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training-system-planning

Estimate GPU memory, training step time, checkpoint costs, and recovery overhead for training or reinforcement learning. Compare state partitioning, recomputation, offloading, and device counts to assess whether a job can finish within its deadline.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

估算训练或 RL 作业的显存、步时、checkpoint、故障重做和完成期限。用于比较分片、重计算、卸载与集群规模。

查看安装方法

适用场景

  • Check whether training can finish by a deadline
  • Compare memory-saving methods and their time costs
  • Plan checkpoint intervals and recovery overhead
  • Estimate usable sample throughput for reinforcement learning

包含的文件(1)

  • SKILL.md1.4 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

训练系统规划

适用:需要证明某训练目标能否在给定设备和期限内完成。依据 manuscripts/10-训练系统.md 第 10.1—10.6 节。

  1. 明确目标有效样本量、模型与优化器状态格式、序列长度、batch、设备数、故障率和截止时间;区分样本生成吞吐与有效样本吞吐。
  2. 逐项计算参数、梯度、优化器状态、激活、通信与临时缓冲的峰值显存。ZeRO、重计算和卸载都以额外通信或计算换容量,需同时检查步时。
  3. 沿一个训练步的依赖图累计前向、反向、归约、更新和未被覆盖的通信;最慢设备决定同步步时。检查 batch 扩大是否改变优化目标或收敛效率。
  4. 把数据准备、checkpoint 写入、故障概率与预期重做加入长期完成时间。checkpoint 保存参数/优化器状态,也保存数据进度;保存间隔需平衡写入与丢失工作。
  5. 对 RL 额外跟踪 rollout 样本、策略版本、行为概率、验证结果和训推数值一致性,避免把生成总量当作可训练样本数。

输出显存峰值、步时分解、有效样本吞吐、恢复开销和期限余量。硬件可行与训练质量可达应分别说明。

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