【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
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Collecting h5py==3.9.0
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Collecting jieba==0.42.1
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Collecting joblib==1.3.1
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Collecting matplotlib==3.7.2
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Collecting matplotlib-inline==0.1.6
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Collecting nltk==3.8.1
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Collecting numpy==1.23.0
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Collecting packaging==23.1
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Collecting pandas==2.0.3
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Collecting parso==0.8.3
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Collecting pexpect==4.8.0
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Collecting pickleshare==0.7.5
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Collecting Pillow==10.0.0
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Collecting platformdirs==3.10.0
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Collecting prompt-toolkit==3.0.39
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Collecting protobuf==4.23.4
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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
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Collecting pyzmq==25.1.0
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Collecting safetensors==0.3.1
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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
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Collecting blinker>=1.6.2
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Collecting click>=8.1.3
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Collecting contourpy>=1.0.1
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Collecting cycler>=0.10
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Collecting fonttools>=4.22.0
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Collecting itsdangerous>=2.1.2
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Collecting jedi>=0.16
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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
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Building wheels for collected packages: jieba
  Building wheel for jieba (setup.py): started
  Building wheel for jieba (setup.py): finished with status 'done'
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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
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Step 4/5 : RUN chmod -x /home/bert_model:v1/run.sh
 ---> Running in 57624b20b496
Removing intermediate container 57624b20b496
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Step 5/5 : CMD bash /home/bert_model:v1/run.sh
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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
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