Page tree
Skip to end of metadata
Go to start of metadata



기반 문서 : https://medium.com/google-cloud/hello-world-on-gcp-ml-engine-cc09f506361c



ML 프로젝트 템플릿


$ tree .
.
├── setup.py
└── trainer
    ├── __init__.py
    ├── model.py : 학습 모델
    ├── task.py : job을 관리하기 위한 로직
    └── input.py : 데이터 제공


실습

기반 자료 : https://medium.com/google-cloud/hello-world-on-gcp-ml-engine-cc09f506361c

코드 : https://github.com/jgensler8/gcp-minimal-ml-engine-project


GCP shell 준비

google에 로그인

https://console.cloud.google.com/ 열기


우측 상단의 'Activate Cloud Shell' 클릭


프로젝트 다운로드

$ git clone https://github.com/jgensler8/gcp-minimal-ml-engine-project
Cloning into 'gcp-minimal-ml-engine-project'...
remote: Enumerating objects: 12, done.
remote: Total 12 (delta 0), reused 0 (delta 0), pack-reused 12
Unpacking objects: 100% (12/12), done.


$ cd gcp-minimal-ml-engine-project/



코드 수정

trainer/task.py

# print "job_dir: {}".format(ARGS.job_dir)
print("job_dir: {}".format(ARGS.job_dir))


setup.py

#REQUIRED_PACKAGES = ['tensorflow>=1.8.0']
REQUIRED_PACKAGES = ['tensorflow==1.15.0']


trainer/task.py

#import model
#import input
#import tuil
import trainer.model as model
import trainer.input as input
import trainer.util as util



환경 설치와 필요 라이브러리 설치

$ cd gcp-minimal-ml-engine-project/
$ make install


local에서 학습

$ make train_local



bucket 만들기

GCP  console에서 만듬.

본 문서에서는 'tftraining1234"


Makefile 파일 수정

#BUCKET_NAME=tftraining
BUCKET_NAME=tftraining1234


GCP에서 학습


$ make train_job


상태 확인

state: PREPARING
trainingInput:
  args:
  - --train-files
  - ./train
  - --eval-files
  - ./eval
  jobDir: gs://tftraining1234/tftraining1234_2  packageUris:
  - gs://tftraining1234/tftraining1234_2/packages/46ee892af3a209f2b7819ee630faf7e8a8a76f3e9c1bed5216c1d1d6073bd835/hello-world-0.1.tar.gz
  pythonModule: trainer.task
  region: us-central1
  runtimeVersion: '1.5'
trainingOutput: {}
View job in the Cloud Console at:
https://console.cloud.google.com/mlengine/jobs/tftraining1234_2?project=slipp-study-256111
View logs at:
https://console.cloud.google.com/logs?resource=ml.googleapis.com%2Fjob_id%2Ftftraining1234_2&project=slipp-study-256111




프로젝트 파일 상세


from : https://medium.com/google-cloud/hello-world-on-gcp-ml-engine-cc09f506361c


프로젝트 구조

$ tree .
.
├── Makefile : make의 설정 파일
├── eval : 평가 데이터
├── output
├── setup.py : python 프로젝트 셋업 설정
├── train : 학습 데이터
└── trainer
    ├── __init__.py
    ├── model.py : 모델
    ├── task.py : GCP ML의 작업 로직
    └── input.py : 데이터 제공 로직


Makefile

VIRTUALENV_DIR=./env
PIP=${VIRTUALENV_DIR}/bin/pip
ACTIVATE=source ${VIRTUALENV_DIR}/bin/activate

# Python + Environment

virtualenv:
	virtualenv ${VIRTUALENV_DIR}

install: virtualenv
	${PIP} install -e .
# TensorFlow

MODEL_DIR=./output
TRAIN_DATA=./train
EVAL_DATA=./eval

TRAINER_PACKAGE=trainer
TRAINER_MAIN=${TRAINER_PACKAGE}.task

train_local:
	bash -c '${ACTIVATE} && gcloud ml-engine local train \
    --module-name ${TRAINER_MAIN} \
    --package-path ${TRAINER_PACKAGE} \
    --job-dir ${MODEL_DIR} \
    -- \
    --train-files ${TRAIN_DATA} \
    --eval-files ${EVAL_DATA}'

# --train-steps 1000 \
# --eval-steps 100'

#BUCKET_NAME=tftraining
BUCKET_NAME=tftraining1234

upload_train_eval_data:
	echo "would upload training data"
	echo gsutil cp ${TRAIN_DATA} gs://${BUCKET_NAME}/train
	echo gsutil cp ${EVAL_DATA} gs://${BUCKET_NAME}/eval
	
