Developer Guide

Algorithms

Yolov5

  1. Create industrial model

    Create industrial model based Yolov5

    Create industrial model based Yolov5

    Model extra information

    { "labels": [ "license-plate", "vehicle" ] }

  2. Quickstart - train

    Quickstart train - Yolov5

    Hyperparameters

    Hyperparameter Default value Comment
    data /opt/ml/input/data/cfg/data.yaml Data configuration file
    cfg yolov5s.yaml Model configuration file
    weight yolov5s Model weight file (if it is not existed yet and it is standard weight file, it will download automatically
    project /opt/ml/model/ Model project directory
    name tutorial Model name
    img 640 Image size
    batch 16 Batch size
    epochs 10 Number of epochs

    Input data configuration

    Channel name Mandatory Comment
    images Yes S3 URI of images which contains train, validation images
    labels Yes S3 URI of labels which contains train, validation labels
    cfg Yes S3 URI of cfg which contains data.yaml
    weights No S3 URI of weights which contains model file

    Sample data configuration file

    train: /opt/ml/input/data/images/train/
val: /opt/ml/input/data/images/valid/

# number of classes
    nc: 2

# class names
names: ['license-plate','vehicle']

  3. Quickstart - deploy

    Quickstart deploy - Yolov5

    Environment variables

    Environment variable Default value Comment
    model_name custom Indicate if it is a custom model or pre-trained model. In case it is a custom model, the best.pt will be loaded from the specific location. Otherwise, pre-trained model file will be loaded.
    size 415 Chunk image size to be passed to model

  4. Quickstart - inference

    Quickstart inference - Yolov5

    HTTP request

    Inference approach HTTP Body Comment
    Raw image data Bytes of image file ContentType: image/png, image/jpg, image/jpeg
    S3 image data { “bucket”: [s3-bucket], “image_uri”: [s3-key] } ContentType: application/json

    HTTP response

    • One row per object
    • Each row is class x_center y_center width height format.
    • Box coordinates must be normalized by the dimensions of the image (i.e. have values between 0 and 1)
    • Class numbers are zero-indexed (start from 0). For example: 
      1 0.511271 0.571540 0.936967 0.631636
0 0.887925 0.714687 0.072043 0.101056

GluonCV

  1. Create industrial model

    Create industrial model based GluonCV

    Create industrial model based GluonCV

    Create industrial model based GluonCV

    Model extra information

    Image search { "task": "search" }

    Image classification { "task": "classification", "classes": [ "tench", "goldfish", ... ] }

  2. Quickstart - train

    Quickstart train - GluonCV

    Hyperparameters

    Hyperparameter Default value Comment
    classes 10 Number of classes
    batch_size 8 Batch size
    epochs 20 Number of epochs
    learning_rate 0.001 Learning rate
    momentum 0.9 Momentum
    wd 0.0001 wd
    num_workers 8 Number of workers
    model_name ResNet50_v2 Pretrained model name

    Input data configuration

    Channel name Mandatory Comment
    train Yes S3 URI of train images
    val Yes S3 URI of val images
    test Yes S3 URI of test images

  3. Quickstart - deploy

    Image search

    Quickstart deploy - GluonCV

    Image classification

    Quickstart deploy - GluonCV

    Environment variables

    Environment variable Default value Comment
    task search Indicate if the task is search or classification
    classes 1000 Number of classes
    model_name ResNet50_v2 Pretrained model name

  4. Quickstart - inference

    Quickstart inference - GluonCV

    Quickstart inference - GluonCV

    HTTP request

    Inference approach HTTP Body Comment
    Raw image data Bytes of image file ContentType: image/png, image/jpg, image/jpeg
    S3 image data { “bucket”: [s3-bucket], “image_uri”: [s3-key] } ContentType: application/json

    HTTP response

    • If task is search, return 2048 dimension embedding vector.
    • If task is classification, return top-k matched class id array.

PaddleOCR

  1. Create industrial model

    Create industrial model based PaddleOCR

    Create industrial model based PaddleOCR

    Model extra information

    {}

  2. Quickstart - train

    Quickstart train - PaddleOCR

    Hyperparameters

    Hyperparameter Default value Comment
    classes 10 Number of classes
    batch_size 8 Batch size
    epochs 20 Number of epochs
    learning_rate 0.001 Learning rate
    momentum 0.9 Momentum
    wd 0.0001 wd
    num_workers 8 Number of workers
    model_name ResNet50_v2 Pretrained model name

    Input data configuration

    Channel name Mandatory Comment
    dataset Yes S3 URI of train dataset
    pretrained_models Yes S3 URI of pretrained models

  3. Quickstart - deploy

    Quickstart deploy - PaddleOCR

    Environment variables

    Environment variable Default value Comment
    task ocr Indicate if it is an OCR task or structure task
    device cpu CPU or GPU device
    det_model_dir None det model directory when it is an OCR task
    rec_model_dir None Rec model directory when it is an OCR task
    table_model_dir None table model directory when it is an OCR task
    rec_char_dict_path None rec custom dictionary path when it is an structure task
    table_char_dict_path None table custom dictionary path when it is an structure task
    lang ch language
    table True Indicate if table recognition is enabled when it is a structure task
    layout True Indicate if layout recognition is enabled when it is a structure task
    ocr True Indicate if ocr recognition is enabled when it is a structure task

  4. Quickstart - inference

    Quickstart inference - PaddleOCR

    HTTP request

    Inference approach HTTP Body Comment
    Raw image data Bytes of image file ContentType: image/png, image/jpg, image/jpeg
    S3 image data { “bucket”: [s3-bucket], “image_uri”: [s3-key] } ContentType: application/json

