Platform For AI:Deploy an LLM as a service

Standard deployment

Call an EAS service by using WebUI

1. Find the desired service and click View Web App in the Service Type column.

2. Test the inference performance on the WebUI page.

   Enter the dialogue content in the text box on the ChatLLM-WebUI page. For example, you can enter What is the capital of Canada? and click Send to start a dialogue.

Call an EAS service by using API operations

1. Retrieve the service endpoint and token.

   1. Navigate to EAS, select a workspace, and access EAS.

   2. Click the name of the desired service to view its details page.

   3. In the Basic Information section, click View Call Information. On the Public Endpoint Call tab, retrieve the service token and endpoint.

2. To call API operations to perform inference, use one of the following methods:

   Use HTTP

   • Non-streaming mode

     The client sends the following types of standard HTTP requests when curl commands are run.

     • STRING requests

       curl $host -H 'Authorization: $authorization' --data-binary @chatllm_data.txt -v

       Replace $authorization with the service token. Replace $host with the service endpoint. The chatllm_data.txt file is a plain text file that contains the prompt, such as what is the capital of Canada?

     • Structured requests

       curl $host -H 'Authorization: $authorization' -H "Content-type: application/json" --data-binary @chatllm_data.json -v -H "Connection: close"

       Use the chatllm_data.json file to configure inference parameters. The following sample code provides a format example of the chatllm_data.json file:

       {
         "max_new_tokens": 4096,
         "use_stream_chat": false,
         "prompt": "What is the capital of Canada?",
         "system_prompt": "Act like you are a knowledgeable assistant who can provide information on geography and related topics.",
         "history": [
           [
             "Can you tell me what's the capital of France?",
             "The capital of France is Paris."
           ]
         ],
         "temperature": 0.8,
         "top_k": 10,
         "top_p": 0.8,
         "do_sample": true,
         "use_cache": true
       }

       The following table describes the parameters in the preceding code. Configure the parameters based on your business requirements.

       Parameter	Description	Default value
       max_new_tokens	The maximum number of output tokens.	2048
       use_stream_chat	Specifies whether to return the output tokens in streaming mode.	true
       prompt	The user prompt.	""
       system_prompt	The system prompt.	""
       history	The dialogue history. The value is in the List[Tuple(str, str)] format.	[()]
       temperature	The randomness of the model output. A larger value specifies a higher randomness. The value 0 specifies a fixed output. The value is of the Float type and ranges from 0 to 1.	0.95
       top_k	The number of outputs selected from the generated results.	30
       top_p	The probability threshold of outputs selected from the generated results. The value is of the Float type and ranges from 0 to 1.	0.8
       do_sample	Specifies whether to enable output sampling.	true
       use_cache	Specifies whether to enable KV cache.	true

     • You can also implement your client based on the Python requests package. You can use the --prompt parameter to specify the request content, such as python xxx.py --prompt "What is the capital of Canada?".

       import argparse
       import json
       from typing import Iterable, List
       
       import requests
       
       def post_http_request(prompt: str,
                             system_prompt: str,
                             history: list,
                             host: str,
                             authorization: str,
                             max_new_tokens: int = 2048,
                             temperature: float = 0.95,
                             top_k: int = 1,
                             top_p: float = 0.8,
                             langchain: bool = False,
                             use_stream_chat: bool = False) -> requests.Response:
           headers = {
               "User-Agent": "Test Client",
               "Authorization": f"{authorization}"
           }
           if not history:
               history = [
                   (
                       "San Francisco is a",
                       "city located in the state of California in the United States. \
                       It is known for its iconic landmarks, such as the Golden Gate Bridge \
                       and Alcatraz Island, as well as its vibrant culture, diverse population, \
                       and tech industry. The city is also home to many famous companies and \
                       startups, including Google, Apple, and Twitter."
                   )
               ]
           pload = {
               "prompt": prompt,
               "system_prompt": system_prompt,
               "top_k": top_k,
               "top_p": top_p,
               "temperature": temperature,
               "max_new_tokens": max_new_tokens,
               "use_stream_chat": use_stream_chat,
               "history": history
           }
           if langchain:
               pload["langchain"] = langchain
           response = requests.post(host, headers=headers,
                                    json=pload, stream=use_stream_chat)
           return response
       
