【AI实战】BERT 文本分类模型自动化部署之 dockerfile

szZack2023-08-21 0:00

【AI实战】BERT 文本分类模型自动化部署之 dockerfile

本文主要介绍：

  1. 基于BERT的文本分类模型，样本不均衡的多分类loss函数的写法
  2. dockerfile自动构建docker镜像，服务部署

BERT

BERT 的全称为 Bidirectional Encoder Representation from Transformers，是一个预训练的语言表征模型。它强调了不再像以往一样采用传统的单向语言模型或者把两个单向语言模型进行浅层拼接的方法进行预训练，而是采用新的masked language model（MLM），以致能生成深度的双向语言表征。

BERT 文本分类模型

基于中文预训练bert的文本分类模型

基本架构：bert_model + dropout + 全连接层

代码：

class BERTClass(torch.nn.Module):
    def __init__(self, num_class):
        super(BERTClass, self).__init__()
        self.bert_model = BertModel.from_pretrained('bert-base-chinese', return_dict=True)
        self.dropout = torch.nn.Dropout(0.3)
        self.linear = torch.nn.Linear(768, num_class)

    def forward(self, input_ids, attn_mask, token_type_ids):
        output = self.bert_model(
            input_ids, 
            attention_mask=attn_mask, 
            token_type_ids=token_type_ids
        )
        output_dropout = self.dropout(output.pooler_output)
        output = self.linear(output_dropout)
        return output

针对多分类模型的loss函数

样本不均衡时

代码：

class MultiClassFocalLossWithAlpha(nn.Module):
    def __init__(self, alpha, gamma=2, reduction='mean'):
        """
        :param alpha: alpha=[0.2, 0.3, 0.5] 权重系数列表，三分类中第0类权重0.2，第1类权重0.3，第2类权重0.5
        :param gamma: 困难样本挖掘的gamma
        :param reduction:
        """
        super(MultiClassFocalLossWithAlpha, self).__init__()
        self.alpha = alpha#torch.tensor(alpha)
        self.gamma = gamma
        self.reduction = reduction

    def forward(self, pred, target_src):
        
        target = torch.argmax(target_src, axis = 1)
        alpha = self.alpha[target]  
        log_softmax = torch.log_softmax(pred, dim=1)
        logpt = torch.gather(log_softmax, dim=1, index=target.view(-1, 1))
        logpt = logpt.view(-1)
        ce_loss = -logpt 
        pt = torch.exp(logpt) 
        f_loss = alpha * (1 - pt) ** self.gamma * ce_loss 
        if self.reduction == "mean":
            return torch.mean(f_loss)
        if self.reduction == "sum":
            return torch.sum(f_loss)
        return f_loss
    
def focal_loss(outputs, targets):
    
    import json
    with open('./output/loss_weight.json') as f:
        data = f.read()
    loss_weight = json.loads(data)

    class_weight = torch.tensor(loss_weight['class_weight'])
    class_weight = class_weight.to(torch.device(device))

    loss = MultiClassFocalLossWithAlpha(alpha=class_weight)
    return loss.forward(outputs, targets)

多标签分类时

代码：

# BCEWithLogitsLoss combines a Sigmoid layer and the BCELoss in one single class. 
# This version is more numerically stable than using a plain Sigmoid followed 
# by a BCELoss as, by combining the operations into one layer, 
# we take advantage of the log-sum-exp trick for numerical stability.
def loss_fn(outputs, targets):
    
    import json
    with open('./output/loss_weight.json') as f:
        data = f.read()
    loss_weight = json.loads(data)

    class_weight = torch.tensor(loss_weight['class_weight'])
    pos_weight = torch.tensor(loss_weight['pos_weight'])

    class_weight = class_weight.to(torch.device(device))
    pos_weight = pos_weight.to(torch.device(device))
    loss_fn2 = torch.nn.BCEWithLogitsLoss(weight=class_weight, pos_weight=pos_weight)
    
    outputs = outputs.float()
    targets = targets.float()
    loss = loss_fn2(outputs, targets)
    return loss

dockerfile

Dockerfile 是一个用来构建镜像的文本文件，文本内容包含了一条条构建镜像所需的指令和说明。

编写 dockerfile

以python3.9来构建 bert 模型运行环境的镜像，基于 torch 的 CPU 版本。

Dockerfile：

FROM ludotech/python3.9-poetry:latest

ADD bert_model.tar /home

RUN ["pip", "--no-cache-dir", "install", "-r", "/home/bert_model/requirements.txt", "-i", "https://pypi.tuna.tsinghua.edu.cn/simple"]

