Requirements of imported files

Save your csv files using the UTF-8 encoding.
Files saved with different encoding might work on the platform, but special characters may cause incompatibilities.

Use less than 50000 characters in a single feature.
This is particularly true if you upload a text feature, where the text could be arbitrarily long. Consider that no model could handle such long features anyway.

Be aware that the platform supports up to 2000 unique categories per feature, but datasets with more categories will not run properly.

Try to limit your datasets to less than 10 millions rows. You will most likely hit very long training times, and it is rare that a model needs so many examples to train itself.

The resolution of the images, i.e., the width and height in pixels, is controlled on platform by the Transformation option.

The amount of channels, i.e., grayscale or color with transparency or not, is controlled by the Color mode option.

Google Cloud Storage URLs start with gs://

Amazon S3 URLs start with s3://

The platform only supports 32-bit floating point numbers, because this is the standard format on GPUs and it makes calculations run faster.
If you work with Python and NumPy, it is likely that the default format is, instead, 64-bit floating point numbers. Make sure that you convert your values to 32-bit floats when you save your arrays to npy files:

import numpy as np
A = np.array([1.0, 1.1, 1.2, 1.3, 1.4, 1.5])
np.save('/my/folder/data.npy', A.astype('float32'))

The NumPy arrays must also be saved using little-endian byte order and C-ordering. These are likely correct by default, but if you get errors about byte order of Fortran order during upload, check byte swapping and the ascontiguousarray() function from NumPy.

Visualisation of npy files depends on the number of dimensions. Find out more in known issues.