We have recently set up a TensorFlow assessment function in AWS lambda, and got very close to the maximum allowed size of a lambda function (250MB) with the trained model currently being 85MB, and the TensorFlow libraries and binaries taking up another 140 or so megabytes by default (<- which we cracked down a bit, but that's quite a hack).
I feel like Amazon could do some work in this area to support users to use their own engines and not be bound to AWS AI Platforms and Services.
This could be as simple as publicly documenting the time lambda's stay 'warm' for and retain data in /tmp persisting through multiple invocations or some other examples on how an AI workflow could be implemented with popular custom engines such as TensorFlow.
Does anybody else have any experience in this regard?
I feel like Amazon could do some work in this area to support users to use their own engines and not be bound to AWS AI Platforms and Services.
This could be as simple as publicly documenting the time lambda's stay 'warm' for and retain data in /tmp persisting through multiple invocations or some other examples on how an AI workflow could be implemented with popular custom engines such as TensorFlow.
Does anybody else have any experience in this regard?