RUN chmod -x /home/bert_model/run.sh
CMD bash /home/bert_model/run.sh

其中：

  • bert_model.tar 为完整的代码、模型、数据等
  • requirements.txt 包含完整的依赖库
  • run.sh 中为启动模型的脚步，可以支持多进程启动

requirements.txt :

urllib3==1.26.16
charset-normalizer==3.2.0
Flask==2.3.2
gensim==4.3.1
h5py==3.9.0
huggingface-hub==0.16.4
importlib-metadata==6.8.0
importlib-resources==6.0.0
ipython==8.14.0
jieba==0.42.1
joblib==1.3.1
matplotlib==3.7.2
matplotlib-inline==0.1.6
nltk==3.8.1
numpy==1.23.0
packaging==23.1
pandas==2.0.3
parso==0.8.3
pexpect==4.8.0
pickleshare==0.7.5
Pillow==10.0.0
platformdirs==3.10.0
prompt-toolkit==3.0.39
protobuf==4.23.4
psutil==5.9.5
pyparsing==3.0.9
python-dateutil==2.8.2
pytorch-pretrained-bert==0.6.2
pytz==2023.3
PyYAML==6.0
pyzmq==25.1.0
safetensors==0.3.1
scikit-learn==1.3.0
scipy==1.11.1
sentencepiece==0.1.99
tensorboardX==2.6.2
threadpoolctl==3.2.0
tokenizers==0.12.1
torch==1.10.1
tqdm==4.65.0
transformers==4.30.2

run.sh（我启动了 2 个服务进程）：

cd /home/bert_model
python deploy.py &

cd /home/bert_model
python train_srv.py &

touch /home/a
tail -f /home/a

build镜像

  • 文件准备：

    $ ls
build.sh  Dockerfile  bert_model.tar

    其中：

    build.sh：

    docker build -t  bert_model:v1.

