как развернуть пользовательскую модель в Amazon SageMaker - PullRequest
Новая
       6

как развернуть пользовательскую модель в Amazon SageMaker

0 голосов
/ 16 апреля 2020

Я новичок ie в AWS sagemaker, я пытаюсь развернуть пользовательскую модель lstm временного ряда в sagemaker, пожалуйста, помогите мне и как подготовить режим сценария. это мой файл скрипта timer_series.py код. 

import sagemaker
import boto3
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow 
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator

from sklearn.metrics import mean_squared_error


if __name__ =='__main__':

    parser.add_argument('--epochs', type=int, default=50)
    parser.add_argument('--batch_size', type=int, default=72)
    parser.add_argument('--n_train_hours', type=int, default=24*365*2)
    parser.add_argument('--n_validation_hours', type=int, default=24*365*4)

    # input data and model directories
    parser.add_argument('--model_dir', type=str)

    args, _ = parser.parse_known_args()

    train_dataset_dir = os.environ.get('SM_INPUT_DIR') + '/data/training/' 
    output_model_dir = os.environ.get('SM_MODEL_DIR')
    output_object_dir = os.environ.get('SM_OUTPUT_DATA_DIR')

    epochs = args.epochs
    batch_size = args.batch_size
    input_data = {args.input_data}
    dataset = read_csv( train_dataset_dir + 'dataset.csv', header=0, index_col='Date')
    dataset.sort_index(inplace=True)
    train = dataset.iloc[:109]
    test= dataset.iloc[109:]  
    scaler = MinMaxScaler()
    scaled_train = scaler.fit_transform(train)
    scaled_test=scaler.fit_transform(test)
    n_input = 12
    n_feature = 1

    train_generator = TimeseriesGenerator(scaled_train,scaled_train,length=n_input, batch_size=1)

    model = Sequential()

    model.add(LSTM(128,activation = 'relu', input_shape= (n_input, n_feature), return_sequences=True))
    model.add(LSTM(128, activation='relu', return_sequences=True))
    model.add(LSTM(128, activation='relu', return_sequences=False))
    model.add(Dense(1))
    model.compile(optimizer='adam', loss='mse')
    history =model.fit_generator(train_generator,epochs=50, batch_size=1,verbose=1)

# Get a SageMaker-compatible role used by this Notebook Instance.
    role = get_execution_role()
    with open(output_model_dir + '/history.json', 'w') as f:
         json.dump(history.history, f)
    #Save the Scaler
    dump(scaler, output_model_dir + '/scaler.model', protocol=2) 
    #Save the trained model and weights
    model_json = model.to_json()
    with open(output_model_dir + "/model.json", "w") as json_file:
        json_file.write(model_json)
    model.save_weights(output_model_dir + "/model.h5")

здесь показывается какая-то ошибка: 

 train_instance_type = "ml.m4.xlarg"

tf_estimator = TensorFlow(entry_point='time_series.py', role=get_execution_role(),
                          train_instance_count=1, train_instance_type=train_instance_type,
                          framework_version='1.12', py_version='py3', script_mode=True,
                          output_path = 's3://' + s3Bucket, base_job_name = "sales-forecasting-lstm",
                         hyperparameters={'batch_size': 2,
                                           'epochs': 50})

tf_estimator.fit(uploaded_data_path)

Здесь я получил ошибку. что это за ошибка, я не понял эту ошибку помогите мне и как подготовить сценарий режима python сценарий для развертывания. это мой файл сценария timer_series.py код. 

import sagemaker
import boto3
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow 
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator

from sklearn.metrics import mean_squared_error


if __name__ =='__main__':

    parser.add_argument('--epochs', type=int, default=50)
    parser.add_argument('--batch_size', type=int, default=72)
    parser.add_argument('--n_train_hours', type=int, default=24*365*2)
    parser.add_argument('--n_validation_hours', type=int, default=24*365*4)

    # input data and model directories
    parser.add_argument('--model_dir', type=str)

    args, _ = parser.parse_known_args()

    train_dataset_dir = os.environ.get('SM_INPUT_DIR') + '/data/training/' 
    output_model_dir = os.environ.get('SM_MODEL_DIR')
    output_object_dir = os.environ.get('SM_OUTPUT_DATA_DIR')

    epochs = args.epochs
    batch_size = args.batch_size
    input_data = {args.input_data}
    dataset = read_csv( input_data + 'dataset.csv', header=0, index_col='Date')
    dataset.sort_index(inplace=True)
    train = dataset.iloc[:109]
    test= dataset.iloc[109:]  
    scaler = MinMaxScaler()
    scaled_train = scaler.fit_transform(train)
    scaled_test=scaler.fit_transform(test)
    n_input = 12
    n_feature = 1

    train_generator = TimeseriesGenerator(scaled_train,scaled_train,length=n_input, batch_size=1)

    model = Sequential()

    model.add(LSTM(128,activation = 'relu', input_shape= (n_input, n_feature), return_sequences=True))
    model.add(LSTM(128, activation='relu', return_sequences=True))
    model.add(LSTM(128, activation='relu', return_sequences=False))
    model.add(Dense(1))
    model.compile(optimizer='adam', loss='mse')
    history =model.fit_generator(train_generator,epochs=50, batch_size=1,verbose=1)

# Get a SageMaker-compatible role used by this Notebook Instance.
    role = get_execution_role()
    with open(output_model_dir + '/history.json', 'w') as f:
         json.dump(history.history, f)
    #Save the Scaler
    dump(scaler, output_model_dir + '/scaler.model', protocol=2) 
    #Save the trained model and weights
    model_json = model.to_json()
    with open(output_model_dir + "/model.json", "w") as json_file:
        json_file.write(model_json)
    model.save_weights(output_model_dir + "/model.h5")

здесь это показывает некоторую ошибку: 

 train_instance_type = "ml.m4.xlarg"

tf_estimator = TensorFlow(entry_point='time_series.py', role=get_execution_role(),
                          train_instance_count=1, train_instance_type=train_instance_type,
                          framework_version='1.12', py_version='py3', script_mode=True,
                          output_path = 's3://' + s3Bucket, base_job_name = "sales-forecasting-lstm",
                         hyperparameters={'batch_size': 2,
                                           'epochs': 50})

tf_estimator.fit(uploaded_data_path)

Здесь я получил ошибку. что это за ошибка, я не понял эту ошибку. 

UnexpectedStatusException: Error for Training job sales-forecasting-lstm-2020-04-13-10-17-34-919: Failed. Reason: AlgorithmError: ExecuteUserScriptError:
Command "/usr/bin/python time_series.py --batch_size 2 --epochs 50 --model_dir s3://sagemaker12/sales-forecasting-lstm-2020-04-13-10-17-34-919/model"

​
...