text2vec-service

Bert model to vector service.

text2vec-service搭建了一个高效的文本转向量(Text-To-Vector)服务。

Guide - Feature - Install - Usage - Contact - Reference

Feature

BERT service with C/S.

Install

shell pip install torch # conda install pytorch pip install -U text2vec-service

or

```shell pip install torch # conda install pytorch pip install -r requirements.txt

git clone https://github.com/shibing624/text2vec-service.git cd text2vec-service pip install --no-deps . ```

Usage

1. Start the BERT service

After installing the server, you should be able to use service-server-start CLI as follows: bash service-server-start -model_dir shibing624/text2vec-base-chinese This will start a service with four workers, meaning that it can handle up to four concurrent requests. More concurrent requests will be queued in a load balancer.

Alternatively, one can start the BERT Service in a Docker Container (click to expand...)

```bash docker build -t text2vec-service -f ./docker/Dockerfile . NUM_WORKER=1 PATH_MODEL=/PATH_TO/_YOUR_MODEL/ docker run --runtime nvidia -dit -p 5555:5555 -p 5556:5556 -v $PATH_MODEL:/model -t text2vec-service $NUM_WORKER ```

2. Use Client to Get Sentence Encodes

Now you can encode sentences simply as follows: python from service.client import BertClient bc = BertClient() bc.encode(['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']) It will return a ndarray (or List[List[float]] if you wish), in which each row is a fixed-length vector representing a sentence. Having thousands of sentences? Just encode! Don't even bother to batch, the server will take care of it.

Use BERT Service Remotely

One may also start the service on one (GPU) machine and call it from another (CPU) machine as follows:

```python

on another CPU machine

from service.client import BertClient bc = BertClient(ip='xx.xx.xx.xx') # ip address of the GPU machine bc.encode(['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']) ```

Server and Client API

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Server API

bash service-server-start --help service-server-terminate --help service-server-benchmark --help

| Argument | Type | Default | Description | |--------------------|------|-------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------| | model_dir | str | Required | folder path of the pre-trained BERT model. | | max_seq_len | int | 25 | maximum length of sequence, longer sequence will be trimmed on the right side. Set it to NONE for dynamically using the longest sequence in a (mini)batch. | | cased_tokenization | bool | False | Whether tokenizer should skip the default lowercasing and accent removal. Should be used for e.g. the multilingual cased pretrained BERT model. | | num_worker | int | 1 | number of (GPU/CPU) worker runs BERT model, each works in a separate process. | | max_batch_size | int | 256 | maximum number of sequences handled by each worker, larger batch will be partitioned into small batches. | | priority_batch_size | int | 16 | batch smaller than this size will be labeled as high priority, and jumps forward in the job queue to get result faster | | port | int | 5555 | port for pushing data from client to server | | port_out | int | 5556| port for publishing results from server to client | | http_port | int | None | server port for receiving HTTP requests | | cors | str | * | setting "Access-Control-Allow-Origin" for HTTP requests | | gpu_memory_fraction | float | 0.5 | the fraction of the overall amount of memory that each GPU should be allocated per worker | | cpu | bool | False | run on CPU instead of GPU | | xla | bool | False | enable XLA compiler for graph optimization (experimental!) | | fp16 | bool | False | use float16 precision (experimental) | | device_map | list | [] | specify the list of GPU device ids that will be used (id starts from 0)|

Client API

| Argument | Type | Default | Description | |----------------------|------|-----------|-------------------------------------------------------------------------------| | ip | str | localhost | IP address of the server | | port | int | 5555 | port for pushing data from client to server, must be consistent with the server side config | | port_out | int | 5556| port for publishing results from server to client, must be consistent with the server side config | | output_fmt | str | ndarray | the output format of the sentence encodes, either in numpy array or python ListList[float] | | show_server_config | bool | False | whether to show server configs when first connected | | check_version | bool | True | whether to force client and server to have the same version | | identity | str | None | a UUID that identifies the client, useful in multi-casting | | timeout | int | -1 | set the timeout (milliseconds) for receive operation on the client |

A BertClient implements the following methods and properties:

| Method | Description | |--------|------| |.encode()|Encode a list of strings to a list of vectors| |.encode_async()|Asynchronous encode batches from a generator| |.fetch()|Fetch all encoded vectors from server and return them in a generator, use it with .encode_async() or .encode(blocking=False). Sending order is NOT preserved.| |.fetch_all()|Fetch all encoded vectors from server and return them in a list, use it with .encode_async() or .encode(blocking=False). Sending order is preserved.| |.close()|Gracefully close the connection between the client and the server| |.status|Get the client status in JSON format| |.server_status|Get the server status in JSON format|

:book: Tutorial

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The full list of examples can be found in examples/. You can run each via python examples/base-demo.py.

Serving a fine-tuned BERT model

Pretrained BERT models often show quite "okayish" performance on many tasks. However, to release the true power of BERT a fine-tuning on the downstream task (or on domain-specific data) is necessary.

In this example, serve a fine-tuned BERT model.

bash service-server-start -model_dir shibing624/bert-base-chinese

Asynchronous encoding

The complete example can be found examples/async_demo.py.

BertClient.encode() offers a nice synchronous way to get sentence encodes. However, sometimes we want to do it in an asynchronous manner by feeding all textual data to the server first, fetching the encoded results later. This can be easily done by: ```python

an endless data stream, generating data in an extremely fast speed

def textgen(): while True: yield lststr # yield a batch of text lines

bc = BertClient()

get encoded vectors

for j in bc.encodeasync(textgen(), maxnumbatch=10): print('received %d x %d' % (j.shape[0], j.shape[1])) ```

Broadcasting to multiple clients

example: examples/multicast_demo.py.

The encoded result is routed to the client according to its identity. If you have multiple clients with same identity, then they all receive the results! You can use this multicast feature to do some cool things, e.g. training multiple different models (some using scikit-learn some using pytorch) in multiple separated processes while only call BertServer once. In the example below, bc and its two clones will all receive encoded vector.