       def get_response(response: requests.Response) -> List[str]:
           data = json.loads(response.content)
           output = data["response"]
           history = data["history"]
           return output, history
       
       if __name__ == "__main__":
           parser = argparse.ArgumentParser()
           parser.add_argument("--top-k", type=int, default=4)
           parser.add_argument("--top-p", type=float, default=0.8)
           parser.add_argument("--max-new-tokens", type=int, default=2048)
           parser.add_argument("--temperature", type=float, default=0.95)
           parser.add_argument("--prompt", type=str, default="How can I get there?")
           parser.add_argument("--langchain", action="store_true")
       
           args = parser.parse_args()
       
           prompt = args.prompt
           top_k = args.top_k
           top_p = args.top_p
           use_stream_chat = False
           temperature = args.temperature
           langchain = args.langchain
           max_new_tokens = args.max_new_tokens
       
           host = "<Public endpoint of the EAS service>"
           authorization = "<Public token of the EAS service>"
       
           print(f"Prompt: {prompt!r}\n", flush=True)
           # System prompts can be included in the requests. 
           system_prompt = "Act like you are programmer with \
                       5+ years of experience."
       
           # Dialogue history can be included in the client request. The client manages the dialogue history to implement multi-round dialogues. In most cases, information from the previous round of dialogue is used. The information is in the List[Tuple(str, str)] format. 
           history = []
           response = post_http_request(
               prompt, system_prompt, history,
               host, authorization,
               max_new_tokens, temperature, top_k, top_p,
               langchain=langchain, use_stream_chat=use_stream_chat)
           output, history = get_response(response)
           print(f" --- output: {output} \n --- history: {history}", flush=True)
       
       # The server returns a JSON response that includes the inference result and dialogue history. 
       def get_response(response: requests.Response) -> List[str]:
           data = json.loads(response.content)
           output = data["response"]
           history = data["history"]
           return output, history

       Take note of the following parameters:

       • Set the host parameter to the service endpoint.

       • Set the authorization parameter to the service token.

   • Streaming mode

     In streaming mode, the HTTP SSE method is used. You can use the --prompt parameter to specify the request content, such as python xxx.py --prompt "What is the capital of Canada?".

     import argparse
     import json
     from typing import Iterable, List
     
     import requests
     
     
     def clear_line(n: int = 1) -> None:
         LINE_UP = '\033[1A'
         LINE_CLEAR = '\x1b[2K'
         for _ in range(n):
             print(LINE_UP, end=LINE_CLEAR, flush=True)
     
     
     def post_http_request(prompt: str,
                           system_prompt: str,
                           history: list,
                           host: str,
                           authorization: str,
                           max_new_tokens: int = 2048,
                           temperature: float = 0.95,
                           top_k: int = 1,
                           top_p: float = 0.8,
                           langchain: bool = False,
                           use_stream_chat: bool = False) -> requests.Response:
         headers = {
             "User-Agent": "Test Client",
             "Authorization": f"{authorization}"
         }
         if not history:
             history = [
                 (
                     "San Francisco is a",
                     "city located in the state of California in the United States. \
                     It is known for its iconic landmarks, such as the Golden Gate Bridge \
                     and Alcatraz Island, as well as its vibrant culture, diverse population, \
                     and tech industry. The city is also home to many famous companies and \
                     startups, including Google, Apple, and Twitter."
                 )
             ]
         pload = {
             "prompt": prompt,
             "system_prompt": system_prompt,
             "top_k": top_k,
             "top_p": top_p,
             "temperature": temperature,
             "max_new_tokens": max_new_tokens,
             "use_stream_chat": use_stream_chat,
             "history": history
         }
         if langchain:
             pload["langchain"] = langchain
         response = requests.post(host, headers=headers,
                                  json=pload, stream=use_stream_chat)
         return response
     
     
     def get_streaming_response(response: requests.Response) -> Iterable[List[str]]:
         for chunk in response.iter_lines(chunk_size=8192,
                                          decode_unicode=False,
                                          delimiter=b"\0"):
             if chunk:
                 data = json.loads(chunk.decode("utf-8"))
                 output = data["response"]
                 history = data["history"]
                 yield output, history
     
