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 = gammaself.reduction = reductiondef 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_lossdef focal_loss(outputs, targets):import jsonwith 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 jsonwith 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:latestADD bert_model.tar /homeRUN ["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==3.2.0Downloading 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==2.3.2Downloading https://pypi.tuna.tsinghua.edu.cn/packages/fa/1a/f191d32818e5cd985bdd3f47a6e4f525e2db1ce5e8150045ca0c31813686/Flask-2.3.2-py3-none-any.whl (96 kB)
  Collecting gensim==4.3.1Downloading 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==3.9.0Downloading 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==0.16.4Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7f/c4/adcbe9a696c135578cabcbdd7331332daad4d49b7c43688bc2d36b3a47d2/huggingface_hub-0.16.4-py3-none-any.whl (268 kB)
  Collecting importlib-metadata==6.8.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/cc/37/db7ba97e676af155f5fcb1a35466f446eadc9104e25b83366e8088c9c926/importlib_metadata-6.8.0-py3-none-any.whl (22 kB)
  Collecting importlib-resources==6.0.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/29/d1/bed03eca30aa05aaf6e0873de091f9385c48705c4a607c2dfe3edbe543e8/importlib_resources-6.0.0-py3-none-any.whl (31 kB)
  Collecting ipython==8.14.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/52/d1/f70cdafba20030cbc1412d7a7d6a89c5035071835cc50e47fc5ed8da553c/ipython-8.14.0-py3-none-any.whl (798 kB)
  Collecting jieba==0.42.1Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c6/cb/18eeb235f833b726522d7ebed54f2278ce28ba9438e3135ab0278d9792a2/jieba-0.42.1.tar.gz (19.2 MB)
  Collecting joblib==1.3.1Downloading https://pypi.tuna.tsinghua.edu.cn/packages/28/08/9dcdaa5aac4634e4c23af26d92121f7ce445c630efa0d3037881ae2407fb/joblib-1.3.1-py3-none-any.whl (301 kB)
  Collecting matplotlib==3.7.2Downloading 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==0.1.6Downloading https://pypi.tuna.tsinghua.edu.cn/packages/f2/51/c34d7a1d528efaae3d8ddb18ef45a41f284eacf9e514523b191b7d0872cc/matplotlib_inline-0.1.6-py3-none-any.whl (9.4 kB)
  Collecting nltk==3.8.1Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a6/0a/0d20d2c0f16be91b9fa32a77b76c60f9baf6eba419e5ef5deca17af9c582/nltk-3.8.1-py3-none-any.whl (1.5 MB)
  Collecting numpy==1.23.0Downloading 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==23.1Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ab/c3/57f0601a2d4fe15de7a553c00adbc901425661bf048f2a22dfc500caf121/packaging-23.1-py3-none-any.whl (48 kB)
  Collecting pandas==2.0.3Downloading 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==0.8.3Downloading https://pypi.tuna.tsinghua.edu.cn/packages/05/63/8011bd08a4111858f79d2b09aad86638490d62fbf881c44e434a6dfca87b/parso-0.8.3-py2.py3-none-any.whl (100 kB)
  Collecting pexpect==4.8.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/39/7b/88dbb785881c28a102619d46423cb853b46dbccc70d3ac362d99773a78ce/pexpect-4.8.0-py2.py3-none-any.whl (59 kB)
  Collecting pickleshare==0.7.5Downloading https://pypi.tuna.tsinghua.edu.cn/packages/9a/41/220f49aaea88bc6fa6cba8d05ecf24676326156c23b991e80b3f2fc24c77/pickleshare-0.7.5-py2.py3-none-any.whl (6.9 kB)
  Collecting Pillow==10.0.0Downloading 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==3.10.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/14/51/fe5a0d6ea589f0d4a1b97824fb518962ad48b27cd346dcdfa2405187997a/platformdirs-3.10.0-py3-none-any.whl (17 kB)
  Collecting prompt-toolkit==3.0.39Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a9/b4/ba77c84edf499877317225d7b7bc047a81f7c2eed9628eeb6bab0ac2e6c9/prompt_toolkit-3.0.39-py3-none-any.whl (385 kB)
  Collecting protobuf==4.23.4Downloading https://pypi.tuna.tsinghua.edu.cn/packages/01/cb/445b3e465abdb8042a41957dc8f60c54620dc7540dbcf9b458a921531ca2/protobuf-4.23.4-cp37-abi3-manylinux2014_x86_64.whl (304 kB)
  Collecting psutil==5.9.5Downloading 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==3.0.9Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6c/10/a7d0fa5baea8fe7b50f448ab742f26f52b80bfca85ac2be9d35cdd9a3246/pyparsing-3.0.9-py3-none-any.whl (98 kB)