    HTTP response

    • One row per text box
    • Each row is in x_left_top, y_left_top, x_right_top, y_right_top, x_right_bottom, y_right_bottom, x_left_bottom, y_left_bottom format, for example:
      479,138,1871,138,1871,198,479,198,英开曼群岛商史泰博股份有限公司台灣分公司
972,212,1386,219,1385,283,971,275,電子發票證明聨
1021,304,1336,304,1336,353,1021,353,2022-01-06
57,371,496,371,496,417,57,417,發票号碼:WP79071184
1625,364,1771,364,1771,413,1625,413,格式:25
61,424,646,424,646,470,61,470,實方名：網资讯股份有限公司
54,477,429,477,429,523,54,523,統一编号:24549210
57,530,157,530,157,576,57,576,地址:
54,576,150,576,150,629,54,629,備:
2029,629,2293,629,2293,668,2029,668,第1真/共1真
921,678,1432,678,1432,724,921,724,視同正本,凡經改即無效
1318,731,1400,731,1400,774,1318,774,軍價
1579,731,1671,731,1671,774,1579,774,金额
486,735,568,735,568,777,486,777,品名
1064,735,1154,735,1154,774,1064,774,數量
1986,728,2075,728,2075,781,1986,781,備
1379,781,1493,781,1493,834,1379,834,11.43
57,788,864,788,864,834,57,834,雄狮SIMBALION细字奇翼筆600/黑/1.0mm
1196,788,1225,788,1225,837,1196,837,5
1650,781,1761,781,1761,834,1650,834,57.14
1196,834,1225,834,1225,897,1196,897,3
1357,837,1489,837,1489,887,1357,887,100.00
61,841,807,841,807,887,61,887,立强REGINA無耳三孔D型±/R8603D/黑
1629,834,1761,834,1761,887,1629,887,300.00
1196,887,1225,887,1225,950,1196,950,5
1379,887,1489,887,1489,940,1379,940,56.19
57,894,893,894,893,940,57,940,11孔透明萬能袋/A4/亮面/0.04mm/100张/包
1620,888,1758,879,1762,932,1624,941,280.95
1377,941,1489,931,1494,988,1382,997,15.24
57,947,836,947,836,993,57,993,SDI小三角迥针0731B/25.4mm/70支/盒
1196,947,1225,947,1225,989,1196,989,2
1650,940,1761,940,1761,993,1650,993,30.48
1379,996,1493,996,1493,1046,1379,1046,61.90
54,1000,989,1000,989,1046,54,1046,3M小管芯隐形膠带810/19mmx32.9M/纸盒-3/4时
1196,1000,1225,1000,1225,1042,1196,1042,1
1650,996,1761,996,1761,1046,1650,1046,61.90
57,1053,118,1053,118,1099,57,1099,/卷
1379,1102,1489,1102,1489,1152,1379,1152,57.14
54,1106,986,1106,986,1152,54,1152,3M大管芯OPP透明文具膠带502/18mmx36M/8卷
1196,1109,1225,1109,1225,1144,1196,1144,1
1650,1102,1761,1102,1761,1152,1650,1152,57.14
61,1159,118,1159,118,1201,61,1201,/束
1379,1201,1489,1201,1489,1254,1379,1254,85.72
53,1208,996,1201,997,1250,54,1258,3M大管芯OPP透明封箱膠带/3036-6PK/48mmx36
1629,1201,1761,1201,1761,1250,1629,1250,171.43
1200,1215,1221,1215,1221,1247,1200,1247,2
64,1265,236,1265,236,1303,64,1303,M/6卷/束
1379,1303,1493,1303,1493,1356,1379,1356,10.48
61,1311,857,1303,857,1353,61,1360,得力Deli彩色迥纹針/E39716/33mm/100支
1650,1303,1761,1303,1761,1353,1650,1353,10.48
51,2769,265,2777,263,2830,49,2822,銷售额合计
1678,2776,2278,2773,2279,2822,1679,2826,970營業人用统一發票専用章
125,2829,264,2829,264,2879,125,2879,營業税
450,2833,536,2833,536,2875,450,2875,鹰税
832,2833,957,2833,957,2872,832,2872,零税率
1218,2833,1307,2833,1307,2875,1218,2875,免税
1707,2829,2261,2829,2261,2875,1707,2875,48英曼群岛商史泰博股份
50,2879,136,2879,136,2932,50,2932,總計
1654,2879,2132,2879,2132,2925,1654,2925,1,018有限公司台灣分公司
361,2928,954,2936,953,3010,360,3002,壹仟零壹拾捌元整
1753,2928,2128,2925,2129,2974,1754,2978,統一编号:27946944
54,2939,393,2939,393,2999,54,2999,總計新台
1764,2985,1971,2985,1971,3024,1764,3024,负贵人：吴
1754,3027,2279,3031,2278,3080,1753,3077,地址:新北市新莊區思源路60
1752,3076,1912,3084,1909,3134,1749,3125,1号15楼

CPT

  1. Create industrial model

    Create industrial model based CPT

    Create industrial model based CPT

    Model extra information

    {}

  2. Quickstart - train

    Quickstart train - CPT

    Hyperparameters

    Hyperparameter Default value Comment
    model_name_or_path fnlp/cpt-large Pretrained model name
    num_train_epochs 10 Number of epochs
    per_device_train_batch_size 4 Batch size per device
    text_column text Label of text column
    summary_column summary Label of summary column
    output_dir /opt/ml/model Output directory
    train_file /opt/ml/input/data/dataset/train.json Path of train file
    validation_file /opt/ml/input/data/dataset/val.json Path of validation file
    test_file /opt/ml/input/data/dataset/test.json Path of test file
    val_max_target_length 80 Max target length
    path json Extension name

    Input data configuration

    Channel name Mandatory Comment
    dataset Yes S3 URI of train dataset

    Content format of train/validation/test file

    {
    “text”: <text>, 
    “summary”: <summary>
}

  3. Quickstart - deploy

    Quickstart deploy - CPT

    Environment variables

    Environment variable Default value Comment
    input_max_length 512 Maximum effective input length
    output_max_length 512 Maximum effective output length
    top_p 0.95 Top probability of output summary

