z_55": 261.3, " Z_56": 257.8, " Z_57": 259.8, " Z_58": 263.4, " Z_59": 272.7, " Z_60": 282.3, " Z_61": 264.0, " Z_62": 260.6, " Z_63": 258.1, " Z_64": 266.4, " Z_65": 257.6, " Z_66": 259.5, " Z_67": 263.3}, "gaze": {" gaze_0_x": 0.050328, " gaze_0_y": 0.246996, " gaze_0_z": -0.967709, " gaze_1_x": -0.237988, " gaze_1_y": 0.259606, " gaze_1_z": -0.935931}, "gazeangel": {" gaze_angle_x": -0.098, " gaze_angle_y": 0.26}, "eye_lmk": {" eye_lmk_x_0": 170.5, " eye_lmk_x_1": 174.5, " eye_lmk_x_2": 183.0, " eye_lmk_x_3": 190.9, " eye_lmk_x_4": 193.7, " eye_lmk_x_5": 190.3, " eye_lmk_x_6": 181.3, " eye_lmk_x_7": 173.3, " eye_lmk_x_8": 161.2, " eye_lmk_x_9": 165.7, " eye_lmk_x_10": 172.2, " eye_lmk_x_11": 180.5, " eye_lmk_x_12": 189.3, " eye_lmk_x_13": 195.4, " eye_lmk_x_14": 199.5, " eye_lmk_x_15": 193.5, " eye_lmk_x_16": 186.2, " eye_lmk_x_17": 178.2, " eye_lmk_x_18": 171.1, " eye_lmk_x_19": 165.4, " eye_lmk_x_20": 177.5, " eye_lmk_x_21": 181.8, " eye_lmk_x_22": 186.5, " eye_lmk_x_23": 188.7, " eye_lmk_x_24": 187.2, " eye_lmk_x_25": 182.9, " eye_lmk_x_26": 178.2, " eye_lmk_x_27": 176.0, " eye_lmk_x_28": 270.1, " eye_lmk_x_29": 272.8, " eye_lmk_x_30": 281.8, " eye_lmk_x_31": 291.8, " eye_lmk_x_32": 296.9, " eye_lmk_x_33": 294.2, " eye_lmk_x_34": 285.2, " eye_lmk_x_35": 274.6, " eye_lmk_x_36": 264.3, " eye_lmk_x_37": 269.2, " eye_lmk_x_38": 276.4, " eye_lmk_x_39": 285.6, " eye_lmk_x_40": 294.2, " eye_lmk_x_41": 301.2, " eye_lmk_x_42": 306.7, " eye_lmk_x_43": 302.1, " eye_lmk_x_44": 295.9, " eye_lmk_x_45": 288.1, " eye_lmk_x_46": 279.2, " eye_lmk_x_47": 270.9, " eye_lmk_x_48": 278.9, " eye_lmk_x_49": 284.2, " eye_lmk_x_50": 289.0, " eye_lmk_x_51": 290.4, " eye_lmk_x_52": 287.7, " eye_lmk_x_53": 282.4, " eye_lmk_x_54": 277.6, " eye_lmk_x_55": 276.2, " eye_lmk_y_0": 257.3, " eye_lmk_y_1": 249.2, " eye_lmk_y_2": 246.3, " eye_lmk_y_3": 250.4, " eye_lmk_y_4": 259.0, " eye_lmk_y_5": 267.8, " eye_lmk_y_6": 270.0, " eye_lmk_y_7": 265.9, " eye_lmk_y_8": 258.4, " eye_lmk_y_9": 254.1, " eye_lmk_y_10": 251.8, " eye_lmk_y_11": 251.2, " eye_lmk_y_12": 253.4, " eye_lmk_y_13": 257.8, " eye_lmk_y_14": 264.2, " eye_lmk_y_15": 265.4, " eye_lmk_y_16": 265.5, " eye_lmk_y_17": 265.0, " eye_lmk_y_18": 263.5, " eye_lmk_y_19": 261.4, " eye_lmk_y_20": 262.4, " eye_lmk_y_21": 264.7, " eye_lmk_y_22": 263.1, " eye_lmk_y_23": 258.7, " eye_lmk_y_24": 253.9, " eye_lmk_y_25": 251.7, " eye_lmk_y_26": 253.2, " eye_lmk_y_27": 257.7, " eye_lmk_y_28": 262.3, " eye_lmk_y_29": 252.7, " eye_lmk_y_30": 248.0, " eye_lmk_y_31": 250.9, " eye_lmk_y_32": 259.8, " eye_lmk_y_33": 269.4, " eye_lmk_y_34": 274.1, " eye_lmk_y_35": 272.0, " eye_lmk_y_36": 266.1, " eye_lmk_y_37": 259.6, " eye_lmk_y_38": 255.5, " eye_lmk_y_39": 253.9, " eye_lmk_y_40": 254.9, " eye_lmk_y_41": 257.7, " eye_lmk_y_42": 262.4, " eye_lmk_y_43": 265.7, " eye_lmk_y_44": 267.7, " eye_lmk_y_45": 