在 Gpu 上使用bert Ner

小咪咪 2022-12-20 11:25 187阅读 0赞

### 查看 gpu 支持 ###

# 查看 gpu 支持
    nvidia-smi
    Wed Nov 11 09:28:37 2020
    +-----------------------------------------------------------------------------+
    | NVIDIA-SMI 418.67       Driver Version: 418.67       CUDA Version: 10.1     |
    |-------------------------------+----------------------+----------------------+
    | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
    | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
    |===============================+======================+======================|
    |   0  Tesla T4            Off  | 00000000:21:01.0 Off |                    0 |
    | N/A   56C    P0    28W /  70W |      0MiB / 15079MiB |      6%      Default |
    +-------------------------------+----------------------+----------------------+
    
    
    +-----------------------------------------------------------------------------+
    | Processes:                                                       GPU Memory |
    |  GPU       PID   Type   Process name                             Usage      |
    |=============================================================================|
    |  No running processes found                                                 |
    +——————————————————————————————————————+

### tensor cuda 版本对应关系 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI4MDE4Mjgz_size_16_color_FFFFFF_t_70_pic_center]

### 安装 bert 环境 ###

# 安装 bert 环境
    conda create --name leon python=3.6 pip tensorflow-gpu==1.11.0 numpy pandas
    
    # 会 安装 cuda 9 和 cudNN 7 
    cudatoolkit:         9.2-0                     https://mirrors.ustc.edu.cn/anaconda/pkgs/main
    cudnn:               7.6.5-cuda9.2_0           https://mirrors.ustc.edu.cn/anaconda/pkgs/main
    tensorboard:         1.11.0-py36hf484d3e_0     https://mirrors.ustc.edu.cn/anaconda/pkgs/main
    tensorflow:          1.11.0-gpu_py36h9c9050a_0 https://mirrors.ustc.edu.cn/anaconda/pkgs/main
    tensorflow-base:     1.11.0-gpu_py36had579c0_0 https://mirrors.ustc.edu.cn/anaconda/pkgs/main
    tensorflow-gpu:      1.11.0-h0d30ee6_0         https://mirrors.ustc.edu.cn/anaconda/pkgs/main
    
    # in
    source activate leon
    
    # Note that the server MUST be running on Python >= 3.5 with Tensorflow >= 1.10 (one-point-ten) 
    
    pip install bert-serving-server  # server
    pip install bert-serving-client  # client, independent of `bert-serving-server`
    erver does not support Python 2!
    
    pip install keras_preprocessing
    pip install keras_applications
    pip install h5py==2.8.0
    
    pip install gas
    
    pip install bert-base==0.9.0

### 启动 bert ###

[pre-trained model 下载链接][pre-trained model]  
[chinese\_L-12\_H-768\_A-12][chinese_L-12_H-768_A-12]

# 启动 bert server 
    bert-base-serving-start -bert_model_dir /home/Ner/chinese_L-12_H-768_A-12 -model_pb_dir /home/Ner/model_pb_dir -model_dir /home/Ner/model_pb_dir   -mode NER  -num_worker=1
    
    bert_model_dir = /home/Ner/chinese_L-12_H-768_A-12
               ckpt_name = bert_model.ckpt
             config_name = bert_config.json
                    cors = *
                     cpu = False
              device_map = []
                    fp16 = False
     gpu_memory_fraction = 0.5
        http_max_connect = 10
               http_port = None
            mask_cls_sep = False
          max_batch_size = 1024
             max_seq_len = 128
                    mode = NER
               model_dir = /home/Ner/model_pb_dir
            model_pb_dir = /home/Ner/model_pb_dir
              num_worker = 1
           pooling_layer = [-2]
        pooling_strategy = REDUCE_MEAN
                    port = 5555
                port_out = 5556
           prefetch_size = 10
     priority_batch_size = 16
         tuned_model_dir = None
                 verbose = False
                     xla = False
    
    
    I:VENTILATOR:[__i:__i: 91]:lodding ner model, could take a while...
    pb_file exits /home/Ner/model_pb_dir/ner_model.pb
    I:VENTILATOR:[__i:__i:100]:optimized graph is stored at: /home/Ner/model_pb_dir/ner_model.pb
    I:VENTILATOR:[__i:_ru:148]:bind all sockets
    I:VENTILATOR:[__i:_ru:153]:open 8 ventilator-worker sockets, ipc://tmpUp04dl/socket,ipc://tmpG9e8EV/socket,ipc://tmpG51b6v/socket,ipc://tmpMLsgx6/socket,ipc://tmpKBelYG/socket,ipc://tmpExAqph/socket,ipc://tmp0zzwQR/socket,ipc://tmpIeRChs/socket
    I:VENTILATOR:[__i:_ru:157]:start the sink
    I:SINK:[__i:_ru:317]:ready
    I:VENTILATOR:[__i:_ge:239]:get devices
    I:VENTILATOR:[__i:_ge:271]:device map:
            worker  0 -> gpu  0
    I:WORKER-0:[__i:_ru:497]:use device gpu: 0, load graph from /home/Ner/model_pb_dir/ner_model.pb
    
    
    # Use
    from bert_serving.client import BertClient
    bc = BertClient()
    bc.encode(['First do it', 'then do it right', 'then do it better'])

### 参考 ###

[hanxiao博士 bert-as-service][hanxiao_ bert-as-service]

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[pre-trained model]: https://github.com/google-research/bert#pre-trained-models
[chinese_L-12_H-768_A-12]: https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip
[hanxiao_ bert-as-service]: https://github.com/hanxiao/bert-as-service