无法在MLEngineTrainingOperator中指定master_type [英] unable to specify master_type in MLEngineTrainingOperator

问题描述

我正在使用气流来调度管道，这将导致使用ai平台训练scikitlearn模型.我用这个DAG训练它

I am using airflow to schedule a pipeline that will result in training a scikitlearn model with ai platform. I use this DAG to train it

    with models.DAG(JOB_NAME,
                    schedule_interval=None,
                    default_args=default_args) as dag:

        # Tasks definition
        training_op = MLEngineTrainingOperator(
            task_id='submit_job_for_training',
            project_id=PROJECT,
            job_id=job_id,
            package_uris=[os.path.join(TRAINER_BIN)],
            training_python_module=TRAINER_MODULE,
            runtime_version=RUNTIME_VERSION,
            region='europe-west1',
            training_args=[
                '--base-dir={}'.format(BASE_DIR),
                '--event-date=20200212',
            ],
            python_version='3.5')
        training_op

培训包将加载所需的csv文件，并在其上训练RandomForestClassifier.

The training package loads the desired csv files and train a RandomForestClassifier on it.

这很好，直到文件的数量和大小增加为止.然后我得到这个错误:

This works fine until the number and the size of the files increase. Then I get this error:

ERROR - The replica master 0 ran out-of-memory and exited with a non-zero status of 9(SIGKILL). To find out more about why your job exited please check the logs:

文件的总大小约为4 Gb.我不知道使用的默认计算机是什么，但似乎还不够.希望这可以解决内存消耗问题，我尝试将分类器的参数n_jobs从-1更改为1，没有更多的运气.

The total size of the files is around 4 Gb. I dont know what is the default machine used but is seems not enough. Hoping this would solve the memory consumption issue I tried to change the parameter n_jobs of the classifier from -1 to 1, with no more luck.

看看MLEngineTrainingOperator的代码和文档，我添加了一个自定义的scale_tier和一个master_type n1-highmem-8、8个CPU和52GB的RAM，像这样:

Looking at the code of MLEngineTrainingOperator and the documentation I added a custom scale_tier and a master_type n1-highmem-8, 8 CPUs and 52GB of RAM , like this:

with models.DAG(JOB_NAME,
                schedule_interval=None,
                default_args=default_args) as dag:

    # Tasks definition
    training_op = MLEngineTrainingOperator(
        task_id='submit_job_for_training',
        project_id=PROJECT,
        job_id=job_id,
        package_uris=[os.path.join(TRAINER_BIN)],
        training_python_module=TRAINER_MODULE,
        runtime_version=RUNTIME_VERSION,
        region='europe-west1',
        master_type="n1-highmem-8",
        scale_tier="custom",
        training_args=[
            '--base-dir={}'.format(BASE_DIR),
            '--event-date=20200116',
        ],
        python_version='3.5')
    training_op

这导致了另一个错误:

ERROR - <HttpError 400 when requesting https://ml.googleapis.com/v1/projects/MY_PROJECT/jobs?alt=json returned "Field: master_type Error: Master type must be specified for the CUSTOM scale tier.">

我不知道哪里出了问题，但看来这不是解决问题的方法.

I don't know what is wrong but it appears that is not the way to do that.

使用命令行，我设法启动该作业:

Using command line I manage to launch the job:

gcloud ai-platform jobs submit training training_job_name --packages=gs://path/to/package/package.tar.gz --python-version=3.5 --region=europe-west1 --runtime-version=1.14 --module-name=trainer.train --scale-tier=CUSTOM --master-machine-type=n1-highmem-16

但是我想在气流中做到这一点.

However i would like to do this in airflow.

任何帮助将不胜感激.

我的环境使用的是Apache Airflow的旧版本1.10.3，其中master_type参数不存在. 将版本更新到1.10.6解决了此问题

My environment used an old version of apache airflow, 1.10.3 where the master_type argument was not present. Updating the version to 1.10.6 solved this issue

推荐答案

我的环境使用的是Apache Airflow的旧版本1.10.3，其中master_type参数不存在.将版本更新到1.10.6解决了此问题

My environment used an old version of apache airflow, 1.10.3 where the master_type argument was not present. Updating the version to 1.10.6 solved this issue

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