klingai-batch-processing

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Klingai Batch Processing

Klingai 批量处理

Overview

概述

This skill teaches efficient batch processing patterns for generating multiple videos, including parallel submission, progress tracking, rate limit management, and result collection.

本Skill介绍了生成多个视频的高效批量处理模式，包括并行提交、进度追踪、速率限制管理以及结果收集。

Prerequisites

前提条件

Kling AI API key with sufficient credits
Python 3.8+ with asyncio support
Understanding of async/await patterns

拥有足够额度的Kling AI API密钥
支持asyncio的Python 3.8+版本
了解async/await编程模式

Instructions

操作步骤

Follow these steps for batch processing:

Prepare Batch: Collect all prompts and parameters
Rate Limit Planning: Calculate submission pace
Parallel Submission: Submit jobs within limits
Track Progress: Monitor all jobs simultaneously
Collect Results: Gather outputs and handle failures

按照以下步骤进行批量处理：

准备批量任务：收集所有提示词和参数
规划速率限制：计算任务提交节奏
并行提交任务：在限制范围内提交作业
追踪进度：同时监控所有作业状态
收集结果：汇总输出内容并处理失败任务

Output

输出结果

Successful execution produces:

Parallel job submission within rate limits
Real-time progress tracking
Collected results with success/failure status
Performance metrics (duration, throughput)

执行成功后将生成：

符合速率限制的并行作业提交
实时进度追踪
包含成功/失败状态的结果汇总
性能指标（耗时、吞吐量）

Error Handling

错误处理

See

{baseDir}/references/errors.md

for comprehensive error handling.

完整的错误处理说明请查看

{baseDir}/references/errors.md

。

Examples

示例

See

{baseDir}/references/examples.md

for detailed examples.

详细示例请查看

{baseDir}/references/examples.md

。