# Preprocessing training data with autoencoders before training a ML model

I have a 300D training data set and I want to use autoencoders to reduce the dimensionality before running a machine learning model on this data set.

In the classical dimensionality reduction technique PCA it is recommended that PCA is run only on the training set, and testing/validation sets are "predicted" with it.

How is the procedure for autoencoders? For example, I want to reduce the dimension of my training data from 300D to 10D, which means 10 neurons in the hidden layer of a neural network (autoencoders). My question is, should I run autoencoders on the training/validation/testing sets separately?