How can you train multiple neural networks simultaneously in nolearn/lasagne/theano on Python?
<p>I am writing a calibration pipeline to learn the hyperparameters for neural networks to detect properties of DNA sequences*. This therefore requires training a large number of models on the same dataset with different hyperparameters.</p>

<p>I am trying to optimise this to run on GPU. DNA sequence datasets are quite small compared to image datasets (typically 10s or 100s of base-pairs in 4 'channels' to represent the 4 DNA bases, A, C, G and T, compared to 10,000s of pixels in 3 RGB channels), and consequently cannot make full use of the parallelisation on a GPU unless multiple models are trained at the same time.</p>

<p>Is there a way to do this in nolearn, lasagne or, at worst, Theano?</p>

<p>* It's based on the <a href="http://www.nature.com/nbt/journal/v33/n8/full/nbt.3300.html" rel="nofollow">DeepBind model</a> for detecting where transcription factors bind to DNA, if you're interested.</p>