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Playing with water
======
![H2o Flow gradient boosting job][1]
I'm currently taking a machine learning class and although it is an insane amount of work, I like it a lot. I initially had planned to use [R][2] to play around with the database I have, but the teacher recommended I use [H2o][3], a FOSS machine learning framework.
I was a bit sceptical at first since I'm already pretty good with R, but then I found out you could simply import H2o as an R library. H2o replaces most R functions by its own parallelized ones to cut down on processing time (no more `doParallel` calls) and uses an "external" server you have to run on the side instead of running R calls directly.
![H2o Flow gradient boosting model][4]
I was pretty happy with this situation, that is until I actually started using H2o in R. With the huge database I'm playing with, the library felt clunky and I had a hard time doing anything useful. Most of the time, I just ended up with long Java traceback calls. Much love.
I'm sure in the right hands using H2o as a library could have been incredibly powerful, but sadly it seems I haven't earned my black belt in R-fu yet.
![H2o Flow variable importance weights][5]
I was pissed for at least a whole day - not being able to achieve what I wanted to do - until I realised H2o comes with a WebUI called Flow. I'm normally not very fond of using web thingies to do important work like writing code, but Flow is simply incredible.
Automated graphing functions, integrated ETA when running resource intensive models, descriptions for each and every model parameters (the parameters are even divided in sections based on your familiarly with the statistical models in question), Flow seemingly has it all. In no time I was able to run 3 basic machine learning models and get actual interpretable results.
So yeah, if you've been itching to analyse very large databases using state of the art machine learning models, I would recommend using H2o. Try Flow at first instead of the Python or R hooks to see what it's capable of doing.
The only downside to all of this is that H2o is written in Java and depends on Java 1.7 to run... That, and be warned: it requires a metric fuckton of processing power and RAM. My poor server struggled quite a bit even with 10 available cores and 10Gb of RAM...
--------------------------------------------------------------------------------
via: https://veronneau.org/playing-with-water.html
作者：[Louis-Philippe Véronneau][a]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]:https://veronneau.org/
[1]:https://veronneau.org/media/blog/2018-03-14/h2o_job.png (H2o Flow gradient boosting job)
[2]:https://en.wikipedia.org/wiki/R_(programming_language)
[3]:https://www.h2o.ai
[4]:https://veronneau.org/media/blog/2018-03-14/h2o_model.png (H2o Flow gradient boosting model)
[5]:https://veronneau.org/media/blog/2018-03-14/h2o_var_importance.png (H2o Flow variable importance weights)

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尝试 H2o
======
![H2o Flow gradient boosting job][1]
我目前正在参加一个机器学习班，虽然有疯狂的工作量，但我非常喜欢。我最初计划使用 [R][2] 来训练我的数据库，但老师建议我使用一个 FOSS 机器学习框架 [H2o][3]。
起初我有点怀疑，因为我已经对 R 掌握得不错了，但后来我发现你可以简单地将 H2o 作为 R 库导入。H2o 将大多数 R 函数替换为其自己的并行化函数，以减少处理时间（不再需要 `doParallel` 调用），并且使用“外部”服务端来运行，而不是直接调用 R。
![H2o Flow gradient boosting model][4]
直到我真正在实际中开始在 H2o 中使用 R 时，我对这种情况都非常满意。我在使用非常大得数据库时，库变得笨重，我几乎不能做任何有用得事情。大多数时候，我最后只是得到一个很长的 Java 回溯调用。
我相信正确地将 H2o 作为一个库使用将非常强大，但可惜的是，它似乎在我的 R-fu 中无效。
![H2o Flow variable importance weights][5]
我至少有一整天都很生气 - 无法实现我想做的事 - 直到我意识到 H2o 有一个名为 Flow 的 WebUI。我通常不喜欢使用 web 来完成重要的工作，比如编写代码，但是 Flow 简直太不可思议了。
自动绘图功能，运行资源密集模型时集成 ETA（预计剩余时间），每个模型参数的描述（这些参数甚至会根据您熟悉的统计模型分成不同部分），Flow 似乎拥有所有功能。我很快就能够运行 3 种基本的机器学习模型并获得实际可解释的结果。
所以，如果你一直渴望使用最先进的机器学习模型分析非常大的数据库，我会推荐使用 H2o。首先尝试使用 Flow，而不是 Python 或 R 的钩子，来看看它能做什么。
唯一缺点是，H2o 是用 Java 编写的，并依赖 Java 1.7 来运行。并且需要警告：它需要非常强大的处理器和大量的内存。即使有 10 个可用的内核和 10Gb 的 RAM，我可怜的服务器也苦苦挣扎了一段时间。
--------------------------------------------------------------------------------
via: https://veronneau.org/playing-with-water.html
作者：[Louis-Philippe Véronneau][a]
译者：[geekpi](https://github.com/geekpi)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]:https://veronneau.org/
[1]:https://veronneau.org/media/blog/2018-03-14/h2o_job.png (H2o Flow gradient boosting job)
[2]:https://en.wikipedia.org/wiki/R_(programming_language)
[3]:https://www.h2o.ai
[4]:https://veronneau.org/media/blog/2018-03-14/h2o_model.png (H2o Flow gradient boosting model)
[5]:https://veronneau.org/media/blog/2018-03-14/h2o_var_importance.png (H2o Flow variable importance weights)