  • 执行build

    sh build.sh

    如果是正常，则会输出如下：

    $ sh build.sh  
Sending build context to Docker daemon  822.3MB
Step 1/5 : FROM ludotech/python3.9-poetry:latest
 ---> bbbc285de928
Step 2/5 : ADD bert_model:v1.tar /home
 ---> fb481b25dbfd
Step 3/5 : RUN ["pip", "--no-cache-dir", "install", "-r", "/home/bert_model:v1/requirements.txt", "-i", "https://pypi.tuna.tsinghua.edu.cn/simple"]
 ---> Running in 122bad5d6b64
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting charset-normalizer==3.2.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/f9/0d/514be8597d7a96243e5467a37d337b9399cec117a513fcf9328405d911c0/charset_normalizer-3.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (202 kB)
Collecting Flask==2.3.2
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/fa/1a/f191d32818e5cd985bdd3f47a6e4f525e2db1ce5e8150045ca0c31813686/Flask-2.3.2-py3-none-any.whl (96 kB)
Collecting gensim==4.3.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/79/93/bb490709bb24004d3d4c20005e19939ef1e1ee62ed7698e85c186745b01d/gensim-4.3.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (26.5 MB)
Collecting h5py==3.9.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/4f/79/8e6e05bc4954ebdb8b9c587f780a11f28790585798bd15a8e4870cfc02bc/h5py-3.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB)
Collecting huggingface-hub==0.16.4
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7f/c4/adcbe9a696c135578cabcbdd7331332daad4d49b7c43688bc2d36b3a47d2/huggingface_hub-0.16.4-py3-none-any.whl (268 kB)
Collecting importlib-metadata==6.8.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/cc/37/db7ba97e676af155f5fcb1a35466f446eadc9104e25b83366e8088c9c926/importlib_metadata-6.8.0-py3-none-any.whl (22 kB)
Collecting importlib-resources==6.0.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/29/d1/bed03eca30aa05aaf6e0873de091f9385c48705c4a607c2dfe3edbe543e8/importlib_resources-6.0.0-py3-none-any.whl (31 kB)
Collecting ipython==8.14.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/52/d1/f70cdafba20030cbc1412d7a7d6a89c5035071835cc50e47fc5ed8da553c/ipython-8.14.0-py3-none-any.whl (798 kB)
Collecting jieba==0.42.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c6/cb/18eeb235f833b726522d7ebed54f2278ce28ba9438e3135ab0278d9792a2/jieba-0.42.1.tar.gz (19.2 MB)
Collecting joblib==1.3.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/28/08/9dcdaa5aac4634e4c23af26d92121f7ce445c630efa0d3037881ae2407fb/joblib-1.3.1-py3-none-any.whl (301 kB)
Collecting matplotlib==3.7.2
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/47/b9/6c0daa9b953a80b4e6933bf6a11a2d0633f257e84ee5995c5fd35de564c9/matplotlib-3.7.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.6 MB)
Collecting matplotlib-inline==0.1.6
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/f2/51/c34d7a1d528efaae3d8ddb18ef45a41f284eacf9e514523b191b7d0872cc/matplotlib_inline-0.1.6-py3-none-any.whl (9.4 kB)
Collecting nltk==3.8.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a6/0a/0d20d2c0f16be91b9fa32a77b76c60f9baf6eba419e5ef5deca17af9c582/nltk-3.8.1-py3-none-any.whl (1.5 MB)
Collecting numpy==1.23.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/da/0e/496e529f440f528273f6847e14d7b132b0556a824fc2af36e8afd8e6a020/numpy-1.23.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB)
Collecting packaging==23.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ab/c3/57f0601a2d4fe15de7a553c00adbc901425661bf048f2a22dfc500caf121/packaging-23.1-py3-none-any.whl (48 kB)
Collecting pandas==2.0.3
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/9e/0d/91a9fd2c202f2b1d97a38ab591890f86480ecbb596cbc56d035f6f23fdcc/pandas-2.0.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.4 MB)
Collecting parso==0.8.3
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/05/63/8011bd08a4111858f79d2b09aad86638490d62fbf881c44e434a6dfca87b/parso-0.8.3-py2.py3-none-any.whl (100 kB)
Collecting pexpect==4.8.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/39/7b/88dbb785881c28a102619d46423cb853b46dbccc70d3ac362d99773a78ce/pexpect-4.8.0-py2.py3-none-any.whl (59 kB)
Collecting pickleshare==0.7.5
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/9a/41/220f49aaea88bc6fa6cba8d05ecf24676326156c23b991e80b3f2fc24c77/pickleshare-0.7.5-py2.py3-none-any.whl (6.9 kB)
Collecting Pillow==10.0.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/50/e5/0d484d1ac71b934638f91b7156203ba5bf3eb12f596b616a68a85c123808/Pillow-10.0.0-cp39-cp39-manylinux_2_28_x86_64.whl (3.4 MB)
Collecting platformdirs==3.10.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/14/51/fe5a0d6ea589f0d4a1b97824fb518962ad48b27cd346dcdfa2405187997a/platformdirs-3.10.0-py3-none-any.whl (17 kB)