268.8, " eye_lmk_y_46": 269.0, " eye_lmk_y_47": 268.4, " eye_lmk_y_48": 267.0, " eye_lmk_y_49": 268.6, " eye_lmk_y_50": 266.1, " eye_lmk_y_51": 261.0, " eye_lmk_y_52": 256.2, " eye_lmk_y_53": 254.6, " eye_lmk_y_54": 257.1, " eye_lmk_y_55": 262.3}, "eye3dlmk": {" eye_lmk_X_0": -32.9, " eye_lmk_X_1": -30.7, " eye_lmk_X_2": -26.2, " eye_lmk_X_3": -22.0, " eye_lmk_X_4": -20.5, " eye_lmk_X_5": -22.3, " eye_lmk_X_6": -27.1, " eye_lmk_X_7": -31.4, " eye_lmk_X_8": -38.5, " eye_lmk_X_9": -35.7, " eye_lmk_X_10": -31.9, " eye_lmk_X_11": -27.3, " eye_lmk_X_12": -22.7, " eye_lmk_X_13": -19.6, " eye_lmk_X_14": -17.6, " eye_lmk_X_15": -20.6, " eye_lmk_X_16": -24.4, " eye_lmk_X_17": -28.6, " eye_lmk_X_18": -32.5, " eye_lmk_X_19": -35.9, " eye_lmk_X_20": -29.2, " eye_lmk_X_21": -26.9, " eye_lmk_X_22": -24.4, " eye_lmk_X_23": -23.3, " eye_lmk_X_24": -24.1, " eye_lmk_X_25": -26.4, " eye_lmk_X_26": -28.8, " eye_lmk_X_27": -30.0, " eye_lmk_X_28": 17.5, " eye_lmk_X_29": 18.9, " eye_lmk_X_30": 23.3, " eye_lmk_X_31": 28.1, " eye_lmk_X_32": 30.4, " eye_lmk_X_33": 28.9, " eye_lmk_X_34": 24.5, " eye_lmk_X_35": 19.6, " eye_lmk_X_36": 14.8, " eye_lmk_X_37": 17.1, " eye_lmk_X_38": 20.5, " eye_lmk_X_39": 24.9, " eye_lmk_X_40": 29.1, " eye_lmk_X_41": 32.7, " eye_lmk_X_42": 35.6, " eye_lmk_X_43": 33.0, " eye_lmk_X_44": 29.7, " eye_lmk_X_45": 25.8, " eye_lmk_X_46": 21.6, " eye_lmk_X_47": 17.8, " eye_lmk_X_48": 21.7, " eye_lmk_X_49": 24.3, " eye_lmk_X_50": 26.6, " eye_lmk_X_51": 27.4, " eye_lmk_X_52": 26.2, " eye_lmk_X_53": 23.6, " eye_lmk_X_54": 21.3, " eye_lmk_X_55": 20.5, " eye_lmk_Y_0": 0.9, " eye_lmk_Y_1": -3.3, " eye_lmk_Y_2": -4.8, " eye_lmk_Y_3": -2.7, " eye_lmk_Y_4": 1.8, " eye_lmk_Y_5": 6.4, " eye_lmk_Y_6": 7.6, " eye_lmk_Y_7": 5.5, " eye_lmk_Y_8": 1.5, " eye_lmk_Y_9": -0.7, " eye_lmk_Y_10": -1.9, " eye_lmk_Y_11": -2.2, " eye_lmk_Y_12": -1.1, " eye_lmk_Y_13": 1.2, " eye_lmk_Y_14": 4.5, " eye_lmk_Y_15": 5.2, " eye_lmk_Y_16": 5.2, " eye_lmk_Y_17": 5.0, " eye_lmk_Y_18": 4.2, " eye_lmk_Y_19": 3.1, " eye_lmk_Y_20": 3.6, " eye_lmk_Y_21": 4.8, " eye_lmk_Y_22": 4.0, " eye_lmk_Y_23": 1.7, " eye_lmk_Y_24": -0.8, " eye_lmk_Y_25": -2.0, " eye_lmk_Y_26": -1.2, " eye_lmk_Y_27": 1.1, " eye_lmk_Y_28": 3.2, " eye_lmk_Y_29": -1.3, " eye_lmk_Y_30": -3.6, " eye_lmk_Y_31": -2.2, " eye_lmk_Y_32": 2.0, " eye_lmk_Y_33": 6.6, " eye_lmk_Y_34": 8.7, " eye_lmk_Y_35": 7.8, " eye_lmk_Y_36": 5.0, " eye_lmk_Y_37": 1.9, " eye_lmk_Y_38": 0.0, " eye_lmk_Y_39": -0.8, " eye_lmk_Y_40": -0.3, " eye_lmk_Y_41": 1.1, " eye_lmk_Y_42": 3.3, " eye_lmk_Y_43": 4.9, " eye_lmk_Y_44": 5.7, " eye_lmk_Y_45": 6.2, " eye_lmk_Y_46": 6.3, " eye_lmk_Y_47": 6.1, " eye_lmk_Y_48": 5.5, " eye_lmk_Y_49": 6.2, " eye_lmk_Y_50": 5.0, " eye_lmk_Y_51": 2.6, " eye_lmk_Y_52": 0.3, " eye_lmk_Y_53": -0.4, " eye_lmk_Y_54": 0.8, " eye_lmk_Y_55": 3.2, " eye_lmk_Z_0": 235.8, " eye_lmk_Z_1": 