【AI实战】BERT 文本分类模型自动化部署之 dockerfile

这篇具有很好参考价值的文章主要介绍了【AI实战】BERT 文本分类模型自动化部署之 dockerfile。希望对大家有所帮助。如果存在错误或未考虑完全的地方,请大家不吝赐教,您也可以点击"举报违法"按钮提交疑问。

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:文章来源地址https://www.toymoban.com/news/detail-652421.html

{"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

到了这里,关于【AI实战】BERT 文本分类模型自动化部署之 dockerfile的文章就介绍完了。如果您还想了解更多内容,请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章,希望大家以后多多支持TOY模板网!

本文来自互联网用户投稿,该文观点仅代表作者本人,不代表本站立场。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如若转载,请注明出处: 如若内容造成侵权/违法违规/事实不符,请点击违法举报进行投诉反馈,一经查实,立即删除!

领支付宝红包 赞助服务器费用

相关文章

  • 深度学习实战48-【未来的专家团队】基于AutoCompany模型的自动化企业概念设计与设想

    大家好,我是微学AI,今天给大家介绍一下深度学习实战48-【未来的专家团队】基于AutoCompany模型的自动化企业概念设计与设想,文本将介绍AutoCompany模型的概念设计,涵盖了AI智能公司的各个角色,并结合了GPT-4接口来实现各个角色的功能,设置中央控制器,公司运作过程会生

    2024年02月12日
    浏览(42)
  • 大模型开发(十六):从0到1构建一个高度自动化的AI项目开发流程(中)

    授权声明: 本文基于九天Hector的原创课程资料创作,已获得其正式授权。 原课程出处:九天Hector的B站主页,感谢九天Hector为学习者带来的宝贵知识。 请尊重原创,转载或引用时,请标明来源。 全文共1w余字,预计阅读时间约40~60分钟 | 满满干货(附代码),建议收藏! 本文目

    2024年02月14日
    浏览(35)
  • OpenAI开发系列(十五):AI敏捷开发的新范式:利用大模型优化和自动化应用开发流程(上)

    授权声明: 本文基于九天Hector的原创课程资料创作,已获得其正式授权。 原课程出处:九天Hector的B站主页,感谢九天Hector为学习者带来的宝贵知识。 请尊重原创,转载或引用时,请标明来源。 全文共5000余字,预计阅读时间约30~50分钟 | 满满干货(附代码),建议收藏! 本文

    2024年02月03日
    浏览(50)
  • AI大模型在效率工具、内容平台、商业流程自动化、机器人、操作系统、智能设备等场景的应用

    随着人工智能技术的快速发展,越来越多的公司开始利用AI大模型来解决各种问题和挑战。AI大模型是指参数量巨大、拥有训练好的模型的人工智能系统,通常需要大量的数据和计算资源进行训练。本文将介绍AI大模型在效率工具、内容平台、商业流程自动化、机器人、操作系

    2024年02月07日
    浏览(42)
  • 探索云原生容器编排技术:如Kubernetes如何为大数据处理和AI模型的自动化部署带来便利

    🎈个人主页:程序员 小侯 🎐CSDN新晋作者 🎉欢迎 👍点赞✍评论⭐收藏 ✨收录专栏:云原生 ✨文章内容: 🤝希望作者的文章能对你有所帮助,有不足的地方请在评论区留言指正,大家一起学习交流!🤗 Kubernetes是一个强大的容器编排和自动化部署工具,它为大数据处理和

    2024年02月10日
    浏览(59)
  • 【AI Programming 】使用AI大模型来自动化生成DSL代码、RPC-SQL代码和UI Component 用户界面

    在当今的时代,随着人工智能技术的不断发展,越来越多的开发者开始探索低代码开发的领域。低代码开发可以让开发者更加高效地开发软件,减少重复性劳动,提高开发效率。在本文中,我们将介绍如何结合低代码开发和ChatGPT技术实现一个高效的开发流程。

    2024年02月13日
    浏览(89)
  • 基于BERT-PGN模型的中文新闻文本自动摘要生成——文本摘要生成(论文研读)

    基于BERT-PGN模型的中文新闻文本自动摘要生成(2020.07.08) 针对文本自动摘要任务中生成式摘要模型对句子的上下文理解不够充分、生成内容重复的问题,基于BERT 和指针生成网络(PGN),提出了一种面向中文新闻文本的生成式摘要模型——BERT-指针生成网络(BERTPGN)。首先,

    2024年02月01日
    浏览(54)
  • Python办公自动化 – 自动化文本翻译和Oracle数据库操作

    以下是往期的文章目录,需要可以查看哦。 Python办公自动化 – Excel和Word的操作运用 Python办公自动化 – Python发送电子邮件和Outlook的集成 Python办公自动化 – 对PDF文档和PPT文档的处理 Python办公自动化 – 对Excel文档和数据库的操作运用、设置计划任务 Python办公自动化 – 对

    2024年01月17日
    浏览(74)
  • Slither自动化测试智能合约并进行分类存储

    Slither是一个用Python 3编写的Solidity静态分析框架。它运行一套漏洞检测器,打印有关智能合约细节的可视化信息,并提供一个API来轻松编写自定义分析。Slither使开发人员能够发现漏洞,增强代码理解能力,并快速原型定制分析。 并且Slither相比较其他工具而言,最好的一个地

    2024年02月12日
    浏览(44)
  • python自动化测试(四):ECShop后台:商品分类添加

    前置条件: 本地部署:ECShop的版本是3.0.0、Google版本是 Google Chrome 65.0.3325.162 (正式版本) (32 位)  Google驱动的selenium版本是3.11.0 目录 前置代码 一、登录(后台登录) 二、进入商品分类页面 三、子页面 四、返回父级/最外层页面 五、点击添加分类按钮 六、输入分类 6.1 输

    2024年02月08日
    浏览(46)

觉得文章有用就打赏一下文章作者

支付宝扫一扫打赏

博客赞助

微信扫一扫打赏

请作者喝杯咖啡吧~博客赞助

支付宝扫一扫领取红包,优惠每天领

二维码1

领取红包

二维码2

领红包