Collecting prompt-toolkit==3.0.39
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a9/b4/ba77c84edf499877317225d7b7bc047a81f7c2eed9628eeb6bab0ac2e6c9/prompt_toolkit-3.0.39-py3-none-any.whl (385 kB)
Collecting protobuf==4.23.4
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/01/cb/445b3e465abdb8042a41957dc8f60c54620dc7540dbcf9b458a921531ca2/protobuf-4.23.4-cp37-abi3-manylinux2014_x86_64.whl (304 kB)
Collecting psutil==5.9.5
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/af/4d/389441079ecef400e2551a3933224885a7bde6b8a4810091d628cdd75afe/psutil-5.9.5-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (282 kB)
Collecting pyparsing==3.0.9
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6c/10/a7d0fa5baea8fe7b50f448ab742f26f52b80bfca85ac2be9d35cdd9a3246/pyparsing-3.0.9-py3-none-any.whl (98 kB)
Collecting python-dateutil==2.8.2
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/36/7a/87837f39d0296e723bb9b62bbb257d0355c7f6128853c78955f57342a56d/python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)
Collecting pytorch-pretrained-bert==0.6.2
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d7/e0/c08d5553b89973d9a240605b9c12404bcf8227590de62bae27acbcfe076b/pytorch_pretrained_bert-0.6.2-py3-none-any.whl (123 kB)
Collecting pytz==2023.3
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7f/99/ad6bd37e748257dd70d6f85d916cafe79c0b0f5e2e95b11f7fbc82bf3110/pytz-2023.3-py2.py3-none-any.whl (502 kB)
Collecting PyYAML==6.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/12/fc/a4d5a7554e0067677823f7265cb3ae22aed8a238560b5133b58cda252dad/PyYAML-6.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (661 kB)
Collecting pyzmq==25.1.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/94/4b/1093172b73984b568d9f1a72bcd61793822fab40aa571f5d6ed9db6234cb/pyzmq-25.1.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)
Collecting safetensors==0.3.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/60/c7/1911e04710666eb79ca3311a4e91b669419a1f23c2b2619005165104368c/safetensors-0.3.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)
Collecting scikit-learn==1.3.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d4/61/966d3238f6cbcbb13350d31bd0accfc5efdf9e349cd2a42d9761b8b67a18/scikit_learn-1.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.9 MB)
Collecting scipy==1.11.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/08/25/035fe07fc32c5a8b314f882faa9d4817223fa5faf524d3fedcf17a4b9d22/scipy-1.11.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.5 MB)
Collecting sentencepiece==0.1.99
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6b/22/4157918b2112d47014fb1e79b0dd6d5a141b8d1b049bae695d405150ebaf/sentencepiece-0.1.99-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)
Collecting tensorboardX==2.6.2
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/44/7b/eee50dcadcee4c674353ca207fdcd53a5b1f382021af1ed1797f9c0c45d2/tensorboardX-2.6.2-py2.py3-none-any.whl (101 kB)
Collecting threadpoolctl==3.2.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/81/12/fd4dea011af9d69e1cad05c75f3f7202cdcbeac9b712eea58ca779a72865/threadpoolctl-3.2.0-py3-none-any.whl (15 kB)
Collecting tokenizers==0.12.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/60/fc/3da9736965bf6edd96e8b098984c9f4559c4a1cc5be563436cd228ad1e69/tokenizers-0.12.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)
Collecting torch==1.10.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/2c/c8/dcef19018d2fe730ecacf47650d3d6e8d6fe545f02fbdbde0174e0279f02/torch-1.10.1-cp39-cp39-manylinux1_x86_64.whl (881.9 MB)
Collecting tqdm==4.65.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e6/02/a2cff6306177ae6bc73bc0665065de51dfb3b9db7373e122e2735faf0d97/tqdm-4.65.0-py3-none-any.whl (77 kB)
Collecting transformers==4.30.2
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5b/0b/e45d26ccd28568013523e04f325432ea88a442b4e3020b757cf4361f0120/transformers-4.30.2-py3-none-any.whl (7.2 MB)
Collecting urllib3==1.26.16
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c5/05/c214b32d21c0b465506f95c4f28ccbcba15022e000b043b72b3df7728471/urllib3-1.26.16-py2.py3-none-any.whl (143 kB)
Collecting blinker>=1.6.2
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/0d/f1/5f39e771cd730d347539bb74c6d496737b9d5f0a53bc9fdbf3e170f1ee48/blinker-1.6.2-py3-none-any.whl (13 kB)
Collecting click>=8.1.3
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/1a/70/e63223f8116931d365993d4a6b7ef653a4d920b41d03de7c59499962821f/click-8.1.6-py3-none-any.whl (97 kB)
Collecting contourpy>=1.0.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/38/6f/5382bdff9dda60cb17cef6dfa2bad3e6edacffd5c2243e282e851c63f721/contourpy-1.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (300 kB)