  4. Quickstart - inference

    Quickstart inference - CPT

    HTTP request

    {
    “inputs”: “中国农业银行股份有限公司安远县支行与魏坤元、魏松兰等借款合同纠纷一审民事判决书 江西省安远县人民法院 民 事 判 决 书 （2017）赣0726民初928号原告中国农业银行股份有限公司安远县支行。 住所地：安远县欣山镇龙泉路12号。 法定代表人唐文中，系该行行长。 委托代理人严海，系该行工作人员。 代理权限：代为承认、放弃或者变更诉讼请求，进行和解、提起反诉或者上诉。 被告魏坤元，男，1957年9月9日生，汉族，江西省安远县人，住安远县。 被告魏松兰，男，1971年12月16日生，汉族，江西省安远县人，住安远县。 被告魏碧星，男，1982年1月20日生，汉族，江西省安远县人，住安远县。 原告中国农业银行股份有限公司安远县支行（以下简称农行安远支行）诉被告魏坤元、魏松兰、魏碧星借款合同纠纷一案，本院立案受理后，依法由审判员徐海峰适用简易程序，于2017年8月8日公开开庭进行了审理。 原告农行安远支行的委托代理人严海到庭参加了诉讼，被告魏坤元、魏松兰、魏碧星经本院传票传唤无正当理由未到庭参加诉讼。 本案现已审理终结。 原告农行安远支行诉称，被告魏坤元于2013年12月19日向原告申请农户小额最高额可循环贷款一笔，金额50000元，并由被告魏松兰、魏碧星提供保证担保，合同到期日为2016年12月18日，合同约定在最高额度和期限内，借款人随借随还，自助放款还款，单笔借款期限最长不超过一年。 合同期限内，借款人魏坤元于2015年12月23日通过原告自助电子渠道申请贷款一笔、金额50000元，到期日为2016年11月22日，至今仍结欠原告贷款本金50000元及827.95元（利息计算至2016年12月20日止）。 该笔贷款已逾期，为此，原告诉至法院，请求依法判令：1、被告魏坤元、魏松兰、魏碧星归还原告贷款本金50000元及利息827.95元（利息计算至2016年12月20日止），之后的利息按合同约定的罚息利率计算； 2、本案诉讼费用由被告承担。 被告魏松兰、魏碧星未作答辩。 被告魏坤元未到庭答辩，其在本院的《询问笔录》中辩称，对原告起诉没有异议，确实是和被告魏松兰、魏碧星组成联保小组，相互担保，被告魏碧星是被告魏坤元的儿子，被告魏松兰是被告魏坤元的堂弟。 三被告各向原告借款5万元，拖欠了本息至今。 经审理查明，2013年11月25日，被告魏坤元、魏松兰、魏碧星三人组成联保小组，相互承担连带保证责任向原告农行安远支行申请借款。 三被告组成联保小组后，被告魏坤元向原告借款50000元，并签订了《农户贷款借款合同》（以下简称《借款合同》），合同内容：“第一条借款金额/可循环借款额度（人民币大写）：伍万元。 用款方式：自助可循环方式。 自2013年12月19日起至2016年12月18日（额度有效期），借款人可在伍万元的可循环借款额度内向贷款人申请借款，单笔借款期限最长不超过壹年且到期日最迟不得超过额度有效期。 借款用途：生产经营。 第二条本合同项下，借款执行利率以借款发放当日中国人民银行同期同档次人民币贷款基准利率基础上浮30%确定。 1年期以内（含）的借款执行浮动利率。 1年期以上的借款执行浮动利率。 浮动利率指如遇中国人民银行人民币贷款基准利率调整，自基准利率调整之日起，按调整后相应期限档次的基准利率和本合同约定的借款利率浮动幅度确定新的借款执行利率，且不再另行通知借款人和担保人。 第五条保证方式为连带责任保证，保证期间为借款期限届满之日起二年。 第六条借款人未按约定期限归还借款本金，贷款人对逾期借款从逾期之日起在借款执行利率基础上上浮百分之伍拾计收罚息，直至本息清偿为止。 ……” ，三被告均在该《借款合同》上签名捺印。 合同期限内，被告魏坤元于2015年12月23日借到原告农行安远支行发放的借款本金50000元，借款凭证上载明借款金额为伍万元整，执行利率为5.655％，逾期利率为8.4825%，借款日期为2015年12月23日，到期日期为2016年11月22日。 被告魏坤元借款后，经原告多次催款，截至2017年6月20日，仍拖欠原告借款本金50000元及利息2993.36元。 上述事实，有原告的陈述，原告提交的《借款合同》、《中国农业银行借款凭证》、《联合保证担保承诺书》、《中国农业银行农户小额贷款业务申请表》、《中国农业银行农户小额贷款面谈记录》、《本息清单》等证据予以证实，上述证据经庭审审查，能相互印证，本院予以确认。 本院认为，被告魏坤元向原告农行安远支行借款，双方签订了借款合同，原告也依约向被告魏坤元发放了借款，由此形成的借款合同关系合法有效，受法律保护。 借款到期后，被告魏坤元应清偿借款本金并依约支付利息，但其至今未还清借款本金及相应利息，其行为显属违约。 由于被告魏坤元、魏松兰、魏碧星三人组成联保小组，三被告中任一人借款均由其他二被告提供连带责任保证，即三被告相互承担连带保证责任。 因此，被告魏松兰、魏碧星二人应与债务人被告魏坤元承担连带偿还责任，依约偿还原告借款本金及相应利息。 综上，依照《中华人民共和国合同法》第一百零七条、第二百零四条、第二百零五条、第二百零七条，《中华人民共和国担保法》第十二条、第十八条、第二十一条，《中华人民共和国民事诉讼法》第一百四十四条之规定，判决如下：一、被告魏坤元于本判决生效后三十日内偿还原告中国农业银行股份有限公司安远县支行借款本金50000元及利息（截至2017年6月20日的利息为2993.36元，2017年6月20日之后的利息按照合同约定的罚息利率计算）。 二、被告魏松兰、魏碧星对上述款项承担连带偿还责任。 如果未按本判决指定的期间履行给付金钱义务，应当依照《中华人民共和国民事诉讼法》第二百五十三条之规定，加倍支付迟延履行期间的债务利息。 案件受理费1070元，减半收取535元，由被告魏坤元、魏松兰、魏碧星共同负担。 如不服本判决，可在判决书送达之日起十五日内，向本院递交上诉状，并按对方当事人的人数提出副本（在递交上诉状之日起七日内预交上诉费，缴交上诉费账号：99×××88，开户行：招商银行赣州长征大道支行，户名：江西省赣州市中级人民法院，备注栏注明上诉费），上诉于江西省赣州市中级人民法院。 （法律文书生效后，一方拒绝履行的，对方当事人向本院申请执行的期限是从判决书规定的履行期限届满二年内）审判员　　徐海峰 二〇一七年九月十一日 书记员　　李魁鹏”
}

