我曾尝试谷歌云，你可以特定的路径，但我的步道期限到期，然后我使用谷歌驱动器，这是免费的。
你可以使用model.save或对称的方法，我把它们存储在一个数据库缓冲区，但结果和保存的它的权重可以保持在一个安全的驱动器不动。有很多功能的谷歌云和谷歌协作，但对于数据集存储，谷歌驱动器是足够的，除了过滤器。
示例：较长的终端距离可以保存提醒成本。

import io
import os
from os.path import exists

from apiclient import discovery
from httplib2 import Http
from oauth2client import client, file, tools
from googleapiclient.http import MediaIoBaseDownload

import tensorflow as tf
import tensorflow_io as tfio
import matplotlib.pyplot as plt

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
n_folder = 50
encoding = "utf-8"
    
# define path variables
credentials_file_path = 'F:\\temp\\Python\\credentials\\credentials.json'
clientsecret_file_path = 'F:\\temp\\Python\\credentials\\client_secret_183167298301-pfhgtdf6k8r4918csmftemgk00ln8l4r.apps.googleusercontent.com.json'

# define API scope
SCOPE = 'https://www.googleapis.com/auth/drive'

# define store
store = file.Storage(credentials_file_path)
credentials = store.get()
# get access token
if not credentials or credentials.invalid:
    flow = client.flow_from_clientsecrets(clientsecret_file_path, SCOPE)
    credentials = tools.run_flow(flow, store)
    
# define API service
http = credentials.authorize(Http())
drive = discovery.build('drive', 'v3', http=http)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Fuctions
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
def download_file( file_id, filename, filetype ): 
    print( 'downfile: ' + filename + ': ' + filetype )

    request = drive.files().get_media( fileId=file_id )
    file = io.BytesIO()
    downloader = MediaIoBaseDownload( file, request )
    done = False

    if filetype == "application/vnd.google-apps.folder":
        return

    try:
        while done is False:
            status, done = downloader.next_chunk()
            print( F'Download {int(status.progress() * 100)}.' )
        
    except HttpError as error:
        print(F'An error occurred: {error}')
        file = None
    
    tf.io.write_file(
        filename, file.getvalue(), name='write_file'
    )

    return 

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Write result to file
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
file = 'F:\\datasets\\downloads\\Actors\\train\\Pikaploy\\01.tif'
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
image = tf.image.resize(image, [8,8], method='nearest')

filename='F:\\temp\\datasets\\9.tif'

with open( filename, "wb" ) as f:
    b = bytes(str(image), encoding='utf-8')
    f.write(b)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Read result to file
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
temp = tf.io.read_file(
    filename, name='dataset_9'
)
temp = tf.io.decode_raw(
    temp, tf.uint8, little_endian=True, fixed_length=None, name=None
)
temp = tf.constant(temp, shape=(1, 8, 8, 3))

输出量：

tf.Tensor(
[[[133 141 126 255]
  [ 94 107  90 255]
  [106 125  97 255]
  [141 140 122 255]
  [ 96 114  90 255]
  [ 88 106  82 255]
  [112 141  93 255]
  [116 127 111 255]]

 ...

 [[150 122 111 255]
  [180 152 141 255]
  [192 160 145 255]
  [185 153 138 255]
  [168 148 139 255]
  [189 158 138 255]
  [166 136 110 255]
  [ 68  83  64 255]]], shape=(8, 8, 4), dtype=uint8)