Date: November 2-5, 2017
Shaheed Sukhdev College of Business Studies,
PSP Area IV, Dr. K.N. Katju Marg,
Sector 16, Rohini, New Delhi, Delhi – 110089
Lately, there has been a lot of interest in Deep Learning(dl) and thanks to frameworks like Tensorflow anyone can implement dl-papers and create models. But unfortunately, the deployment patterns followed are mostly rudimentary REST calls to the model or using tensorflow-serving, which is fine when you are experimenting but when the model gets deployed and the requests start flying, such methods will create a bottleneck in your Architecture. There are obvious workarounds like running multiple model instances behind a load balancer, but what if there is a much better Pythonic way.
Actor and CSP patterns have been around since the 70s(73 and 78 respectively) but only a niche group has taken a keen look at them and since the introduction of asyncio from Python3.5 onwards, the Python ecosystem has been opened up to these patterns in some limited but useful forms. This talk will show these patterns and how they can be used to deploy Deep Learning models in the right way, (the reference to Deep Learning alone has been intentional and relates to the batching in tensorflow).
A basic idea of asyncio coroutines and if possible streams.
The talk would not cover
– Microservices, are good but you cannot have 1000’s if not tens of thousands unique Microservices created on the fly, connected uniquely for each user.
– Deep Learning algorithms, as there are plenty of resources for the same, we are only looking at the model deployment perspective i.e. inference time optimization.
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