    HTTP response

    {
    “result”: “[SEP] [CLS] 原 被 告 系 借 款 合 同 纠 纷 。 原 告 提 出 诉 讼 请 求 ： 被 告 偿 还 原 告 贷 款 及 利 息 、 罚 息 ； 保 证 人 承 担 连 带 清 偿 责 任 。 被 告 未 答 辩 。 经 审 查 ， 原 告 与 被 告 签 订 的 农 户 贷 款 借 款 及 担 保 合 同 合 法 有 效 ， 被 告 应 当 按 照 合 同 约 定 履 行 偿 还 借 款 义 务 ， 否 则 原 告 对 被 告 抵 押 物 享 有 优 先 受 偿 权 。 综 上 ， 依 照 《 中 华 人 民 共 和 国 合 同 法 》 第 六 十 条 第 一 款 、 第 一 百 九 十 六 条 、 第 二 百 零 五 条 、 二 百 一 十 一 条 、 《 担 保 法 》 及 《 最 高 人 民 法 院 关 于 适 用 若 干 问 题 的 解 释 （ 二 ） 》 第 二 十 四 条 及 《 民 事 诉 讼 法 》 之 规 定 ， 判 决 被 告 给 付 原 告 农 户 最 高 额 可 循 环 贷 款 逾 期 利 息 及 罚 息 。 [SEP]”
}

GABSA

  1. Create industrial model

    Create industrial model based GABSA

    Create industrial model based GABSA

    Model extra information

    {}

  2. Quickstart - train

    Quickstart train - GABSA

    Hyperparameters

    Hyperparameter Default value Comment
    task uabsa The name of the task, selected from: [uabsa, aste, tasd, aope]
    dataset rest14 The name of the dataset, selected from: [laptop14, rest14, rest15, rest16]
    model_name_or_path t5-base Pretrained model name
    paradigm annotation The way to construct target sentence, selected from: [annotation, extraction]
    do_train True Whether to run training.
    do_eval False Whether to run eval on the dev/test set.
    do_batch_predict False Whether to run batch prediction.
    do_direct_eval False Whether to run direct eval on the dev/test set.
    do_direct_predict False Whether to run direct eval on the dev/test set.
    max_seq_length 128 Maximum sequence length
    train_batch_size 16 Batch train size
    eval_batch_size 16 Batch eval size
    gradient_accumulation_steps 1 Number of updates steps to accumulate before performing a backward/update pass.
    learning_rate 3e-4 Learning rate
    num_train_epochs 20 Number of train epochs
    seed 42 seed
    ckpoint_path /opt/ml/model/cktepoch=1.ckpt Checkpoint path
    weight_decay 0.0 Weight decay
    adam_epsilon 1e-8 Adam epsilon
    warmup_steps 0.0 Warmup steps

    Input data configuration

    Channel name Mandatory Comment
    dataset Yes S3 URI of train dataset

    Content format of train/validation/test file

    -   Each row contains origin string####[(word, aspect, sentiment),…], for example:

    The wine list is interesting and has many good values .####[('wine list', 'drinks style_options', 'positive'), ('wine list', 'drinks prices', 'positive')]

  3. Quickstart - deploy

    Quickstart deploy - GABSA

    Environment variables

    Environment variable Default value Comment
    input_max_length 512 Maximum effective input length
    output_max_length 512 Maximum effective output length
    top_p 0.95 Top probability of output summary

  4. Quickstart - inference

    Quickstart inference - GABSA

    HTTP request

    {
    "inputs": "The wine list is wonderful and the food reminds me of my recent trip to Italy ."
}

    HTTP response

    {
    "result": "(wine list, drinks style_options, positive); (food, food quality, positive)"
}

PaddleNLP

  1. Create industrial model

    Create industrial model based PaddleNLP

    Create industrial model based PaddleNLP

    Model extra information

    {}

  2. Quickstart - train

    Quickstart train - PaddleNLP

    Hyperparameters

    Hyperparameter Default value Comment
    batch_size 16 Batch size
    learning_rate 1e-5 Learning rate
    train_path /opt/ml/input/data/dataset/train.txt Path of train file
    dev_path /opt/ml/input/data/dataset/dev.txt Path of dev file
    max_seq_len 512 Maximum sequence length
    num_epochs 100 Number of epochs
    seed 1000 seed
    logging_steps 10 Number of logging steps
    valid_steps 100 Number of validation steps
    device gpu CPU or GPU
    model uie-base Pretrained model name

    Input data configuration

    Channel name Mandatory Comment
    dataset Yes S3 URI of train dataset

    Content format of train/validation/test file

    -   Each row contains json string with content, result_list, and prompt, for example:

        {
            "content": "5月9日交通费29元从北苑到望京搜后", 
            "result_list": [{"text": "5月9日", "start": 0, "end": 4}], 
            "prompt": "时间"
        }

  3. Quickstart - deploy

    Quickstart deploy - PaddleNLP

    Environment variables

    Environment variable Default value Comment
    device cpu CPU or GPU
    schema Schema to inference

  4. Quickstart - inference

    Quickstart inference - PaddleNLP

    HTTP request

    {
    "inputs":"上海虹桥高铁到杭州时间是9月24日费用是73元"
}

    HTTP response

    {
    "result": 
        [
            {
                "出发地": 
                    [
                        {
                            "text": "上海",
                            "start": 0, 
                            "end": 2, 
                            "probability": "0.99601215"
                        }
                    ], 
                "目的地": 
                    [
                        {
                            "text": "杭州", 
                            "start": 7, 
                            "end": 9, 
                            "probability": "0.99965054"
                        }
                    ], 
                "费用": 
                    [
                        {
                            "text": "73元", 
                            "start": 20, 
                            "end": 23, 
                            "probability": "0.79425305"
                        }
                    ], 
                "时间": 
                    [
                        {
                            "text": "9月24日", 
                            "start": 12, 
                            "end": 17, 
                            "probability": "0.9998573"
                        }
                    ]
            }
        ]
}