     
     if __name__ == "__main__":
         parser = argparse.ArgumentParser()
         parser.add_argument("--top-k", type=int, default=4)
         parser.add_argument("--top-p", type=float, default=0.8)
         parser.add_argument("--max-new-tokens", type=int, default=2048)
         parser.add_argument("--temperature", type=float, default=0.95)
         parser.add_argument("--prompt", type=str, default="How can I get there?")
         parser.add_argument("--langchain", action="store_true")
         args = parser.parse_args()
     
         prompt = args.prompt
         top_k = args.top_k
         top_p = args.top_p
         use_stream_chat = True
         temperature = args.temperature
         langchain = args.langchain
         max_new_tokens = args.max_new_tokens
     
         host = ""
         authorization = ""
     
         print(f"Prompt: {prompt!r}\n", flush=True)
         system_prompt = "Act like you are programmer with \
                     5+ years of experience."
         history = []
         response = post_http_request(
             prompt, system_prompt, history,
             host, authorization,
             max_new_tokens, temperature, top_k, top_p,
             langchain=langchain, use_stream_chat=use_stream_chat)
     
         for h, history in get_streaming_response(response):
             print(
                 f" --- stream line: {h} \n --- history: {history}", flush=True)
     

     Take note of the following parameters:

     • Set the host parameter to the service endpoint.

     • Set the authorization parameter to the service token.

   Use WebSocket

   The WebSocket protocol can efficiently handle the dialogue history. You can use the WebSocket method to connect to the service and perform one or more rounds of dialogue. Sample code:

   import os
   import time
   import json
   import struct
   from multiprocessing import Process
   
   import websocket
   
   round = 5
   questions = 0
   
   
   def on_message_1(ws, message):
       if message == "<EOS>":
           print('pid-{} timestamp-({}) receives end message: {}'.format(os.getpid(),
                 time.time(), message), flush=True)
           ws.send(struct.pack('!H', 1000), websocket.ABNF.OPCODE_CLOSE)
       else:
           print("{}".format(time.time()))
           print('pid-{} timestamp-({}) --- message received: {}'.format(os.getpid(),
                 time.time(), message), flush=True)
   
   
   def on_message_2(ws, message):
       global questions
       print('pid-{} --- message received: {}'.format(os.getpid(), message))
       # end the client-side streaming
       if message == "<EOS>":
           questions = questions + 1
           if questions == 5:
               ws.send(struct.pack('!H', 1000), websocket.ABNF.OPCODE_CLOSE)
   
   
   def on_message_3(ws, message):
       print('pid-{} --- message received: {}'.format(os.getpid(), message))
       # end the client-side streaming
       ws.send(struct.pack('!H', 1000), websocket.ABNF.OPCODE_CLOSE)
   
   
   def on_error(ws, error):
       print('error happened: ', str(error))
   
   
   def on_close(ws, a, b):
       print("### closed ###", a, b)
   
   
   def on_pong(ws, pong):
       print('pong:', pong)
   
   # stream chat validation test
   def on_open_1(ws):
       print('Opening Websocket connection to the server ... ')
       params_dict = {}
       params_dict['prompt'] = """Show me a golang code example: """
       params_dict['temperature'] = 0.9
       params_dict['top_p'] = 0.1
       params_dict['top_k'] = 30
       params_dict['max_new_tokens'] = 2048
       params_dict['do_sample'] = True
       raw_req = json.dumps(params_dict, ensure_ascii=False).encode('utf8')
       # raw_req = f"""To open a Websocket connection to the server: """
   