# JOB_NAME=${BUCKET_NAME}_$(shell date +%s)
JOB_NAME=${BUCKET_NAME}_2
BUCKET_JOB_DIR=gs://${BUCKET_NAME}/${JOB_NAME}
REGION=us-central1
RUNTIME_VERSION=1.5

train_job:
	gcloud ml-engine jobs submit training ${JOB_NAME} \
    --job-dir ${BUCKET_JOB_DIR} \
    --runtime-version ${RUNTIME_VERSION} \
    --module-name ${TRAINER_MAIN} \
    --package-path ${TRAINER_PACKAGE} \
    --region ${REGION} \
    -- \
    --train-files ${TRAIN_DATA} \
    --eval-files ${EVAL_DATA}

MODEL_NAME=helloworld_model

create_model:
	gcloud ml-engine models create ${MODEL_NAME} --regions=${REGION}

MODEL_BINARIES=gs://${BUCKET_NAME}/${JOB_NAME}/export/estimator/1529119938

MODEL_VERSION=v1

create_model_version:
	gcloud ml-engine versions create ${MODEL_VERSION} \
	--model ${MODEL_NAME} \
	--origin ${MODEL_BINARIES} \
	--runtime-version ${RUNTIME_VERSION}

JSON_INSTANCES=./json_instances.jsonl

test_model_version:
	gcloud ml-engine predict \
	  --model ${MODEL_NAME} \
	  --version ${MODEL_VERSION} \
	  --json-instances ${JSON_INSTANCES}


input.py

import tensorflow as tf

def train_input_fn():
    return tf.constant([ [[1],[1]], [[1], [2]] ]), [1]
    
def eval_input_fn():
    return [2, 3], [1]

2개의 함수 train_input_fn()과 eval_input_fn()을 구현한다.

어디서 데이터를 읽고 오는 지는 무관하다.


model.py

import tensorflow as tf
from tensorflow.python.framework import ops

def cnn_model_fn(features, labels, mode):


  # features = Tensor("Const:0", shape=(2, 2, 1), dtype=int32, device=/device:CPU:0)
  # lables = Tensor("Const_2:0", shape=(1,), dtype=int32)
  # mode = "train"

  input_layer = tf.constant([[1.0]])
  labels = tf.constant([0])
  dense = tf.layers.dense(inputs=input_layer, units=1, activation=tf.nn.relu)
  dropout = tf.layers.dropout(...)
  logits = tf.layers.dense(inputs=dropout, units=2)

  predictions = {
      "classes": tf.argmax(input=logits, axis=1),
      "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
  }
  

  loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
  optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
  train_op = optimizer.minimize(
        loss=loss,
        global_step=tf.train.get_global_step())

  return tf.estimator.EstimatorSpec(mode=mode, loss=loss, predictions=predictions, train_op=train_op, ...)
  })


def estimator(config):
    return tf.estimator.Estimator(model_fn=cnn_model_fn, config=config)


estimator()를 구현하여야 하고 tf.estimator.Estimator()를 반환한다.

def estimator(config):
    return tf.estimator.Estimator(model_fn=cnn_model_fn, config=config)



task.py

def main():
    ARGS = args_parser.parse_args()
    config = util.config(ARGS.job_dir)
    
	# config = tf.estimator.RunConfig(
	#        tf_random_seed=19830610,
	#        log_step_count_steps=1000,
	#        save_checkpoints_secs=120,  # change if you want to change frequency of saving checkpoints
	#        keep_checkpoint_max=3,
	#        model_dir=job_dir
	#    )
    estimator = model.estimator(config)
    
    train_spec = tf.estimator.TrainSpec(
        input.train_input_fn,
        max_steps=100
    )

    exporter = tf.estimator.FinalExporter(
        'estimator',
        input.json_serving_function,
        as_text=False  # change to true if you want to export the model as readable text
    )

    eval_spec = tf.estimator.EvalSpec(
        input.eval_input_fn,
        exporters=[exporter],
        name='estimator-eval',
        steps=100
    )
    
    tf.estimator.train_and_evaluate(
        estimator,
        train_spec,
        eval_spec
    )
    
args_parser = argparse.ArgumentParser()
args_parser.add_argument(
    '--job-dir',
    help='GCS location to write checkpoints and export models',
    required=True
)
args_parser.add_argument(
    '--train-files',
    help='GCS or local paths to training data',
    nargs='+',
    required=True
)
args_parser.add_argument(
    '--eval-files',
    help='GCS or local paths to evaluation data',
    nargs='+',
    required=True
)

if __name__ == '__main__':
    main()


task.py는 tf.estimator.train_and_evalute()호출이 핵심이다.

  tf.estimator.train_and_evaluate(
        estimator, # 학습 중의 기타 설정들. 모델 저장 위치, 저장 주기, 저장 최대 수, ...
        train_spec, # 학습 관련 설정들. 데이터 제공 함수, 최대 학습 수, ...
        eval_spec # 검증 관련 설정들. 데이터 제공 함수, ...
    )



  • No labels