Collecting cycler>=0.10
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5c/f9/695d6bedebd747e5eb0fe8fad57b72fdf25411273a39791cde838d5a8f51/cycler-0.11.0-py3-none-any.whl (6.4 kB)
Collecting fonttools>=4.22.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/91/0e/8303b815e3bcc211a2da3e4427748cb963247594837dceb051e28d4e4b66/fonttools-4.42.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB)
Collecting itsdangerous>=2.1.2
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/68/5f/447e04e828f47465eeab35b5d408b7ebaaaee207f48b7136c5a7267a30ae/itsdangerous-2.1.2-py3-none-any.whl (15 kB)
Collecting jedi>=0.16
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8e/46/7e3ae3aa2dcfcffc5138c6cef5448523218658411c84a2000bf75c8d3ec1/jedi-0.19.0-py2.py3-none-any.whl (1.6 MB)
Collecting Jinja2>=3.1.2
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/bc/c3/f068337a370801f372f2f8f6bad74a5c140f6fda3d9de154052708dd3c65/Jinja2-3.1.2-py3-none-any.whl (133 kB)
Collecting kiwisolver>=1.0.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a4/36/c414d75be311ce97ef7248edcc4fc05afae2998641bf6b592d43a9dee581/kiwisolver-1.4.4-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)
Collecting MarkupSafe>=2.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/de/63/cb7e71984e9159ec5f45b5e81e896c8bdd0e45fe3fc6ce02ab497f0d790e/MarkupSafe-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)
Collecting ptyprocess>=0.5
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl (13 kB)
Collecting pygments>=2.4.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/43/88/29adf0b44ba6ac85045e63734ae0997d3c58d8b1a91c914d240828d0d73d/Pygments-2.16.1-py3-none-any.whl (1.2 MB)
Collecting regex>=2021.8.3
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c0/f4/278e305e02245937579a7952b8a3205116b4d2480a3c03fa11e599b773d6/regex-2023.8.8-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (771 kB)
Collecting six>=1.5
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting smart-open>=1.8.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/47/80/c2d1bdd36c6b64ae566d9a29724291510e4f3796ce99639d3c2999286284/smart_open-6.3.0-py3-none-any.whl (56 kB)
Collecting traitlets>=5
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/77/75/c28e9ef7abec2b7e9ff35aea3e0be6c1aceaf7873c26c95ae1f0d594de71/traitlets-5.9.0-py3-none-any.whl (117 kB)
Collecting typing-extensions>=3.7.4.3
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ec/6b/63cc3df74987c36fe26157ee12e09e8f9db4de771e0f3404263117e75b95/typing_extensions-4.7.1-py3-none-any.whl (33 kB)
Collecting tzdata>=2022.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d5/fb/a79efcab32b8a1f1ddca7f35109a50e4a80d42ac1c9187ab46522b2407d7/tzdata-2023.3-py2.py3-none-any.whl (341 kB)
Collecting Werkzeug>=2.3.3
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/9b/59/a7c32e3d8d0e546a206e0552a2c04444544f15c1da4a01df8938d20c6ffc/werkzeug-2.3.7-py3-none-any.whl (242 kB)
Collecting zipp>=0.5
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8c/08/d3006317aefe25ea79d3b76c9650afabaf6d63d1c8443b236e7405447503/zipp-3.16.2-py3-none-any.whl (7.2 kB)
Collecting backcall
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/4c/1c/ff6546b6c12603d8dd1070aa3c3d273ad4c07f5771689a7b69a550e8c951/backcall-0.2.0-py2.py3-none-any.whl (11 kB)
Collecting boto3
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ec/9a/c0837684f1ab666add90e639944575f7325301d59d19f72b6acf6c850b78/boto3-1.28.27-py3-none-any.whl (135 kB)
Collecting botocore<1.32.0,>=1.31.27
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e8/82/33a94da51ac24033fe83400fd08f5a54dfca5179761e0d3c8935bce538d9/botocore-1.31.27-py3-none-any.whl (11.1 MB)
Collecting jmespath<2.0.0,>=0.7.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/31/b4/b9b800c45527aadd64d5b442f9b932b00648617eb5d63d2c7a6587b7cafc/jmespath-1.0.1-py3-none-any.whl (20 kB)
Collecting s3transfer<0.7.0,>=0.6.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d9/17/a3b666f5ef9543cfd3c661d39d1e193abb9649d0cfbbfee3cf3b51d5af02/s3transfer-0.6.2-py3-none-any.whl (79 kB)
Collecting decorator
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d5/50/83c593b07763e1161326b3b8c6686f0f4b0f24d5526546bee538c89837d6/decorator-5.1.1-py3-none-any.whl (9.1 kB)
Collecting filelock
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/00/45/ec3407adf6f6b5bf867a4462b2b0af27597a26bd3cd6e2534cb6ab029938/filelock-3.12.2-py3-none-any.whl (10 kB)
Collecting fsspec
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e3/bd/4c0a4619494188a9db5d77e2100ab7d544a42e76b2447869d8e124e981d8/fsspec-2023.6.0-py3-none-any.whl (163 kB)