DeBERTa

  1. Create industrial model

    Create industrial model based DeBERTa

    Create industrial model based DeBERTa

    Model extra information

    {}

  2. Quickstart - deploy

    Quickstart deploy - DeBERTa

  3. Quickstart - inference

    Quickstart inference - DeBERTa

    HTTP request

    {
    "inputs": "如何有效学习？",
    "parameters":
        {
            "candidate_labels":
                [
                    "民生",
                    "文化",
                    "娱乐",
                    "体育",
                    "财经",
                    "房产",
                    "汽车",
                    "教育",
                    "科技",
                    "军事",
                    "旅游",
                    "国际",
                    "证券",
                    "农业",
                    "电竞"
                ]
        }
}

    HTTP response

    {
    "sequence": "如何有效学习？",
    "labels":
        [
            "教育",
            "文化",
            "科技",
            "民生",
            "国际",
            "汽车",
            "军事",
            "电竞",
            "证券",
            "财经",
            "农业",
            "体育",
            "房产",
            "娱乐",
            "旅游"
        ],
    "scores":
        [
            0.7116793990135193,
            0.03544081375002861,
            0.0295342355966568,
            0.028479689732193947,
            0.024489011615514755,
            0.023759257048368454,
            0.023078029975295067,
            0.017534229904413223,
            0.01741044595837593,
            0.017365973442792892,
            0.01681639440357685,
            0.01475503295660019,
            0.013634421862661839,
            0.013062535785138607,
            0.012960496358573437
        ]
}

KeyBert

  1. Create industrial model

    Create industrial model based KeyBert

    Create industrial model based KeyBert

    Model extra information

    {}

  2. Quickstart - deploy

    Quickstart deploy - KeyBert

    Environment variables

    Environment variable Default value Comment
    type default One of [sentence-transformer, huggingface-transformer, flair, spacy, universal-sentence-encoder, gensim, default]
    model - paraphrase-multilingual-MiniLM-L12-v2 when type is sentence-transformer
    - en_core_web_trf when type is Spacy
    - https://tfhub.dev/google/universal-sentence-encoder/4 when type is universal-sentence-encoder
    - fasttext-wiki-news-subwords-300 when type is gensim    		 Model name
    huggingface_pipeline feature-extraction when type is huggingface-transformer HuggingFace pipeline
    mode - doc-embedding when type is flair
    - transformer when type is spacy    		 - Use document embeddeding model or word embedding model when type is flair
    - Use non-transformer, transformer, transformer without GPU when type is spacy
    doc_embedding roberta-base when type is flair Document embedding
    word_embedding crawl when type is flair Word embedding
    keyphrase_ngram_start 1 Minimum length in words of generated keywords/keyphrases
    keyphrase_ngram_end 1 Maximum length in words of generated keywords/keyphrases
    stop_words english Stopwords to remove from the document
    top_n 5 Return the top n keywords/keyphrases
    min_df 1 Minimum document frequency of a word across all documents
    use_maxsum False Whether to use Max Sum Distance for the selection of keywords/keyphrases
    use_mmr False Whether to use Maximal Marginal Relevance (MMR) for the selection of keywords/keyphrases
    diversity 0.5 The diversity of the results between 0 and 1 if use_mmr is set to True
    nr_candidates 20 The number of candidates to consider if use_maxsum is set to True
    highlight False Whether to print the document and highlight its keywords/keyphrases

  3. Quickstart - inference

    Quickstart inference - KeyBert

    Quickstart inference - KeyBert

    HTTP request

    {
    "inputs": "近年来,嵌入式技术与无线网络技术深度结合,催生了可计算RFID、嵌入式传感网等新兴领域.这些系统由大量廉价的节点组成,应用前景广泛.在传统设计中,这些系统通常是根据应用定制的.根据应用定制的系统具有开发简便、运行高效等优点,但不适合未来大规模部署.这是因为如果这些系统跟应用密切绑定、难以更新,那么系统一经部署就难以更新其软件,从而阻碍了软件创新的进程.软件定义的思想可以有效解决该问题.当前,软件定义网络成为计算机网络中一个热门的研究领域.传感器网络的软件设计与因特网的软件设计存在诸多差异,其最大的差异在于,传感器网络主要以信息的采集为核心,而因特网主要以信息的传输为核心.此外,传感器节点还具有体积小、电池续航能力有限、价格低廉等特点.文中主要调研了设计软件定义传感器网络(Software-DefinedSensorNetworks,SDSNs)架构的相关工作,列举了在设计一个通用、高效的软件定义传感器网络架构时可能遇到的挑战,并回顾了一些有用的技术.这些技术有的来自于现有方案,有的能够直接被用来解决一部分挑战.此外,文中还从软件定义功能的角度,进一步地对目前通用、高效的软件定义传感器网络架构及其采用的技术进行了分类.我们认为,软件定义传感器网络架构将在已部署的网络中起到至关重要的作用,并带来一场新的技术变革."
}

    HTTP response

    {
    "result": 
        [
            [
                "无线网络", 
                0.6223
            ], 
            [
                "计算机网络", 
                0.447
            ], 
            [
                "definedsensornetworks", 
                0.4356
            ], 
            [
                "技术", 
                0.4297
            ], 
            [
                "因特网", 
                0.4001
            ]
        ]
}

GluonTS

  1. Create industrial model

    Create industrial model based GluonTS

    Create industrial model based GluonTS

    Model extra information

    {}

  2. Quickstart - train

    Quickstart train - GluonTS

    Hyperparameters

    Hyperparameter Default value Comment
    algo-name DeepAR Algorithm name
    model-dir 8 /opt/ml/model
    output-dir 20 /opt/ml/output
    train 0.001 /opt/ml/input/data/dataset
    test 0.9 /opt/ml/input/data/dataset
    freq 1D frequence
    prediction-length 2*14 Prediction length
    context-length 20*14 Context length
    batch-size 2048 Batch size
    epochs 100 Number of epochs
    num-batches-per-epoc 2 Number of batches per epochs
    learning-rate 0.001 Learning rate
    learning-rate-decay-factor 0.5 Learning rate decay factor
    patience 10 Patience
    minimum-learning-rate 5e-5 Minimum learning rate
    clip-gradient 10 Clip gradient
    weight-decay 1e-8 Weight decay
    init xavier
    hybridize False
    use-feat-dynamic-real False
    use-feat-static-cat False
    use-past-feat-dynamic-real False
    cardinality
    use-log1p False

