I have spent a lot of time lately training neural networks. However, my poor laptop has only an AMD GPU, so I am stuck training these networks on the CPU. That means I get to train networks over multiple days only to learn that I made a mistake in my loss function.

I don't have enough money to build a new computer or buy an eGPU, so it seems that my best solution is to train these networks on a cloud provider instead.

There are a lot of options nowadays for these cloud GPUS - AWS, Paperspace, Floydhub, etc.

The good news is that MMA neural networks use MXNET under the hood, and you are able to export any network as MXNET files - Export["mynet.json", network] appears to work quite well (though I haven't tried to install MXNET to see if they'll actually work correctly).

Has anybody trained these MMA-defined MXNET neural networks on these cloud providers? If so, what are your recommendations? I am specifically looking for:

  • ease of use - ideally, export->upload->train->download->use in MMA, with limited hassle - I feel that the data is going to be an annoying step here. I'm currently training on 4000 image-mask pairs, a few hundred MB worth of data. Getting some python written to actually train the network is also not going to be very fun.
  • pricing - it seems to be that FloydHub is the best option in terms of price?
  • does anyone know if the Wolfram Cloud is going to support GPU training anytime soon and I can save myself this hassle?
  • $\begingroup$ Why not just install WL on an AWS/Paperspace/etc machine and use the NN framework to train directly? (what does exporting to MXNet get you?) $\endgroup$ – Sebastian Sep 3 '18 at 14:38
  • $\begingroup$ I don't think my license allows me to do that, but maybe I'm wrong, I haven't read it in detail. But this would definitely be the easiest solution otherwise... $\endgroup$ – Carl Lange Sep 3 '18 at 18:30
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    $\begingroup$ @CarlLange Please let us know if you succeeded $\endgroup$ – gogoolplex Sep 19 '18 at 12:57
  • $\begingroup$ @gogoolplex I haven't tried yet to be honest. It seems that using a remote kernel on an AWS server is the easiest option but I haven't had the money to spend to do that. I would dearly love to be able to do Device->"Cloud" and not have to deal with any of this, but then transferring datasets etc would be hassle. $\endgroup$ – Carl Lange Sep 19 '18 at 14:19
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    $\begingroup$ @CarlLange Ok. I know that Google gives you like 300USD for free in the first year and I guess AWS has a similar offer. So lets hope that 12 brings an easy solution to use whatever cloud you want easily. $\endgroup$ – gogoolplex Sep 19 '18 at 17:19

No need to export to MXNet, put your neural net on a wl script instead and run it on AWS. Install CDFPlayer for linux on a Deep-Learning Ubuntu AMI on a EC2 GPU-compute instance, then download and install manually the drivers and run your training script with TargetDevice->"GPU"

Remember to screen your session to avoid interruption. Check GPU usage with nvidia-smi -l 1

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  • $\begingroup$ That's really helpful. I didn't realise CDFPlayer would be usable for training my network. I'll try this out over the next few weeks and approve your answer then; in theory looks good! $\endgroup$ – Carl Lange Dec 10 '18 at 15:07
  • $\begingroup$ @CarlLange Yes, the Mathkernel that gets installed with the CDF seems able to do the same things the Mathematica one does; it might be the very same. $\endgroup$ – Fortsaint Dec 10 '18 at 17:40
  • $\begingroup$ I never actually got around to testing this, but the new Wolfram Engine seems suitable for this as well. $\endgroup$ – Carl Lange May 20 '19 at 17:21
  • $\begingroup$ @CarlLange Yes I confirm. What I haven't tested yet is the possibility to have a working Wolfram Engine installation on different system simultaneously, something you could definitively have with the previous CDFPlayer kernel. $\endgroup$ – Fortsaint May 21 '19 at 11:17

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