批处理

{"custom_id": "req-001", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-4-turbo", "messages": [{"role": "user", "content": "分析这份财报..."}]}}
{"custom_id": "req-002", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-4-turbo", "messages": [{"role": "user", "content": "总结这篇文章..."}]}}
{"custom_id": "req-003", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-4-turbo", "messages": [{"role": "user", "content": "提取关键信息..."}]}}

import asyncio
from openai import OpenAI

client = OpenAI()

# 1. 上传批处理文件
batch_file = client.files.create(
    file=open("batch_requests.jsonl", "rb"),
    purpose="batch"
)

# 2. 创建批处理任务
batch_job = client.batches.create(
    input_file_id=batch_file.id,
    endpoint="/v1/chat/completions",
    completion_window="24h"
)

# 3. 监控进度
while batch_job.status not in ["completed", "failed"]:
    batch_job = client.batches.retrieve(batch_job.id)
    print(f"进度: {batch_job.request_counts.completed}/{batch_job.request_counts.total}")
    await asyncio.sleep(60)

# 4. 获取结果
if batch_job.status == "completed":
    results = client.files.content(batch_job.output_file_id)

class QueueManager:
    def __init__(self):
        self.queues = {
            "high_priority": PriorityQueue(),
            "standard": Queue(),
            "batch": BatchQueue()
        }
        
    def route_request(self, request):
        if request.deadline < 1_minute:
            return self.queues["high_priority"]
        elif request.deadline < 1_hour:
            return self.queues["standard"]
        else:
            return self.queues["batch"]
    
    def optimize_batch(self, requests):
        # 智能分组优化
        return group_by_similarity(requests)

def adaptive_batch_size(queue_depth, gpu_memory, avg_token_length):
    base_size = 1000
    
    # 根据队列深度调整
    if queue_depth > 10000:
        base_size *= 2
    elif queue_depth < 1000:
        base_size //= 2
    
    # 根据GPU内存调整
    memory_factor = min(gpu_memory / 40_000, 2.0)  # 40GB基准
    
    # 根据平均长度调整
    length_factor = 1000 / avg_token_length
    
    return int(base_size * memory_factor * length_factor)

class CostOptimizer:
    def schedule_jobs(self, jobs):
        # 按紧急程度分类
        urgent = [j for j in jobs if j.deadline < 1_hour]
        normal = [j for j in jobs if 1_hour <= j.deadline < 6_hours]
        batch = [j for j in jobs if j.deadline >= 6_hours]
        
        # 批量任务享受50%折扣
        batch_cost = sum(j.estimated_cost for j in batch) * 0.5
        normal_cost = sum(j.estimated_cost for j in normal) * 0.8
        urgent_cost = sum(j.estimated_cost for j in urgent)
        
        return urgent + normal + batch, batch_cost + normal_cost + urgent_cost

metrics = {
    "throughput": "tokens/second",
    "latency_p50": "seconds",
    "latency_p99": "seconds", 
    "gpu_utilization": "percentage",
    "queue_depth": "count",
    "error_rate": "percentage",
    "cost_per_token": "dollars"
}

# 法律文档批量分析
tasks = [
    {"doc_id": doc.id, "task": "extract_entities"},
    {"doc_id": doc.id, "task": "summarize"},
    {"doc_id": doc.id, "task": "classify_risk"}
]
# 处理速度：1,200 → 5,000 文件/小时

# 批量生成产品描述
products = load_product_catalog()
batch_generate_descriptions(products, 
    batch_size=1000,
    model="gpt-4-turbo",
    temperature=0.7
)
# 成本降低：73%

# 大规模数据集标注
dataset = load_unlabeled_data()
batch_annotate(dataset,
    batch_size=5000,
    annotation_schema=schema,
    quality_threshold=0.95
)
# GPU利用率：60% → 95%