Oct. 9, 2018, midnight
By : Eric A. Scuccimarra
The other day I was having problems with a CoLab notebook and I was trying to debug it when I noticed that TPU is now an option for runtime type. I found no references to this in the CoLab documentation, but apparently it was quietly introduced only recently. If anyone doesn't know, TPUs are chips designed by Google specifically for matrix multiplications and are supposedly incredibly fast. Last I checked the cost to rent one through GCP was about $6 per hour, so the ability to have access to one for free could be a huge benefit.
As TPUs are specialized chips you can't just run the same code as on a CPU or a GPU. TPUs do not support all TensorFlow operations and you need to create a special optimizer to be able to take advantage of the TPU at all. The model I was working with at the time was created using TensorFlow's Keras API so I decided to try to convert that to be TPU compatible in order to test it.
Normally you would have to use a cross shard optimizer, but there is a shortcut for Keras models:
TPU_WORKER = 'grpc://' + os.environ['COLAB_TPU_ADDR']
# create network and compiler
tpu_model = tf.contrib.tpu.keras_to_tpu_model(
keras_model, strategy = tf.contrib.tpu.TPUDistributionStrategy(
The first line finds an available TPU and gets it's address. The second line takes your keras model as input and converts it to a TPU compatible model. Then you would train the model using tpu_model.fit() instead of keras_model. This was the easy part.
For this particular model I am using a lot of custom functions for loss and metrics. Many of the functions turned out to not be compatible with TPUs so had to be rewritten. While at the time this was annoying, it turned out to be worth it regardless of the TPU because I had to optimize the functions in order to make them compatible with TPUs. The specific operations which were not compatible were non-matrix ops - logical operations and boolean masks specifically. Some of the code was downright hideous and this forced me to sit down and think through it and re-write it in a much cleaner manner, vectorizing as much as possible.
After all that effort, so far my experience with the TPUs hasn't been all that great. I can train my model with a significantly larger batch size - whereas on an Nvidia K80 16 was the maximum batch size, I am currently training with batches of 64 on the TPU and may be able to push that even higher. However the time per epoch hasn't really improved all that much - it is about 1750 seconds on the TPU versus 1850 seconds on the K80. I have read code may need to be altered more to take full advantage of TPUs and I have not really tried playing with the batch size to see how that changes the performance yet.
I suspect that if I did some more research about TPUs and coded the model to be optimized for a TPU from scratch there might be a more noticeable performance gain, but this is based solely on having heard other people talk about how fast they are and not from my experience.
Update - I have realized that the data augmentation is the bottleneck which is limiting the speed of training. I am training with a Keras generator which performs the augmentation on the CPU and if this is removed or reduced the TPUs do, in fact, train significantly faster than a GPU and also yield better results.
Labels: coding , machine_learning , google_cloud
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