我必须将 Google Cloud ML Engine 的数据集转换为 TFRecords 吗？
Must I convert dataset to TFRecords for Google Cloud ML Engine?
我可以在 Google Cloud ML Engine 中训练我的模型而不将数据集转换为 TFRecords 吗？
是的，根据 docs：
Cloud ML Engine doesn't get involved in your data format; you can use
whatever input format is convenient for your training application.
That said, you'll need to have your input data in a format that
TensorFlow can read. You also need to have your data in a location
that your Cloud ML Engine project can access. The simplest solution is
often to use a CSV file in a Google Cloud Storage bucket that your
Google Cloud project has access to. Some types of data, sparse vectors
and binary data for example, can be better represented using
TensorFlow's tf.train.Example format serialized in a TFRecords file.
我可以在 Google Cloud ML Engine 中训练我的模型而不将数据集转换为 TFRecords 吗？
是的，根据 docs：
Cloud ML Engine doesn't get involved in your data format; you can use whatever input format is convenient for your training application. That said, you'll need to have your input data in a format that TensorFlow can read. You also need to have your data in a location that your Cloud ML Engine project can access. The simplest solution is often to use a CSV file in a Google Cloud Storage bucket that your Google Cloud project has access to. Some types of data, sparse vectors and binary data for example, can be better represented using TensorFlow's tf.train.Example format serialized in a TFRecords file.