       ws.send(raw_req)
       # end the client-side streaming
   
   
   # multi-round query validation test
   def on_open_2(ws):
       global round
       print('Opening Websocket connection to the server ... ')
       params_dict = {"max_new_tokens": 6144}
       params_dict['temperature'] = 0.9
       params_dict['top_p'] = 0.1
       params_dict['top_k'] = 30
       params_dict['use_stream_chat'] = True
       params_dict['prompt'] = "Hello!"
       params_dict = {
           "system_prompt":
           "Act like you are programmer with 5+ years of experience."
       }
       raw_req = json.dumps(params_dict, ensure_ascii=False).encode('utf8')
       ws.send(raw_req)
       params_dict['prompt'] = "Please write a sorting algorithm in Python."
       raw_req = json.dumps(params_dict, ensure_ascii=False).encode('utf8')
       ws.send(raw_req)
       params_dict['prompt'] = "Please convert the programming language to Java."
       raw_req = json.dumps(params_dict, ensure_ascii=False).encode('utf8')
       ws.send(raw_req)
       params_dict['prompt'] = "Please introduce yourself."
       raw_req = json.dumps(params_dict, ensure_ascii=False).encode('utf8')
       ws.send(raw_req)
       params_dict['prompt'] = "Please summarize the dialogue above."
       raw_req = json.dumps(params_dict, ensure_ascii=False).encode('utf8')
       ws.send(raw_req)
   
   
   # Langchain validation test.
   def on_open_3(ws):
       global round
       print('Opening Websocket connection to the server ... ')
   
       params_dict = {}
       # params_dict['prompt'] = """To open a Websocket connection to the server: """
       params_dict['prompt'] = """Can you tell me what's the MNN?"""
       params_dict['temperature'] = 0.9
       params_dict['top_p'] = 0.1
       params_dict['top_k'] = 30
       params_dict['max_new_tokens'] = 2048
       params_dict['use_stream_chat'] = False
       params_dict['langchain'] = True
       raw_req = json.dumps(params_dict, ensure_ascii=False).encode('utf8')
       ws.send(raw_req)
   
   
   authorization = ""
   host = "ws://" + ""
   
   
   def single_call(on_open_func, on_message_func, on_clonse_func=on_close):
       ws = websocket.WebSocketApp(
           host,
           on_open=on_open_func,
           on_message=on_message_func,
           on_error=on_error,
           on_pong=on_pong,
           on_close=on_clonse_func,
           header=[
               'Authorization: ' + authorization],
       )
   
       # setup ping interval to keep long connection.
       ws.run_forever(ping_interval=2)
   
   
   if __name__ == "__main__":
       for i in range(5):
           p1 = Process(target=single_call, args=(on_open_1, on_message_1))
           p2 = Process(target=single_call, args=(on_open_2, on_message_2))
           p3 = Process(target=single_call, args=(on_open_3, on_message_3))
   
           p1.start()
           p2.start()
           p3.start()
   
           p1.join()
           p2.join()
           p3.join()

   Take note of the following parameters:

   • Set the authorization parameter to the service token.

   • Set the host parameter to the service endpoint. Replace the http prefix in the endpoint with ws.

   • Use the use_stream_chat parameter to specify whether the client generates output in streaming mode. Default value: True.

   • Refer to the on_open_2 function in the preceding code to implement a multi-round dialogue.

BladeLLM-based accelerated deployment

BladeLLM-based accelerated deployment allows you to call a service only by calling API operations. To call a service, perform the following steps:

1. To view the service access address and token:

   1. On the Model Online Service (EAS) page, click the Service Method column of the desired service, then select Call Information.

   2. In the Call Information dialog box, note the service access address and token.

2. Execute the following code in the terminal to call the service and receive the generated text.

   # Call EAS service
   curl -X POST \
       -H "Content-Type: application/json" \
       -H "Authorization: AUTH_TOKEN_FOR_EAS" \
       -d '{"prompt":"What is the capital of Canada?", "stream":"true"}' \
       <service_url>/v1/completions

   Take note of the following parameters:

   • Authorization: Set this to the service token obtained in the previous step.

   • <service_url>: Replace this with the service access address obtained in the previous step.

