如何在Google Cloud Machine Learning Engine上培训Keras模型 [英] How to train Keras model on Google Cloud Machine Learning Engine

问题描述

我可以在google云机器学习引擎上训练tensorflow模型。但是，当我使用Keras代码时，我在google cloud上得到错误没有名为keras 的模块。

解决方案

我发现为了在谷歌云上使用keras，必须使用setup.py脚本安装它，并将它放在运行gcloud命令的相同位置文件夹中：

 ├──setup.py 
└──教练
├──__init__.py 
├──cloudml -gpu.yaml 
├──example5-keras.py 
  

并在设置中。 py你把内容，如：

  from setuptools import setup，find_packages 
 
 setup（name =' example5'，
 version ='0.1'，
 packages = find_packages（），
 description ='在gcloud ml-engine上运行keras的例子'，
 author ='Fuyang Liu '，
 author_email='[e-mail]'，
 license ='MIT'，
 install_requires = [
'keras'，
'h5py' 
]，
 zip _safe = False）
  

然后，您可以开始在gcloud上运行作业，例如：

  export BUCKET_NAME = tf-learn-simple-sentiment 
 export JOB_NAME =example_5_train _ $（date +％Y％m％d_％H ％M％S）
 export JOB_DIR = gs：// $ BUCKET_NAME / $ JOB_NAME 
 export REGION = europe-west1 
 
 gcloud ml-engine作业提交培训$ JOB_NAME \\ \\ 
 --job-dir gs：// $ BUCKET_NAME / $ JOB_NAME \ 
 --runtime-version 1.0 \ 
 --module-name trainer.example5-keras \ 
  - 软件包路径./trainer \ 
 --region $ REGION \ 
 --config = trainer / cloudml-gpu.yaml \ 
  -  \ 
 --train-file gs：//tf-learn-simple-sentiment/sentiment_set.pickle 
  

要使用GPU，然后在模块中添加一个文件，例如 cloudml-gpu.yaml ，其中包含以下内容：

  trainingInput：
 scaleTier：CUSTOM 
＃standard_gpu提供1个GPU。更改为complex_model_m_gpu 4 
 GPU 
 masterType：standard_gpu 
 runtimeVersion：1.0
  

I can train tensorflow models on google cloud machine learning engine. But when I use Keras code, I get error No module named keras on google cloud.

解决方案

I found out that in order to use keras on google cloud one has to install it with a setup.py script and put it on the same place folder where you run the gcloud command:

├── setup.py
└── trainer
    ├── __init__.py
    ├── cloudml-gpu.yaml
    ├── example5-keras.py

And in the setup.py you put content such as:

from setuptools import setup, find_packages

setup(name='example5',
  version='0.1',
  packages=find_packages(),
  description='example to run keras on gcloud ml-engine',
  author='Fuyang Liu',
  author_email='[e-mail]',
  license='MIT',
  install_requires=[
      'keras',
      'h5py'
  ],
  zip_safe=False)

Then you can start your job running on gcloud such as:

export BUCKET_NAME=tf-learn-simple-sentiment
export JOB_NAME="example_5_train_$(date +%Y%m%d_%H%M%S)"
export JOB_DIR=gs://$BUCKET_NAME/$JOB_NAME
export REGION=europe-west1

gcloud ml-engine jobs submit training $JOB_NAME \
  --job-dir gs://$BUCKET_NAME/$JOB_NAME \
  --runtime-version 1.0 \
  --module-name trainer.example5-keras \
  --package-path ./trainer \
  --region $REGION \
  --config=trainer/cloudml-gpu.yaml \
  -- \
  --train-file gs://tf-learn-simple-sentiment/sentiment_set.pickle

To use GPU then add a file such as cloudml-gpu.yaml in your module with the following content:

trainingInput:
  scaleTier: CUSTOM
  # standard_gpu provides 1 GPU. Change to complex_model_m_gpu for 4 
GPUs
  masterType: standard_gpu
  runtimeVersion: "1.0"

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