【AI实战】BERT 文本分类模型自动化部署之 dockerfile
本文主要介绍：
- 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
- 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)
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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
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Collecting pyparsing==3.0.9
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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
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Collecting PyYAML==6.0
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Collecting pyzmq==25.1.0
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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
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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
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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)
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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
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Collecting MarkupSafe>=2.0
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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'
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
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
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
- https://www.runoob.com/docker/docker-dockerfile.html
- https://zhuanlan.zhihu.com/p/98855346