235.5, " eye_lmk_Z_2": 234.8, " eye_lmk_Z_3": 234.2, " eye_lmk_Z_4": 234.1, " eye_lmk_Z_5": 234.7, " eye_lmk_Z_6": 235.1, " eye_lmk_Z_7": 235.6, " eye_lmk_Z_8": 240.5, " eye_lmk_Z_9": 237.7, " eye_lmk_Z_10": 234.8, " eye_lmk_Z_11": 233.1, " eye_lmk_Z_12": 233.1, " eye_lmk_Z_13": 233.9, " eye_lmk_Z_14": 235.2, " eye_lmk_Z_15": 234.2, " eye_lmk_Z_16": 233.5, " eye_lmk_Z_17": 233.8, " eye_lmk_Z_18": 235.5, " eye_lmk_Z_19": 238.0, " eye_lmk_Z_20": 236.2, " eye_lmk_Z_21": 235.8, " eye_lmk_Z_22": 235.5, " eye_lmk_Z_23": 235.3, " eye_lmk_Z_24": 235.4, " eye_lmk_Z_25": 235.7, " eye_lmk_Z_26": 236.1, " eye_lmk_Z_27": 236.3, " eye_lmk_Z_28": 211.6, " eye_lmk_Z_29": 213.1, " eye_lmk_Z_30": 214.3, " eye_lmk_Z_31": 214.4, " eye_lmk_Z_32": 213.4, " eye_lmk_Z_33": 211.9, " eye_lmk_Z_34": 210.8, " eye_lmk_Z_35": 210.8, " eye_lmk_Z_36": 212.0, " eye_lmk_Z_37": 211.6, " eye_lmk_Z_38": 211.5, " eye_lmk_Z_39": 211.7, " eye_lmk_Z_40": 212.8, " eye_lmk_Z_41": 214.9, " eye_lmk_Z_42": 216.7, " eye_lmk_Z_43": 214.3, " eye_lmk_Z_44": 211.6, " eye_lmk_Z_45": 209.8, " eye_lmk_Z_46": 209.7, " eye_lmk_Z_47": 210.7, " eye_lmk_Z_48": 212.4, " eye_lmk_Z_49": 212.4, " eye_lmk_Z_50": 213.0, " eye_lmk_Z_51": 213.8, " eye_lmk_Z_52": 214.4, " eye_lmk_Z_53": 214.3, " eye_lmk_Z_54": 213.7, " eye_lmk_Z_55": 212.9}, "pose": {" pose_Tx": -0.6, " pose_Ty": 39.9, " pose_Tz": 269.1, " pose_Rx": 0.12300000000000001, " pose_Ry": 0.21, " pose_Rz": -0.015}, "pscale": 1.66, "pr": {" p_rx": 0.29, " p_ry": 0.24100000000000002, " p_rz": -0.022000000000000002}, "pt": {" p_tx": 233.165, " p_ty": 321.263}, "p": {" p_0": -25.059, " p_1": -28.208000000000002, " p_2": -7.044, " p_3": -1.804, " p_4": 8.533999999999999, " p_5": 6.8870000000000005, " p_6": -7.106, " p_7": -3.64, " p_8": -16.565, " p_9": -6.859, " p_10": -7.239, " p_11": 1.09, " p_12": -1.247, " p_13": 0.536, " p_14": 2.923, " p_15": -0.591, " p_16": -4.442, " p_17": 1.595, " p_18": 0.9259999999999999, " p_19": -2.165, " p_20": -0.992, " p_21": -2.079, " p_22": 2.06, " p_23": 0.804, " p_24": 1.236, " p_25": 0.784, " p_26": -1.067, " p_27": 0.505, " p_28": 0.11800000000000001, " p_29": 0.212, " p_30": 0.365, " p_31": -0.369, " p_32": 0.145, " p_33": 0.086}, "AU": {" AU01_r": 1.01, " AU02_r": 0.61, " AU04_r": 0.0, " AU05_r": 0.0, " AU06_r": 0.5, " AU07_r": 0.0, " AU09_r": 0.0, " AU10_r": 0.27, " AU12_r": 1.4, " AU14_r": 0.67, " AU15_r": 0.0, " AU17_r": 0.0, " AU20_r": 0.6, " AU23_r": 0.03, " AU25_r": 0.34, " AU26_r": 0.57, " AU45_r": 0.0, " AU01_c": 1.0, " AU02_c": 1.0, " AU04_c": 0.0, " AU05_c": 0.0, " AU06_c": 1.0, " AU07_c": 1.0, " AU09_c": 0.0, " AU10_c": 0.0, " AU12_c": 1.0, " AU14_c": 1.0, " AU15_c": 0.0, " AU17_c": 0.0, " AU20_c": 1.0, " AU23_c": 0.0, " AU25_c": 0.0, " AU26_c": 1.0, " AU28_c": 0.0, " AU45_c": 0.0}}], "b64_img": "data:image/jpeg;base64,/9j/4AAQ....../2Q=="}'
*/

