Remove batches api in docs & example (#7400)
This commit is contained in:
Jinn
@@ -1,94 +0,0 @@
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"""
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Usage:
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python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
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python openai_batch_chat.py
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Note: Before running this script,
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you should create the input.jsonl file with the following content:
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{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-3.5-turbo-0125", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world! List 3 NBA players and tell a story"}],"max_tokens": 300}}
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{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-3.5-turbo-0125", "messages": [{"role": "system", "content": "You are an assistant. "},{"role": "user", "content": "Hello world! List three capital and tell a story"}],"max_tokens": 500}}
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"""
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import json
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import time
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import openai
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class OpenAIBatchProcessor:
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def __init__(self):
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client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
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self.client = client
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def process_batch(self, input_file_path, endpoint, completion_window):
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# Upload the input file
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with open(input_file_path, "rb") as file:
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uploaded_file = self.client.files.create(file=file, purpose="batch")
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# Create the batch job
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batch_job = self.client.batches.create(
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input_file_id=uploaded_file.id,
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endpoint=endpoint,
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completion_window=completion_window,
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)
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# Monitor the batch job status
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while batch_job.status not in ["completed", "failed", "cancelled"]:
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time.sleep(3) # Wait for 3 seconds before checking the status again
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print(
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f"Batch job status: {batch_job.status}...trying again in 3 seconds..."
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)
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batch_job = self.client.batches.retrieve(batch_job.id)
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# Check the batch job status and errors
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if batch_job.status == "failed":
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print(f"Batch job failed with status: {batch_job.status}")
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print(f"Batch job errors: {batch_job.errors}")
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return None
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# If the batch job is completed, process the results
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if batch_job.status == "completed":
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# print result of batch job
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print("batch", batch_job.request_counts)
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result_file_id = batch_job.output_file_id
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# Retrieve the file content from the server
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file_response = self.client.files.content(result_file_id)
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result_content = file_response.read() # Read the content of the file
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# Save the content to a local file
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result_file_name = "batch_job_chat_results.jsonl"
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with open(result_file_name, "wb") as file:
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file.write(result_content) # Write the binary content to the file
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# Load data from the saved JSONL file
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results = []
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with open(result_file_name, "r", encoding="utf-8") as file:
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for line in file:
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json_object = json.loads(
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line.strip()
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) # Parse each line as a JSON object
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results.append(json_object)
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return results
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else:
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print(f"Batch job failed with status: {batch_job.status}")
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return None
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# Initialize the OpenAIBatchProcessor
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processor = OpenAIBatchProcessor()
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# Process the batch job
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input_file_path = "input.jsonl"
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endpoint = "/v1/chat/completions"
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completion_window = "24h"
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# Process the batch job
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results = processor.process_batch(input_file_path, endpoint, completion_window)
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# Print the results
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print(results)
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@@ -1,93 +0,0 @@
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"""
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Usage:
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python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
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python openai_batch_complete.py
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Note: Before running this script,
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you should create the input.jsonl file with the following content:
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{"custom_id": "request-1", "method": "POST", "url": "/v1/completions", "body": {"model": "gpt-3.5-turbo-instruct", "prompt": "List 3 names of famous soccer player: ", "max_tokens": 200}}
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{"custom_id": "request-2", "method": "POST", "url": "/v1/completions", "body": {"model": "gpt-3.5-turbo-instruct", "prompt": "List 6 names of famous basketball player: ", "max_tokens": 400}}
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{"custom_id": "request-3", "method": "POST", "url": "/v1/completions", "body": {"model": "gpt-3.5-turbo-instruct", "prompt": "List 6 names of famous basketball player: ", "max_tokens": 400}}
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"""
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import json
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import time
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import openai
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class OpenAIBatchProcessor:
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def __init__(self):
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client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
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self.client = client
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def process_batch(self, input_file_path, endpoint, completion_window):
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# Upload the input file
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with open(input_file_path, "rb") as file:
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uploaded_file = self.client.files.create(file=file, purpose="batch")
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# Create the batch job
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batch_job = self.client.batches.create(
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input_file_id=uploaded_file.id,
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endpoint=endpoint,
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completion_window=completion_window,
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)
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# Monitor the batch job status
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while batch_job.status not in ["completed", "failed", "cancelled"]:
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time.sleep(3) # Wait for 3 seconds before checking the status again
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print(
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f"Batch job status: {batch_job.status}...trying again in 3 seconds..."
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)
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batch_job = self.client.batches.retrieve(batch_job.id)
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# Check the batch job status and errors
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if batch_job.status == "failed":
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print(f"Batch job failed with status: {batch_job.status}")
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print(f"Batch job errors: {batch_job.errors}")
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return None
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# If the batch job is completed, process the results
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if batch_job.status == "completed":
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# print result of batch job
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print("batch", batch_job.request_counts)
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result_file_id = batch_job.output_file_id
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# Retrieve the file content from the server
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file_response = self.client.files.content(result_file_id)
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result_content = file_response.read() # Read the content of the file
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# Save the content to a local file
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result_file_name = "batch_job_complete_results.jsonl"
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with open(result_file_name, "wb") as file:
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file.write(result_content) # Write the binary content to the file
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# Load data from the saved JSONL file
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results = []
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with open(result_file_name, "r", encoding="utf-8") as file:
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for line in file:
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json_object = json.loads(
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line.strip()
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) # Parse each line as a JSON object
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results.append(json_object)
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return results
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else:
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print(f"Batch job failed with status: {batch_job.status}")
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return None
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# Initialize the OpenAIBatchProcessor
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processor = OpenAIBatchProcessor()
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# Process the batch job
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input_file_path = "input.jsonl"
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endpoint = "/v1/completions"
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completion_window = "24h"
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# Process the batch job
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results = processor.process_batch(input_file_path, endpoint, completion_window)
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# Print the results
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print(results)
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