    Input data configuration

    Channel name Mandatory Comment
    dataset Yes S3 URI of train data

  3. Quickstart - deploy

    Quickstart deploy - GluonTS

    Environment variables

    Environment variable Default value Comment
    freq 1H
    target_quantile 0.5
    use_log1p False

  4. Quickstart - inference

    Quickstart inference - GluonTS

    Quickstart inference - GluonTS

    Quickstart inference - GluonTS

    HTTP request

    {
    "inputs":
        [
            {
                "target":
                    [
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
                    ],
                "start": "1980-01-01 00:00:00",
                "item_id": "T234",
                "freq": "1M",
                "prediction_length": 24
            },
            {
                "target":
                    [
                        12,28,12,0,56,12,8,12,28,4,24,8,12,4,4,8,16,4,24,12,16,16,4,24,12,12,16,24,8,16,12,40,12,32,24,8,8,8,20,20,32,20,16,16,8,24,32,12,16,4,4,28,12,24,20,12,44,32,36,20,24,28,40,84,52,20,20,64,36,44,28,24,32,12,44,72,50,15,35,80,75,24,60,42,12,18,60,54,72,49,72,88,24,88,90,33,22,9,54,88,117,136,99,80,50,110,88,44,44,36,63,72,108,63,60,55,66,33,120,55,12,100,80,117,72,135,55,77,120,66,121,132,72,0,60,77,50,136,40,88,99,77,275,168,26,80,110,130,140,130,187,110,204,220,144,204,60,36,156,220,154,279,297,374,132,192,182,266,140,156,247,228,360,270,209,104,247,364,420,255,240,225,300,338,352,429,273,336,270,390,570,544,765,208,336,405,434,364,416,256,368,493,408,666,1216,221,391,289,812,588,544,320,351,300,660,754,600,432,481,504,650,486,516,480,540,442,572,465,576,130,546,598,640,682,546,546,728,630,616,960,527,585,570,630,825,852,690,770,480,608,1072,1224,720,629,850,1037,988,767,935,900,1110,901,1560,2091,1008,833,1602,1785,1470,1365,1258,1152,1380,1332,2412,2320,1196,1350,1178,1136,117,363,846,595,1488,1691,2508,2992,725,1386,1701,1638,1548,1216,1360,1122,2180,1638,2814,2925,1363,902,1175,1320,1140,1292,1128,1134,1738,1564,3427,3325,1482,1584,1449,2222,1836,1332,1584,1680,1869,2002,3190,2652,1568,966,1380,1794,1350,1577,1104,1188,1495
                    ],
                "start":"1980-01-01 00:00:00",
                "item_id": "T234",
                "freq": "1M",
                "prediction_length": 24
            }
        ]
}

    HTTP response

    {
    "result": 
        [
            [
                1930.8326416015625,
                2290.176025390625,
                2243.770263671875,
                1112.35888671875,
                1216.8353271484375,
                1300.52587890625,
                1319.37451171875,
                1104.0013427734375,
                1231.51611328125,
                1281.2568359375,
                1306.0784912109375,
                1798.2593994140625,
                1773.2158203125,
                2148.71142578125,
                2055.391357421875,
                1097.5042724609375,
                1143.5330810546875,
                1365.8883056640625,
                1335.6077880859375,
                1208.5709228515625,
                1230.0604248046875,
                1293.724853515625,
                1298.18798828125,
                1691.08056640625
            ],
            [
                1820.448974609375,
                2697.013916015625,
                2449.663818359375,
                1375.6854248046875,
                1132.6513671875,
                1368.69091796875,
                1617.199951171875,
                1349.854248046875,
                1284.10986328125,
                1291.0433349609375,
                1288.84130859375,
                1595.6607666015625,
                1741.90185546875,
                2308.14013671875,
                2105.67529296875,
                1253.835205078125,
                1139.4034423828125,
                1357.335205078125,
                1589.59228515625,
                1372.781982421875,
                1298.3154296875,
                1244.7462158203125,
                1267.4632568359375,
                1574.6981201171875
            ]
        ]
}

StyleGAN

  1. Create industrial model

    Create industrial model based StyleGAN

    Create industrial model based StyleGAN

    Model extra information

    {}

  2. Quickstart - train

    Quickstart train - StyleGAN

    Hyperparameters

    Hyperparameter Default value Comment
    gpus 1 Num of GPUS
    snap 50 Number of GPUs to use [default: 1]
    metrics fid50k_full Comma-separated list or "none" [default: fid50k_full]
    seed 0 Random seed
    data Training data (directory or zip)
    cond false Train conditional model based on dataset labels [default: false]
    subset all 'Train with only N images
    mirror false Enable dataset x-flips
    config auto Base config, one of 'auto', 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar'
    gamma Override R1 gamma
    kimg Override training duration
    batch Override batch size
    aug ada Augmentation mode, one of 'noaug', 'ada', 'fixed'
    p Augmentation probability for --aug=fixed
    target ADA target value for --aug=ada
    augpipe bgc Augmentation pipeline
    resume noresume Resume training
    freezed 0 Freezed layers
    fp32 Disable mixed-precision training
    nhwc Use NHWC memory format with FP16
    nobench Disable cuDNN benchmarking
    allow-tf32 Allow PyTorch to use TF32 internally
    workers Override number of DataLoader workers

    Input data configuration

    Channel name Mandatory Comment
    dataset Yes S3 URI of train data

  3. Quickstart - deploy

    Quickstart deploy - StyleGAN

    Environment variables

    Environment variable Default value Comment
    network Pre-trianed network URL

  4. Quickstart - inference

    Quickstart inference - StyleGAN

    Quickstart inference - StyleGAN

    HTTP request

    {
    "inputs": {
        "trunc": "0.7325021066422363", 
        "seeds": "9,206,870,370"
    }
}

    HTTP response

    [
    "s3://sagemaker-ap-east-1-034068151705/stylegan/inference/outputseed0370.png",
    "s3://sagemaker-ap-east-1-034068151705/stylegan/inference/outputseed0009.png",
    "s3://sagemaker-ap-east-1-034068151705/stylegan/inference/outputseed0870.png",
    "s3://sagemaker-ap-east-1-034068151705/stylegan/inference/outputseed0206.png"
]

Yolov5PaddleOCR

  1. Create industrial model

    Create industrial model based Yolov5PaddleOCR

    Create industrial model based Yolov5PaddleOCR

    Model extra information

    {}

  2. Quickstart - inference

    Quickstart inference - Yolov5PaddleOCR

stable-diffusion-webui

  1. Create industrial model

    It will be created by default if you have started stable-diffusion-webui once. Alternative you can create it explicitly. Note industrial model of stable-diffusion-webui is unique within one all-in-one-ai app and with name 'stable-diffusion-webui' by design.

