原来是我的GCP配置有问题，这是我使其工作的步骤：

• 创建一个s3存储桶，并将其中的所有文件公开，以便训练作业可以访问它们

• 在要求fsspec和gcsfs中包括这两个

• 像这样从panda.readCsv删除'engine'参数

  数据集= pandas.read_csv（'gs：///USDJPY.fx5.csv'，usecols = [2，3，4，5]）

由于你是将python文件上传到GCP，因此是一种组织代码的好方法，可以将所有训练逻辑都放入一个方法中，然后在cloud train标志上有条件地调用它：

if tfc.remote():
    train()

如果有人感兴趣，这是完整的工作代码

import pandas
import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from sklearn.preprocessing import MinMaxScaler
import tensorflow_cloud as tfc
import os

os.environ["PATH"] = os.environ["PATH"] + ":<path to google-cloud-sdk/bin"
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "<path to google credentials json (you can generate this through their UI"


def create_dataset(data):
    dataX = data[0:len(data) - 1]
    dataY = data[1:]
    return numpy.array(dataX), numpy.array(dataY)

def train():
    dataset = pandas.read_csv('gs://<bucket>/USDJPY.fx5.csv', usecols=[2, 3, 4, 5])

    scaler = MinMaxScaler(feature_range=(-1, 1))
    scaler = scaler.fit(dataset)

    dataset = scaler.transform(dataset)

    # split into train and test sets
    train_size = int(len(dataset) * 0.67)
    train, test = dataset[0:train_size], dataset[train_size:len(dataset)]

    trainX, trainY = create_dataset(train)

    trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))

    model = Sequential()
    model.add(LSTM(4, input_shape=(1, 4)))
    model.add(Dropout(0.2))
    model.add(Dense(4))
    model.compile(loss='mean_squared_error', optimizer='adam')
    model.fit(trainX, trainY, epochs=1000, verbose=1)


job_labels = {"job": "forex-usdjpy", "team": "zver", "user": "zver1"}
tfc.run(requirements_txt="./requirements.txt",
        job_labels=job_labels,
        stream_logs=True
        )

if tfc.remote():
    train()

注意：这可能不是最佳的LSTM配置，请带一点盐