Collecting requests
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl (62 kB)
Collecting certifi>=2017.4.17
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/4c/dd/2234eab22353ffc7d94e8d13177aaa050113286e93e7b40eae01fbf7c3d9/certifi-2023.7.22-py3-none-any.whl (158 kB)
Collecting idna<4,>=2.5
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/fc/34/3030de6f1370931b9dbb4dad48f6ab1015ab1d32447850b9fc94e60097be/idna-3.4-py3-none-any.whl (61 kB)
Collecting stack-data
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6a/81/aa96c25c27f78cdc444fec27d80f4c05194c591465e491a1358d8a035bc1/stack_data-0.6.2-py3-none-any.whl (24 kB)
Collecting asttokens>=2.1.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/f3/e1/64679d9d0759db5b182222c81ff322c2fe2c31e156a59afd6e9208c960e5/asttokens-2.2.1-py2.py3-none-any.whl (26 kB)
Collecting executing>=1.2.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/28/3c/bc3819dd8b1a1588c9215a87271b6178cc5498acaa83885211f5d4d9e693/executing-1.2.0-py2.py3-none-any.whl (24 kB)
Collecting pure-eval
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/2b/27/77f9d5684e6bce929f5cfe18d6cfbe5133013c06cb2fbf5933670e60761d/pure_eval-0.2.2-py3-none-any.whl (11 kB)
Collecting wcwidth
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/20/f4/c0584a25144ce20bfcf1aecd041768b8c762c1eb0aa77502a3f0baa83f11/wcwidth-0.2.6-py2.py3-none-any.whl (29 kB)
Building wheels for collected packages: jieba
  Building wheel for jieba (setup.py): started
  Building wheel for jieba (setup.py): finished with status 'done'
  Created wheel for jieba: filename=jieba-0.42.1-py3-none-any.whl size=19314477 sha256=57e3819b1f7805dacd361648a3cb7f041a401078f47663d2f5d58f4523b2c1c7
  Stored in directory: /tmp/pip-ephem-wheel-cache-oey5ckcm/wheels/1a/76/68/b6d79c4db704bb18d54f6a73ab551185f4711f9730c0c15d97
Successfully built jieba
Installing collected packages: six, urllib3, python-dateutil, jmespath, idna, charset-normalizer, certifi, botocore, zipp, wcwidth, typing-extensions, traitlets, tqdm, s3transfer, requests, PyYAML, pure-eval, ptyprocess, parso, packaging, numpy, MarkupSafe, fsspec, filelock, executing, asttokens, Werkzeug, tzdata, torch, tokenizers, threadpoolctl, stack-data, smart-open, scipy, safetensors, regex, pytz, pyparsing, pygments, protobuf, prompt-toolkit, Pillow, pickleshare, pexpect, matplotlib-inline, kiwisolver, joblib, Jinja2, jedi, itsdangerous, importlib-resources, importlib-metadata, huggingface-hub, fonttools, decorator, cycler, contourpy, click, boto3, blinker, backcall, transformers, tensorboardX, sentencepiece, scikit-learn, pyzmq, pytorch-pretrained-bert, psutil, platformdirs, pandas, nltk, matplotlib, jieba, ipython, h5py, gensim, Flask
Successfully installed Flask-2.3.2 Jinja2-3.1.2 MarkupSafe-2.1.3 Pillow-10.0.0 PyYAML-6.0 Werkzeug-2.3.7 asttokens-2.2.1 backcall-0.2.0 blinker-1.6.2 boto3-1.28.27 botocore-1.31.27 certifi-2023.7.22 charset-normalizer-3.2.0 click-8.1.6 contourpy-1.1.0 cycler-0.11.0 decorator-5.1.1 executing-1.2.0 filelock-3.12.2 fonttools-4.42.0 fsspec-2023.6.0 gensim-4.3.1 h5py-3.9.0 huggingface-hub-0.16.4 idna-3.4 importlib-metadata-6.8.0 importlib-resources-6.0.0 ipython-8.14.0 itsdangerous-2.1.2 jedi-0.19.0 jieba-0.42.1 jmespath-1.0.1 joblib-1.3.1 kiwisolver-1.4.4 matplotlib-3.7.2 matplotlib-inline-0.1.6 nltk-3.8.1 numpy-1.23.0 packaging-23.1 pandas-2.0.3 parso-0.8.3 pexpect-4.8.0 pickleshare-0.7.5 platformdirs-3.10.0 prompt-toolkit-3.0.39 protobuf-4.23.4 psutil-5.9.5 ptyprocess-0.7.0 pure-eval-0.2.2 pygments-2.16.1 pyparsing-3.0.9 python-dateutil-2.8.2 pytorch-pretrained-bert-0.6.2 pytz-2023.3 pyzmq-25.1.0 regex-2023.8.8 requests-2.31.0 s3transfer-0.6.2 safetensors-0.3.1 scikit-learn-1.3.0 scipy-1.11.1 sentencepiece-0.1.99 six-1.16.0 smart-open-6.3.0 stack-data-0.6.2 tensorboardX-2.6.2 threadpoolctl-3.2.0 tokenizers-0.12.1 torch-1.10.1 tqdm-4.65.0 traitlets-5.9.0 transformers-4.30.2 typing-extensions-4.7.1 tzdata-2023.3 urllib3-1.26.16 wcwidth-0.2.6 zipp-3.16.2
WARNING: You are using pip version 20.3.3; however, version 23.2.1 is available.
You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.
Removing intermediate container 122bad5d6b64
 ---> 532901e8e45d
Step 4/5 : RUN chmod -x /home/bert_model:v1/run.sh
 ---> Running in 57624b20b496
Removing intermediate container 57624b20b496
 ---> 4b68e0e4fcd5
Step 5/5 : CMD bash /home/bert_model:v1/run.sh
 ---> Running in 1deeb0da3ee4
Removing intermediate container 1deeb0da3ee4
 ---> 1309aef51499
Successfully built 1309aef51499
Successfully tagged bert_model:v1
d22928152eb3cfc123ea321cc25a75980d88f7dfee90a762781892c71c54c13