    Create industrial model based stable-diffusion-webui

  2. Quickstart - train

    Basically we support 3 train approach instable-diffusion-webui: embedding, hypernetwork, and dreambooth which can be used to train person, object, style.

    Strongly recommend that you start the training job inside of stable-diffusion-webui since it is already supported with more friendely user interface.

    Train inside of stable-diffusion-webui

    Train inside of stable-diffusion-webui

    Train inside of stable-diffusion-webui

    Alternative you start the training job explicitly.

    Train outside of stable-diffusion-webui

    Hyperparameters

    Hyperparameter Default value Comment
    region Current region name AWS region name
    embeddings-s3uri s3://[sagemaker-default-bucket]/stable-diffusion-webui/embeddings/ S3 URI of embeddings, only applicable for embedding or hypernetwork training
    hypernetwork-s3uri s3://[sagemaker-default-bucket]/stable-diffusion-webui/hypernetwork/ S3 URI of hypernetwork, only applicable for embedding or hypernetwork training
    train-task embedding One of embedding, hypernetwork, dreambooth
    api-endpoint REST API Gateway of all-in-one-ai REST API Gateway
    db-models-s3uri s3://[sagemaker-default-bucket]/stable-diffusion-webui/dreambooth/ S3 URI of dreambooth model S3 URI, only applicable for dreambooth training
    sd-models-s3uri s3://[sagemaker-default-bucket]/stable-diffusion-webui/models/ stable diffusion models S3 URI, only applicable for dreambooth training
    train-args train-args which is up to train-task
    dreambooth-config-id dreambooth config id which is used to identify the dreambooth config in s3://[sagemaker-default-bucket]/stable-diffusion-webui/dreambooth-config/

    train-args example for train dreambooth

    {\"train_dreambooth_settings\": {\"db_create_new_db_model\": true, \"db_new_model_name\": \"my-awsdogtoy-model-002\", \"db_new_model_src\": \"768-v-ema.ckpt\", \"db_new_model_scheduler\": \"ddim\", \"db_create_from_hub\": false, \"db_new_model_url\": \"\", \"db_new_model_token\": \"\", \"db_new_model_extract_ema\": false, \"db_model_name\": \"\", \"db_lora_model_name\": \"\", \"db_lora_weight\": 1, \"db_lora_txt_weight\": 1, \"db_train_imagic_only\": false, \"db_use_subdir\": false, \"db_custom_model_name\": \"\", \"db_train_wizard_person\": false, \"db_train_wizard_object\": true, \"db_performance_wizard\": true}}

    dreambooth-config example for train dreambooth

    """
model_name: str = "",
adam_beta1: float = 0.9,
adam_beta2: float = 0.999,
adam_epsilon: float = 1e-8,
adam_weight_decay: float = 0.01,
attention: str = "default",
center_crop: bool = True,
concepts_path: str = "",
custom_model_name: str = "",
epoch_pause_frequency: int = 0,
epoch_pause_time: int = 0,
gradient_accumulation_steps: int = 1,
gradient_checkpointing: bool = True,
half_model: bool = False,
has_ema: bool = False,
hflip: bool = False,
learning_rate: float = 0.00000172,
lora_learning_rate: float = 1e-4,
lora_txt_learning_rate: float = 5e-5,
lr_scheduler: str = 'constant',
lr_warmup_steps: int = 0,
max_token_length: int = 75,
max_train_steps: int = 1000,
mixed_precision: str = "fp16",
model_path: str = "",
not_cache_latents=False,
num_train_epochs: int = 1,
pad_tokens: bool = True,
pretrained_vae_name_or_path: str = "",
prior_loss_weight: float = 1.0,
resolution: int = 512,
revision: int = 0,
sample_batch_size: int = 1,
save_class_txt: bool = False,
save_embedding_every: int = 500,
save_preview_every: int = 500,
save_use_global_counts: bool = False,
save_use_epochs: bool = False,
scale_lr: bool = False,
scheduler: str = "ddim",
src: str = "",
shuffle_tags: bool = False,
train_batch_size: int = 1,
train_text_encoder: bool = True,
use_8bit_adam: bool = True,
use_concepts: bool = False,
use_cpu: bool = False,
use_ema: bool = True,
use_lora: bool = False,
v2: bool = False,
c1_class_data_dir: str = "",
c1_class_guidance_scale: float = 7.5,
c1_class_infer_steps: int = 60,
c1_class_negative_prompt: str = "",
c1_class_prompt: str = "",
c1_class_token: str = "",
c1_instance_data_dir: str = "",
c1_instance_prompt: str = "",
c1_instance_token: str = "",
c1_max_steps: int = -1,
c1_n_save_sample: int = 1,
c1_num_class_images: int = 0,
c1_sample_seed: int = -1,
c1_save_guidance_scale: float = 7.5,
c1_save_infer_steps: int = 60,
c1_save_sample_negative_prompt: str = "",
c1_save_sample_prompt: str = "",
c1_save_sample_template: str = "",
c2_class_data_dir: str = "",
c2_class_guidance_scale: float = 7.5,
c2_class_infer_steps: int = 60,
c2_class_negative_prompt: str = "",
c2_class_prompt: str = "",
c2_class_token: str = "",
c2_instance_data_dir: str = "",
c2_instance_prompt: str = "",
c2_instance_token: str = "",
c2_max_steps: int = -1,
c2_n_save_sample: int = 1,
c2_num_class_images: int = 0,
c2_sample_seed: int = -1,
c2_save_guidance_scale: float = 7.5,
c2_save_infer_steps: int = 60,
c2_save_sample_negative_prompt: str = "",
c2_save_sample_prompt: str = "",
c2_save_sample_template: str = "",
c3_class_data_dir: str = "",
c3_class_guidance_scale: float = 7.5,
c3_class_infer_steps: int = 60,
c3_class_negative_prompt: str = "",
c3_class_prompt: str = "",
c3_class_token: str = "",
c3_instance_data_dir: str = "",
c3_instance_prompt: str = "",
c3_instance_token: str = "",
c3_max_steps: int = -1,
c3_n_save_sample: int = 1,
c3_num_class_images: int = 0,
c3_sample_seed: int = -1,
c3_save_guidance_scale: float = 7.5,
c3_save_infer_steps: int = 60,
c3_save_sample_negative_prompt: str = "",
c3_save_sample_prompt: str = "",
c3_save_sample_template: str = "",
concepts_list=None
"""

