K80 vs V100

Monday 16 September 2019

Discovering how much cheaper spot EC2 instances were than normal on-demand instances gave me the courage to try out a faster GPU. I had been using K80s which are painfully slow, but very cheap. The spot price for the V100 is about the same as the on-demand price of the K80s, so using those with spot instances won't be any cheaper, but it won't be more expensive either.

I didn't think the V100s were such great GPUs, so I wasn't expecting it to be worth the extra cost. How wrong I was. Training the network I am currently playing with on a K80 with a batch size of 48 took about 8-12 hours per epoch. Training it on a V100 with a batch size of 64 is looking like it's going to take about 2 hours. With the V100s priced at about 4x the K80s, that works out to about the same price per compute to a little bit cheaper, depending on exactly how long it took per epoch on the K80.

When you factor in the value of not having to wait an entire day to see the results of an epoch, this is a no-brainer as far as I'm concerned. Unfortunately, I'm sure my AWS bill is going to increase substantially. That's how they get you... Once you have a taste of HPC they know you'll be back for more...

Labels: machine_learning, ec2, aws, gpu
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AWS EC2 Spot Instances

Thursday 12 September 2019

My major complaint about using EC2 GPU instances was the cost, it gets very expensive to run a GPU instance for more than a few hours. Last week I was wondering why I wasn't using spot instances, so I set up a request and I've been running it for a few days now. It is about 1/4 the price of a normal instance, so it's not much more expensive than renting a CPU-only on-demand instance. I was hoping to get a better GPU than the K80, but I ended up settling for the K80 because it was more available than the better GPUs, but next time I may request a better one and see what happens.

The downside of spot instances is that they will be terminated if the capacity is needed for an on-demand instance, and my instance was terminated the other night. But then I spun up a new one in the morning and that one has been running for a few days now. I can't believe I haven't used these before.

Labels: machine_learning, aws
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Amazon EC2 Deep Learning AMI Instance

Tuesday 27 August 2019

It is difficult to play around with the structure for the GAN I am working on in Colab since it trains so slowly. I can usually get maybe 2 or 3 epochs in a day, which means that I need to wait a day before evaluating each change I make. I decided to rent a GPU in the cloud for a few days so I could train it a bit more quickly and figure out what works and what doesn't work before going back to Colab.

I already have a Google Cloud GPU instance I was using for my work with mammography, but it was running CUDA 9.0 which apparently is not supported by PyTorch out of the box. I tried to upgrade CUDA to 10, but I think I ended up just making things worse. Rather than spend a whole day trying to fix the GCS instance, and since I have some AWS credits, I decided to try to use an AWS Deep Learning AMI instance, which already has everything configured.

It was incredibly easy to get set up, it comes pre-configured with virtual environments for different deep learning frameworks and packages, so there is no need to install CUDA or drivers or anything like that, which is a huge advantage, since back when I was setting up the GCS instance it took me a few days to get everything installed and working. One thing I quickly noticed was that the default disk size was not even close to big enough - after downloading a few data files I was already running out of disk space, but it was very easy to increase the disk size.

Then all I had to do was activate the pytorch environment, launch a notebook and everything was running smoothly. I did run into a few minor issues, none of which were difficult to resolve:

  • If I launch tmux from within a virtual environment it launches a session that does NOT have the environment activated. Then if I activate the environment from within tmux it doesn't have access to the proper modules. This was resolved by launching tmux from outside of the venv, and then activating the venv from inside tmux.
  • In my notebook it didn't seem to have access to pytorch, but this was because I hadn't selected the proper kernel from the kernel -> change kernel menu. I wasn't even aware that one could select the kernel like that.

I used to prefer GCS to AWS because it was more configurable and easier to use. While AWS does have a bit of a learning curve, they really have thought of and provided for just about every possible contingency. We use AWS at my work, and it really is very impressive. I still like the simplicity of GCS, but even simple things like AMIs make such a huge difference in set-up time that I think I'll be using AWS more often now.

Labels: machine_learning, aws, pytorch
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I had been trying to train my autoencoder with a GAN component on and off for a couple of months and it just didn't seem to be working very well. I thought that maybe the autoencoder and the discriminator errors were somehow cancelling each other out or something. Just for the hell of it I decided to try to use the discriminator to optimize a reconstructed image to look real, just to see what the result would be. Instead of optimizing the weights, I created a Variable of the input and optimized that instead. To my surprise I ended up with weird splotches of primary colors against a white background, it actually made the image look less and less real rather than more. After seeing that I decided that there must be some major problem with my code so I went through it in greater detail.

I decided to train all three networks from scratch (the three being the encoder, the decoder and the discriminator) to see what would happen. I was surprised to see that the generator did not seem to be learning ANYTHING and neither did the discriminator. I found a tutorial on creating a GAN in PyTorch and I went through the training code to see how it differed from mine. 

