Hi Theodore,

If your dataset can not be imported with normal method (.csv with values/filenames), then we support a catchall mechanism by taking your dataset in numpy format.

You can first convert your dataset in the numpy format and save each row data in a numpy file (using below code). Create train.csv with one column with numpy filenames and other column for output data.

e.g.

input_data, output_data

a.npz, label1

b.npz, label2

…

Following is a sample code of how we save numpy data and load it in DLS.

```
import numpy as np
a = np.zeros((2, 1))
np.savez_compressed("a.npz",a)
npzfile = np.load("a.npz")
x = npzfile[npzfile.files[0]]
```

Does this work for your use case ?