```python

clone a client by reusing the identity

def client_clone(id, idx): bc = BertClient(identity=id) for j in bc.listen(): print('clone-client-%d: received %d x %d' % (idx, j.shape[0], j.shape[1]))

bc = BertClient()

start two cloned clients sharing the same identity as bc

for j in range(2): threading.Thread(target=client_clone, args=(bc.identity, j)).start()

for _ in range(3): bc.encode(lst_str) ```

Monitoring the service status in a dashboard

The complete example can be found in plugin/dashboard/.

As a part of the infrastructure, one may also want to monitor the service status and show it in a dashboard. To do that, we can use: ```python bc = BertClient(ip='server_ip')

json.dumps(bc.serverstatus, ensureascii=False) ```

This gives the current status of the server including number of requests, number of clients etc. in JSON format. The only thing remained is to start a HTTP server for returning this JSON to the frontend that renders it.

Alternatively, one may simply expose an HTTP port when starting a server via:

bash bert-serving-start -http_port 8081

This will allow one to use javascript or curl to fetch the server status at port 8081.

plugin/dashboard/index.html shows a simple dashboard based on Bootstrap and Vue.js.

Using text2vec-service to serve HTTP requests in JSON

Besides calling text2vec-service from Python, one can also call it via HTTP request in JSON. It is quite useful especially when low transport layer is prohibited. Behind the scene, text2vec-service spawns a Flask server in a separate process and then reuse a BertClient instance as a proxy to communicate with the ventilator.

To enable the build-in HTTP server, we need to first (re)install the server with some extra Python dependencies: bash pip install "text2vec-service[http]"

Then simply start the server with: bash bert-serving-start -model_dir=/YOUR_MODEL -http_port 8081

Done! Your server is now listening HTTP and TCP requests at port 8081 simultaneously!

To send a HTTP request, first prepare the payload in JSON as following: json { "id": 123, "texts": ["hello world", "good day!"] } , where id is a unique identifier helping you to synchronize the results.

Then simply call the server at /encode via HTTP POST request. You can use javascript or whatever, here is an example using curl: bash curl -X POST http://xx.xx.xx.xx:8081/encode \ -H 'content-type: application/json' \ -d '{"id": 123,"texts": ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']}' , which returns a JSON: json { "id": 123, "results": [[768 float-list], [768 float-list]], "status": 200 }

To get the server's status and client's status, you can send GET requests at /status/server and /status/client, respectively.

Finally, one may also config CORS to restrict the public access of the server by specifying -cors when starting bert-serving-start. By default -cors=*, meaning the server is public accessible.

Starting BertServer from Python

Besides shell, one can also start a BertServer from python. Simply do python from service.server.helper import get_args_parser from service.server import BertServer args = get_args_parser().parse_args(['-model_dir', 'YOUR_MODEL_PATH_HERE', '-port', '5555', '-port_out', '5556', '-max_seq_len', 'NONE', '-mask_cls_sep', '-cpu']) server = BertServer(args) server.start()

Note that it's basically mirroring the arg-parsing behavior in CLI, so everything in that .parse_args([]) list should be string, e.g. ['-port', '5555'] not ['-port', 5555].

To shutdown the server, you may call the static method in BertServer class via with args: python shut_args = get_shutdown_parser().parse_args(['-ip','localhost','-port','5555','-timeout','5000']) BertServer.shutdown(shutdown_args)

Or via shell CLI: bash bert-serving-terminate -port 5555

This will terminate the server running on localhost at port 5555. You may also use it to terminate a remote server, see bert-serving-terminate --help for details.

Contact

• Issue(建议)：
• 邮件我：xuming: xuming624@qq.com
• 微信我： 加我微信号：xuming624, 备注：姓名-公司-NLP 进NLP交流群。

Citation

如果你在研究中使用了text2vec-service，请按如下格式引用：

APA: latex Xu, M. text2vec-service: Bert model embedding service (Version 0.0.2) [Computer software]. https://github.com/shibing624/text2vec-service

BibTeX: latex @software{Xu_text2vec-service_Text_to, author = {Xu, Ming}, title = {{text2vec-service: Bert model embedding service}}, url = {https://github.com/shibing624/text2vec-service}, version = {0.0.2} }

License

授权协议为 The Apache License 2.0，可免费用做商业用途。请在产品说明中附加text2vec-service的链接和授权协议。

Contribute

项目代码还很粗糙，如果大家对代码有所改进，欢迎提交回本项目，在提交之前，注意以下两点：

• 在tests添加相应的单元测试
• 使用python -m pytest -v来运行所有单元测试，确保所有单测都是通过的

之后即可提交PR。

Reference

• jina-ai/clip-as-service
• huggingface/transformers