Comments (3)
@HuihuiTong 能不能看下完整的代码?以我们内部的经验,按你这种方式将 evaluate 改成异步,是可以加速一倍的
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@HuihuiTong 能不能看下完整的代码?以我们内部的经验,按你这种方式将 evaluate 改成异步,是可以加速一倍的
主要改动,就是参考我们DI-engine的文档:https://di-engine-docs.readthedocs.io/zh_CN/latest/distributed/index_zh.html#id4
beseline是:https://github.com/opendilab/GoBigger-Challenge-2021/tree/main/di_baseline
修改的方式,跟DI-engine文档只中一致,其他没有改动。
我整理一下,将两份对比代码发出来。
还有个问题,想顺便问一下,其他几个关于DI-engine框架疑惑点:
- 对于训练规模与分布式。我想提升环境数量以及训练规模,比如从baseline中的8个,提升到100个,但是在多个节点上分布式训练(比如10个节点运行env,一个节点运行learner训练),这样基于现有DI-engine需要怎么修改?是否有demo可参考?
- 关于训练中参数max_iterations,我设置为2000,训练依然训练十几个小时,还没有结束。我不太理解这个参数控制的范围。
- 有没有关于DI-engine框架,使用交流的微信或答疑群。
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@HuihuiTong 文档没跟上,可以见最新的变更,https://github.com/opendilab/DI-engine-docs/blob/ae8604be47a248c420e33a2bdb1bbe188d335669/source/distributed/index_zh.rst
对于这几个问题我解释一下:
- 你可以改用 subprocessenvmanager,然后将配置中的 collect_env_num 改成 100 个,但是我比较怀疑这样的效果,因为更多的进程会增加更多的 cpu 和内存,还有采集到 buffer 的 IO 开销。你可以用 step timer 打印出来的时间看一下 learn 和 collect 每一步的时间,如果发现 collect 比 learn 快,那就没必要增加环境数量,而应该增加 learn 的效率。
- max_iterations 决定了总训练循环数,跟第一个问题一样,你得观察一下每个循环的时间,如果一个循环的时间需要几秒,那么确实有可能 2000 个循环也要好几个小时
- 要获得比较快的回答,目前还只能用 github issue,社区和群得花太多时间运营,目前资源还不太够。
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