4

本地部署

一些项目后端是需要GPU进行推理的,部署站支持的并发量不高,如果在线跑不动的话,还可以获取源码部署在自己的机器上尝试。作者提供了项目部署的演示方法,我们进行了本地部署尝试。

首先找一台有docker,docker-compose,nvidia-docker-runtime的linux主机,克隆github项目。

通过百度网盘下载模型文件夹model(1.3G),放入根目录。ssl文件夹可以留空。

项目在本地是这个样子的:

修改docker-compose 文件中的command行,取消https功能:

代码语言:javascript
复制
version: "3.3"

services:
  demo_cpu_server:
    restart: always
    stdin_open: true
    tty: true
    build: ./docker
    image: zzz9958123/demo_server:latest
    ports:
      - 5000:5000
    working_dir: $PWD
    volumes:
      - $PWD:$PWD
      - /var/run/docker.sock:/var/run/docker.sock
      - /usr/bin/docker:/usr/bin/docker
    command: 'gunicorn --workers=1 demo_cpu_server:app  -b 0.0.0.0:5000  -t 600'
  demo_gpu_server:
    restart: always
    stdin_open: true
    tty: true
    build: ./docker
    image: zzz9958123/demo_server:latest
    ports:
      - 5001:5001
    working_dir: $PWD
    volumes:
      - $PWD:$PWD
      - /var/run/docker.sock:/var/run/docker.sock
      - /usr/bin/docker:/usr/bin/docker
    command: 'gunicorn --workers=1 demo_gpu_server:app  -b 0.0.0.0:5001  -t 600'
代码语言:javascript
复制
docker-compose up

运行docker-compose,部署完成.访问http://0.0.0.0:5000.

demo传送门:

https://pc.zzz9958123.com:5000

代码传送门:

https://github.com/zhangqijun/deeplearningdemo

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原始发表:2020-01-08,如有侵权请联系 cloudcommunity@tencent.com 删除

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