  Collecting python-dateutil==2.8.2Downloading 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==0.6.2Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d7/e0/c08d5553b89973d9a240605b9c12404bcf8227590de62bae27acbcfe076b/pytorch_pretrained_bert-0.6.2-py3-none-any.whl (123 kB)
  Collecting pytz==2023.3Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7f/99/ad6bd37e748257dd70d6f85d916cafe79c0b0f5e2e95b11f7fbc82bf3110/pytz-2023.3-py2.py3-none-any.whl (502 kB)
  Collecting PyYAML==6.0Downloading 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==25.1.0Downloading 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==0.3.1Downloading 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==1.3.0Downloading 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==1.11.1Downloading 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==0.1.99Downloading 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==2.6.2Downloading https://pypi.tuna.tsinghua.edu.cn/packages/44/7b/eee50dcadcee4c674353ca207fdcd53a5b1f382021af1ed1797f9c0c45d2/tensorboardX-2.6.2-py2.py3-none-any.whl (101 kB)
  Collecting threadpoolctl==3.2.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/81/12/fd4dea011af9d69e1cad05c75f3f7202cdcbeac9b712eea58ca779a72865/threadpoolctl-3.2.0-py3-none-any.whl (15 kB)
  Collecting tokenizers==0.12.1Downloading 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==1.10.1Downloading 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==4.65.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e6/02/a2cff6306177ae6bc73bc0665065de51dfb3b9db7373e122e2735faf0d97/tqdm-4.65.0-py3-none-any.whl (77 kB)
  Collecting transformers==4.30.2Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5b/0b/e45d26ccd28568013523e04f325432ea88a442b4e3020b757cf4361f0120/transformers-4.30.2-py3-none-any.whl (7.2 MB)
  Collecting urllib3==1.26.16Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c5/05/c214b32d21c0b465506f95c4f28ccbcba15022e000b043b72b3df7728471/urllib3-1.26.16-py2.py3-none-any.whl (143 kB)
  Collecting blinker>=1.6.2Downloading https://pypi.tuna.tsinghua.edu.cn/packages/0d/f1/5f39e771cd730d347539bb74c6d496737b9d5f0a53bc9fdbf3e170f1ee48/blinker-1.6.2-py3-none-any.whl (13 kB)
  Collecting click>=8.1.3Downloading https://pypi.tuna.tsinghua.edu.cn/packages/1a/70/e63223f8116931d365993d4a6b7ef653a4d920b41d03de7c59499962821f/click-8.1.6-py3-none-any.whl (97 kB)
  Collecting contourpy>=1.0.1Downloading 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>=0.10Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5c/f9/695d6bedebd747e5eb0fe8fad57b72fdf25411273a39791cde838d5a8f51/cycler-0.11.0-py3-none-any.whl (6.4 kB)
  Collecting fonttools>=4.22.0Downloading 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>=2.1.2Downloading https://pypi.tuna.tsinghua.edu.cn/packages/68/5f/447e04e828f47465eeab35b5d408b7ebaaaee207f48b7136c5a7267a30ae/itsdangerous-2.1.2-py3-none-any.whl (15 kB)
  Collecting jedi>=0.16Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8e/46/7e3ae3aa2dcfcffc5138c6cef5448523218658411c84a2000bf75c8d3ec1/jedi-0.19.0-py2.py3-none-any.whl (1.6 MB)
  Collecting Jinja2>=3.1.2Downloading https://pypi.tuna.tsinghua.edu.cn/packages/bc/c3/f068337a370801f372f2f8f6bad74a5c140f6fda3d9de154052708dd3c65/Jinja2-3.1.2-py3-none-any.whl (133 kB)
  Collecting kiwisolver>=1.0.1Downloading 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>=2.0Downloading 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>=0.5Downloading https://pypi.tuna.tsinghua.edu.cn/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl (13 kB)
  Collecting pygments>=2.4.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/43/88/29adf0b44ba6ac85045e63734ae0997d3c58d8b1a91c914d240828d0d73d/Pygments-2.16.1-py3-none-any.whl (1.2 MB)
  Collecting regex>=2021.8.3Downloading 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)
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  Building wheels for collected packages: jiebaBuilding wheel for jieba (setup.py): startedBuilding 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=57e3819b1f7805dacd361648a3cb7f041a401078f47663d2f5d58f4523b2c1c7Stored in directory: /tmp/pip-ephem-wheel-cache-oey5ckcm/wheels/1a/76/68/b6d79c4db704bb18d54f6a73ab551185f4711f9730c0c15d97
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  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