I had written my code to optimize it for speed, training the autoencoder without the GAN already took about 4 hours per epoch on a (free) K80 on Colab so I didn't want to slow that down much more, so I tried to minimize the numebr of times data had to be passed through the networks. The tutorial did not do that. First it ran a batch of real data through the discriminator, computed the gradients but did NOT back propagate them. Then it used the generator to generate a batch of faked data, passed that through the discriminator, computed the gradients, added them to the gradients from the first batch and THEN did the back prop. Then it ran the same batch of faked data through the discriminator again, and used that to update the generator. This was different from my code in several major ways:

  1. I was using a single batch containing half real images and half reconstructed images to train my discriminator.
  2. I was training passing data through each network one single time per batch.
  3. I wasn't detaching the reconstructed data before passing them through the discriminator.

After updating my code to bring it more in line with the tutorial both networks began to learn, I think that major change was detaching the reconstructed images before putting them through the discriminator. However I noticed a few strange things regarding the discriminator batches:

  • If I used a single batch containing both real and constructed images to train the discriminator it learned very quickly, it's loss approached 0 very quickly, and the discriminator loss component of the generator overwhelmed the autoencoder loss, which sort of fluctuated but didn't decrease very much.
  • If I trained using two batches, each containing only images for a single label, it's accuracy hovered around 50% and the autoencoder loss decreased rapidly.

I read in a couple of places that using separate batches was a trick to make GANs train better, but no one really had an explanation for why this worked. What I am currently doing it using separate batches most of the time, before every n batches I use a single batch to encourage the discriminator to learn a bit more. I've tested values for n of 8, 16, 32 and 64. Most of those seemed to result in the worst of both worlds, nothing really seemed to improve, but with n = 64 the autoencoder loss is again decreasing, although slowly, and the discriminator accuracy is hovering around 52% rather than the 49-50% it was at using all separate batches.

To me using separate batches doesn't intuitively make sense, I don't see how the network can really learn to differentiate between classes when it only sees one class at a time. Of course the gradients are then added, and the differences should cancel out, with what's left indicating how to differentiate the classes; but to me it seems much more efficient to learn from mixed batches. One would never consider training a network on, say, the CIFAR dataset with each batch consisting exclusively of a single class. Maybe that's the point, to slow down the discriminator's learning enough for the generator to keep up? Anyway I will continue to experiment and see what works and what doesn't work.

 

 

 

 

Labels: machine_learning, pytorch, autoencoders, gan
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I am still working on my face autoencoder in my spare time, although I have much less spare time lately. My non-variational autoencoder works great - it can very accurately reconstruct any face in my dataset of 400,000 faces, but it doesn't work at all for interpolation or anything like that. So I have also been trying to train a variational autoencoder, but it has a lot more difficulty learning.

For a face which is roughly centered and looking in the general direction of the camera it can do a somewhat decent job, but if the picture is off in any way - there is another face off to the side, there is something blocking the face, the face is at a strange angle, etc it does a pretty bad job. And since I want to try to use this for interpolation training it on these bad faces doesn't really help anything.

One of the biggest datasets I am using is this one from ETHZ. The dataset was created to train a network to predict the age of the person, and while the images are all of good quality it does include many images that have some of the issues I mentioned above, as well as pictures that are not faces at all - like drawings or cartoons. Other datasets I am using consist entirely of properly cropped faces as I described above, but this dataset is almost 200k images, so omitting it completely significantly reduces the size of my training data.

The other day I decided I needed to improve the quality of my training dataset if I ever want to get this variational autoencoder properly trained, and to do that I need to filter out the bad images from the ETHZ IMDB dataset. They had already created the dataset using face detectors, but I want to remove faces that have certain attributes:

  • Multiple faces or parts of faces in the image
  • Images with something blocking part of the face
  • Images where the faces are not generally facing forward, such as profiles

I started trying to curate them manually, but after going through 500 images of the 200k I realized that would not be feasible. It would be easy to train a neural network to classify the faces, but that would require training data, but that still means manually classifying the faces. So, what I did is I took another dataset of faces that were all good and added about 700 bad faces from the IMDB dataset for a total size of about 7000 images and made a new dataset. Then I took a pre-trained discriminator I had previously used as part of a GAN to try to generate faces and retrained it to classify the faces as good or bad. 

I ran this for about 10 epochs, until it was achieving very good accuracy, and then I used it to evaluate the IMDB dataset. Any image which it gave a less than 0.03 probability of being good I moved into the bad training dataset, and any images which it gave a 0.99 probability of being good I moved to the good training dataset. Then I continued training it and so on and so on.

This is called weak supervision or semi-supervised learning, and it works a lot better than I thought it would. After training for a few hours, the images which are moved all seem to be correctly classified, and after each iteration the size of the training dataset grows to allow the network to continue learning. Since I only move images which have very high or very low probabilities, the risk of a misclassification should be relatively low, and I expect to be able to completely sort the IMDB dataset by the end of tomorrow, maybe even sooner. What would have taken weeks or longer to do manually has been reduced to days thanks to transfer learning and weak supervision!

Labels: coding, data_science, machine_learning, pytorch, autoencoders
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