Create a TensorFlow record dataset

Create a dataset from TensorFlow records (TFRecords). A TFRecord dataset can be used to train TensorFlow models.

Procedure

  1. From the cluster management console, select Workload > Spark > Deep Learning.
  2. Select the Datasets tab.
  3. Click New.
  4. Create a dataset from TensorFlow Records.
  5. Provide a dataset name.
  6. Specify a Spark instance group.
  7. Provide a training folder.
    The full absolute path to the training folder must be provided.
    The folder must contain an TFRecord file.
  8. Provide a validation folder.
    The full absolute path to the validation folder can be provided. To use this dataset for validation, you must specify a validation folder. Otherwise, this dataset cannot be used to validate a training model.
    The folder must contain an TFRecord file.
  9. Provide a testing folder.
    The full absolute path to the training folder must be provided.
    The folder must contain an TFRecord file.
  10. Provide a label file.
  11. Click Create.

Results

The dataset is created once it is in Created state. If creation failed, see the driver and executor logs in the Spark Applications tab.

What to do next

To view details about the dataset, click the dataset name. To use the dataset in a training run, either create a training model or start a training run.