运行docker

  • 创建 run_docker.sh

    docker run -it -d
    --name bert_model
    -v /bee/xx/:/home/xx/
    -e TZ='Asia/Shanghai'
    -p 12928:12345
    -p 12929:12346
    --shm-size 16G
    bert_model:v1

  • 启动服务

    sh run_docker.sh

测试服务

使用curl测试服务是否正常：

我的服务是post：

curl -H "Content-Type:application/json" -X POST -d '{"text": "xxxxxx"}' http://localhost:12928/xxx

测试响应时间：

time curl -H "Content-Type:application/json" -X POST -d '{"text": "xxxxxx"}' http://localhost:12928/xxx

返回：

{"code": 10000, "label": ["aaa"]}
real    0m0.157s
user    0m0.010s
sys     0m0.006s

参考

  1. https://www.runoob.com/docker/docker-dockerfile.html
  2. https://zhuanlan.zhihu.com/p/98855346
相关推荐
湫ccc14 小时前
Bert框架详解（上）
人工智能·深度学习·bert
快乐点吧17 小时前
BERT 模型在句子分类任务中的作用分析笔记
笔记·分类·bert
weixin_5436628617 小时前
BERT的中文问答系统28
人工智能·python·bert
湫ccc1 天前
Bert框架详解（下）
人工智能·深度学习·bert
Landy_Jay2 天前
跟李沐学AI：BERT
人工智能·自然语言处理·bert
0x2112 天前
[论文阅读]BERT-based Lexical Substitution
论文阅读·人工智能·bert
红米煮粥2 天前
BERT框架
人工智能·深度学习·bert
深度学习实战训练营2 天前
BERT的新闻标题生成
人工智能·深度学习·bert
raoxiaoya2 天前
本地部署bert-base-chinese模型交互式问答，gradio
人工智能·深度学习·bert
娃娃略3 天前
【模型学习之路】手写+分析bert
人工智能·学习·bert
热门推荐
01【HarmonyOS】HUAWEI DevEco Studio 下载地址汇总02(欧拉)openEuler系统添加网卡文件配置流程、(欧拉)openEuler系统手动配置ipv6地址流程、(欧拉)openEuler系统网络管理说明03组基轨迹建模 GBTM的介绍与实现（Stata 或 R）04【AIGC】重塑未来的科技巨轮05全面解析：构建基于深度学习的安全帽检测系统（UI界面+YOLO代码+数据集）06【经验分享】Ubuntu22.04安装微信(linux官方版)07基于YOLOv10深度学习的CT扫描图像肾结石智能检测系统【python源码+Pyqt5界面+数据集+训练代码】深度学习实战、目标检测08Ubuntu 20.04使用Livox mid 360 测试 FAST_LIO09RAG 实践- Ollama+RagFlow 部署本地知识库10红米手机使用google play