[
    "",
    0.9,
    0.999,
    1e-08,
    0.01,
    "default",
    False,
    "",
    "",
    0.0,
    60.0,
    1,
    True,
    False,
    "",
    True,
    2e-06,
    0.0002,
    0.0002,
    "constant",
    500,
    75,
    0,
    "no",
    "",
    True,
    100,
    True,
    "",
    1,
    512,
    "",
    1,
    True,
    500,
    500,
    True,
    False,
    False,
    "",
    "",
    False,
    1,
    True,
    False,
    False,
    False,
    False,
    False,
    "",
    "",
    7.5,
    40,
    "",
    "",
    "photo of dog",
    "/opt/ml/input/data/concepts/images",
    "",
    "photo of awsdogtoy dog",
    -1,
    1,
    0,
    -1,
    7.5,
    40,
    "",
    "",
    "",
    "",
    7.5,
    40,
    "",
    "",
    "",
    "",
    "",
    "",
    -1,
    1,
    0,
    -1,
    7.5,
    40,
    "",
    "",
    "",
    "",
    7.5,
    40,
    "",
    "",
    "",
    "",
    "",
    "",
    -1,
    1,
    0,
    -1,
    7.5,
    40,
    "",
    "",
    ""
]

    Input data configuration

    Channel name Mandatory Comment
    images Yes S3 URI of images
    models No S3 URI of stable diffusion models
    embedding No S3 URI of embeddings
    hypernetwork No S3 URI of hypernetwork
    lora No S3 URI of lora models
    dreambooth No S3 URI of dreambooth models

  3. Quickstart - deploy

    Quickstart deploy - stable-diffusion-webui

    Environment variables

    Environment variable Default value Comment
    api_endpoint REST API Gateway of all-in-one-ai REST API Gateway
    endpoint_name Name of SageMaker Endpoint which is be used to host stable diffusion models and generate images

    Deploy stable-diffusion-webui

  4. Quickstart - Inference - Text to Image

    HTTP request

    payload = {
    'enable_hr': False, 
    'denoising_strength': 0.7, 
    'firstphase_width': 0, 
    'firstphase_height': 0, 
    'prompt': "dog", 
    'styles': ['None', 'None'], 
    'seed': -1, 
    'subseed': -1, 
    'subseed_strength': 0.0, 
    'seed_resize_from_h': 0, 
    'seed_resize_from_w': 0, 
    'sampler_name': None, 
    'batch_size': 1, 
    'n_iter':1, 
    'steps': 20, 
    'cfg_scale': 7.0, 
    'width': 768, 
    'height': 768, 
    'restore_faces': False, 
    'tiling': False, 
    'negative_prompt': '', 
    'eta': 1.0, 
    's_churn': 0.0, 
    's_tmax': None, 
    's_tmin': 0.0, 
    's_noise': 1.0, 
    'override_settings': {}, 
    'script_args': '[0, false, false, false, "", 1, "", 0, "", true, false, false]', 
    'sampler_index': 'Euler a'
}

inputs = {
    'task': 'text-to-image',
    'txt2img_payload': payload,
    'username': 'e'
}

    HTTP response

    {
    "images" : [
        [base64 encoded images],
        ...,
        [base64 encoded images]
    ]
}

    Inference stable-diffusion-webui text-to-image

  5. Quickstart - Inference - Image to Image

    HTTP request

    payload = {
    'init_images': [image_encoded_in_base64],
    'resize_mode': 0, 
    'denoising_strength': 0.75, 
    'mask': None, 
    'mask_blur': 4, 
    'inpainting_fill': 1, 
    'inpaint_full_res': False, 
    'inpaint_full_res_padding': 32, 
    'inpainting_mask_invert': 0, 
    'prompt': 'cat', 
    'styles': ['None', 'None'], 
    'seed': -1, 
    'subseed': -1, 
    'subseed_strength': 0.0, 
    'seed_resize_from_h': 0, 
    'seed_resize_from_w': 0, 
    'sampler_name': None, 
    'batch_size': 1, 
    'n_iter': 1, 
    'steps': 20, 
    'cfg_scale': 7.0, 
    'width': 768, 
    'height': 768, 
    'restore_faces': False, 
    'tiling': False, 
    'negative_prompt': '', 
    'eta': 1.0, 
    's_churn': 0.0, 
    's_tmax': None, 
    's_tmin': 0.0, 
    's_noise': 1.0, 
    'override_settings': {}, 
    'script_args': '[0, "<ul>\\n<li><code>CFG Scale</code> should be 2 or lower.</li>\\n</ul>\\n", true, true, "", "", true, 50, true, 1, 0, false, 4, 1, "<p style=\\"margin-bottom:0.75em\\">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>", 128, 8, ["left", "right", "up", "down"], 1, 0.05, 128, 4, 0, ["left", "right", "up", "down"], false, false, false, "", "<p style=\\"margin-bottom:0.75em\\">Will upscale the image to twice the dimensions; use width and height sliders to set tile size</p>", 64, 0, 1, "", 0, "", true, false, false]', 
    'sampler_index': 'Euler a', 
    'include_init_images': False
}

inputs = {
    'task': 'image-to-image',
    'img2img_payload': payload,
    'username': 'e'
}

    HTTP response

    {
    "images" : [
        [base64 encoded images],
        ...,
        [base64 encoded images]
    ]
}

    Inference stable-diffusion-webui image-to-image