python-3.x - H2O 服务器崩溃

Question

去年我一直在使用 H2O，我对服务器崩溃感到非常厌倦。我已经放弃了“每晚发布”，因为它们很容易被我的数据集破坏。请告诉我在哪里可以下载稳定的版本。

查尔斯

我的环境是:

Windows 10 企业版，内部版本 1607，具有 64 GB 内存。

Java SE 开发工具包 8 更新 77(64 位)。

python Python 3.6.2-0。

我启动了服务器:

localH2O = h2o.init(ip = "localhost",
                    port = 54321,
                    max_mem_size="12G",
                    nthreads = 4)

h2o 初始化信息是:

H2O cluster uptime:         12 hours 12 mins
H2O cluster version:        3.10.5.2
H2O cluster version age:    1 month and 6 days
H2O cluster name:           H2O_from_python_Charles_ji1ndk
H2O cluster total nodes:    1
H2O cluster free memory:    6.994 Gb
H2O cluster total cores:    8
H2O cluster allowed cores:  4
H2O cluster status:         locked, healthy
H2O connection url:         http://localhost:54321
H2O connection proxy:
H2O internal security:      False
Python version:             3.6.2 final

崩溃信息是:

OSError: Job with key $03017f00000132d4ffffffff$_a0ce9b2c855ea5cff1aa58d65c2a4e7c failed with an exception: java.lang.AssertionError: I am really confused about the heap usage; MEM_MAX=11453595648 heapUsedGC=11482667352
stacktrace: 
java.lang.AssertionError: I am really confused about the heap usage; MEM_MAX=11453595648 heapUsedGC=11482667352
    at water.MemoryManager.set_goals(MemoryManager.java:97)
    at water.MemoryManager.malloc(MemoryManager.java:265)
    at water.MemoryManager.malloc(MemoryManager.java:222)
    at water.MemoryManager.arrayCopyOfRange(MemoryManager.java:291)
    at water.AutoBuffer.expandByteBuffer(AutoBuffer.java:719)
    at water.AutoBuffer.putA4f(AutoBuffer.java:1355)
    at hex.deeplearning.Storage$DenseRowMatrix$Icer.write129(Storage$DenseRowMatrix$Icer.java)
    at hex.deeplearning.Storage$DenseRowMatrix$Icer.write(Storage$DenseRowMatrix$Icer.java)
    at water.Iced.write(Iced.java:61)
    at water.AutoBuffer.put(AutoBuffer.java:771)
    at water.AutoBuffer.putA(AutoBuffer.java:883)
    at hex.deeplearning.DeepLearningModelInfo$Icer.write128(DeepLearningModelInfo$Icer.java)
    at hex.deeplearning.DeepLearningModelInfo$Icer.write(DeepLearningModelInfo$Icer.java)
    at water.Iced.write(Iced.java:61)
    at water.AutoBuffer.put(AutoBuffer.java:771)
    at hex.deeplearning.DeepLearningModel$Icer.write105(DeepLearningModel$Icer.java)
    at hex.deeplearning.DeepLearningModel$Icer.write(DeepLearningModel$Icer.java)
    at water.Iced.write(Iced.java:61)
    at water.Iced.asBytes(Iced.java:42)
    at water.Value.<init>(Value.java:348)
    at water.TAtomic.atomic(TAtomic.java:22)
    at water.Atomic.compute2(Atomic.java:56)
    at water.Atomic.fork(Atomic.java:39)
    at water.Atomic.invoke(Atomic.java:31)
    at water.Lockable.unlock(Lockable.java:181)
    at water.Lockable.unlock(Lockable.java:176)
    at hex.deeplearning.DeepLearning$DeepLearningDriver.trainModel(DeepLearning.java:491)
    at hex.deeplearning.DeepLearning$DeepLearningDriver.buildModel(DeepLearning.java:311)
    at hex.deeplearning.DeepLearning$DeepLearningDriver.computeImpl(DeepLearning.java:216)
    at hex.ModelBuilder$Driver.compute2(ModelBuilder.java:173)
    at hex.deeplearning.DeepLearning$DeepLearningDriver.compute2(DeepLearning.java:209)
    at water.H2O$H2OCountedCompleter.compute(H2O.java:1349)
    at jsr166y.CountedCompleter.exec(CountedCompleter.java:468)
    at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)
    at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974)
    at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477)
    at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)

Answer 1

你需要一艘更大的船。

错误信息是“heapUsedGC=11482667352”，高于MEM_MAX。而不是给予 max_mem_size="12G"为什么不给它更多的 64GB 空间呢？或者构建一个不那么雄心勃勃的模型(更少的隐藏节点，更少的训练数据，诸如此类)。

(显然，理想情况下，h2o 不应该崩溃，而应该在接近使用所有可用内存时优雅地中止。如果您能够与 H2O 共享您的数据/代码，则可能值得打开一个错误报告在他们的 JIRA 上。)

顺便说一句，我一直在运行 h2o 3.10.x.x 作为 Web 服务器进程的后端 9 个月左右，在周末自动重新启动它，并且没有发生过一次崩溃。嗯，我做到了 - 在我让它运行 3 周之后，它用越来越多的数据和模型填满了所有内存。这就是为什么我将其切换为每周重新启动，并且只将我需要的模型保存在内存中。 (顺便说一下，这是在 AWS 实例上，4GB 内存；通过 cron 作业和 bash 命令重新启动。)