   You should receive the following command output:

   data: {"id":"91f9a28a-f949-40fb-b720-08ceeeb2****","choices":[{"finish_reason":"","index":0,"logprobs":null,"text":" The"}],"object":"text_completion","usage":{"prompt_tokens":7,"completion_tokens":1,"total_tokens":8},"error_info":null}
   
   data: {"id":"91f9a28a-f949-40fb-b720-08ceeeb2****","choices":[{"finish_reason":"","index":0,"logprobs":null,"text":" capital"}],"object":"text_completion","usage":{"prompt_tokens":7,"completion_tokens":2,"total_tokens":9},"error_info":null}
   
   data: {"id":"91f9a28a-f949-40fb-b720-08ceeeb2****","choices":[{"finish_reason":"","index":0,"logprobs":null,"text":" of"}],"object":"text_completion","usage":{"prompt_tokens":7,"completion_tokens":3,"total_tokens":10},"error_info":null}
   
   data: {"id":"91f9a28a-f949-40fb-b720-08ceeeb2****","choices":[{"finish_reason":"","index":0,"logprobs":null,"text":" Canada"}],"object":"text_completion","usage":{"prompt_tokens":7,"completion_tokens":4,"total_tokens":11},"error_info":null}
   
   data: {"id":"91f9a28a-f949-40fb-b720-08ceeeb2****","choices":[{"finish_reason":"","index":0,"logprobs":null,"text":" is"}],"object":"text_completion","usage":{"prompt_tokens":7,"completion_tokens":5,"total_tokens":12},"error_info":null}
   
   data: {"id":"91f9a28a-f949-40fb-b720-08ceeeb2****","choices":[{"finish_reason":"","index":0,"logprobs":null,"text":" Ottawa"}],"object":"text_completion","usage":{"prompt_tokens":7,"completion_tokens":6,"total_tokens":13},"error_info":null}
   
   data: {"id":"91f9a28a-f949-40fb-b720-08ceeeb2****","choices":[{"finish_reason":"","index":0,"logprobs":null,"text":"."}],"object":"text_completion","usage":{"prompt_tokens":7,"completion_tokens":7,"total_tokens":14},"error_info":null}
   
   data: {"id":"91f9a28a-f949-40fb-b720-08ceeeb2****","choices":[{"finish_reason":"stop","index":0,"logprobs":null,"text":""}],"object":"text_completion","usage":{"prompt_tokens":7,"completion_tokens":8,"total_tokens":15},"error_info":null}
   
   data: [DONE]

vLLM-based accelerated deployment

vLLM-based accelerated deployment allows you to call a service only by calling API operations. To call a service, perform the following steps:

1. To view the service access address and token:

   1. On the Model Online Service (EAS) page, click the Service Method column of the desired service, then select Call Information.

   2. In the Call Information dialog box, note the service access address and token.

2. In the terminal, run the following code to call the service:

   Python

   from openai import OpenAI
   
   ##### API configuration #####
   openai_api_key = "<EAS API KEY>"
   openai_api_base = "<EAS API Endpoint>/v1"
   
   client = OpenAI(
       api_key=openai_api_key,
       base_url=openai_api_base,
   )
   
   models = client.models.list()
   model = models.data[0].id
   print(model)
   
   
   def main():
   
       stream = True
   
       chat_completion = client.chat.completions.create(
           messages=[
               {
                   "role": "user",
                   "content": [
                       {
                           "type": "text",
                           "text": "What is the capital of Canada?",
                       }
                   ],
               }
           ],
           model=model,
           max_completion_tokens=2048,
           stream=stream,
       )
   
       if stream:
           for chunk in chat_completion:
               print(chunk.choices[0].delta.content, end="")
       else:
           result = chat_completion.choices[0].message.content
           print(result)
   
   
   if __name__ == "__main__":
       main()
   

   Take note of the following parameters:

   • <EAS API KEY>: Set this parameter to the service token that you obtained.

   • <EAS API Endpoint>: Set this parameter to the service endpoint that you obtained.

   CLI

   curl -X POST <service_url>/v1/chat/completions -d '{
       "model": "Qwen2.5-7B-Instruct",
       "messages": [
           {
               "role": "system",
               "content": [
                   {
                       "type": "text",
                       "text": "You are a helpful and harmless assistant."
                   }
               ]
           },
           {
               "role": "user",
               "content": "What is the capital of Canada?"
           }
       ]
   }' -H "Content-Type: application/json" -H "Authorization: <your-token>"

   Take note of the following parameters:

   • <service_url>: Set this parameter to the service endpoint that you obtained.

   • <your-token>: Set this parameter to the service token that you obtained.