Dask, Tensorflow serving (and Kubernetes and Streamz)
What is the current 'state of technology' when having a pipeline composed of python code and Tensorflow/Keras models?
We are trying to have scalability and reactive design using dask and Streamz (for servers registered using Kubernetes).
But currently, we do not know what is the right way to design such infrastructure concerning the fact, that we do want our Tensorflow models to persist and not to be repeatedly created and deleted.
Is Tensorflow serving the technology to be used for this task?
(I was able to find only the basic examples like Persistent dataflows with dask and http://matthewrocklin.com/blog/work/2017/02/11/dask-tensorflow)
tensorflow kubernetes cluster-computing dask
asked Nov 14 '18 at 15:45
KhajKhaj
244213
add a comment |
What is the current 'state of technology' when having a pipeline composed of python code and Tensorflow/Keras models?
We are trying to have scalability and reactive design using dask and Streamz (for servers registered using Kubernetes).
But currently, we do not know what is the right way to design such infrastructure concerning the fact, that we do want our Tensorflow models to persist and not to be repeatedly created and deleted.
Is Tensorflow serving the technology to be used for this task?
(I was able to find only the basic examples like Persistent dataflows with dask and http://matthewrocklin.com/blog/work/2017/02/11/dask-tensorflow)
tensorflow kubernetes cluster-computing dask
asked Nov 14 '18 at 15:45
KhajKhaj
244213
So you would want to serve your models in Tensorflow but not persist them?
– Rico
Nov 15 '18 at 4:35
Sorry, mistake in the formulation. I would like to serve my models such that they do persist and are not recreated again and again. :) (I have done an edit and hope it is more clear now. Is it?)
– Khaj
Nov 15 '18 at 11:25
add a comment |
What is the current 'state of technology' when having a pipeline composed of python code and Tensorflow/Keras models?
We are trying to have scalability and reactive design using dask and Streamz (for servers registered using Kubernetes).
But currently, we do not know what is the right way to design such infrastructure concerning the fact, that we do want our Tensorflow models to persist and not to be repeatedly created and deleted.
Is Tensorflow serving the technology to be used for this task?
(I was able to find only the basic examples like Persistent dataflows with dask and http://matthewrocklin.com/blog/work/2017/02/11/dask-tensorflow)
tensorflow kubernetes cluster-computing dask
asked Nov 14 '18 at 15:45
KhajKhaj
244213
What is the current 'state of technology' when having a pipeline composed of python code and Tensorflow/Keras models?
We are trying to have scalability and reactive design using dask and Streamz (for servers registered using Kubernetes).
But currently, we do not know what is the right way to design such infrastructure concerning the fact, that we do want our Tensorflow models to persist and not to be repeatedly created and deleted.
Is Tensorflow serving the technology to be used for this task?
(I was able to find only the basic examples like Persistent dataflows with dask and http://matthewrocklin.com/blog/work/2017/02/11/dask-tensorflow)
tensorflow kubernetes cluster-computing dask
tensorflow kubernetes cluster-computing dask
asked Nov 14 '18 at 15:45
KhajKhaj
244213
asked Nov 14 '18 at 15:45
KhajKhaj
244213
edited Nov 15 '18 at 11:25
Khaj
asked Nov 14 '18 at 15:45
KhajKhaj
244213
asked Nov 14 '18 at 15:45
KhajKhaj
244213
asked Nov 14 '18 at 15:45
KhajKhaj
244213
244213
So you would want to serve your models in Tensorflow but not persist them?
– Rico
Nov 15 '18 at 4:35
Sorry, mistake in the formulation. I would like to serve my models such that they do persist and are not recreated again and again. :) (I have done an edit and hope it is more clear now. Is it?)
– Khaj
Nov 15 '18 at 11:25
add a comment |
So you would want to serve your models in Tensorflow but not persist them?
– Rico
Nov 15 '18 at 4:35
Sorry, mistake in the formulation. I would like to serve my models such that they do persist and are not recreated again and again. :) (I have done an edit and hope it is more clear now. Is it?)
– Khaj
Nov 15 '18 at 11:25
So you would want to serve your models in Tensorflow but not persist them?
– Rico
Nov 15 '18 at 4:35
So you would want to serve your models in Tensorflow but not persist them?
– Rico
Nov 15 '18 at 4:35
Sorry, mistake in the formulation. I would like to serve my models such that they do persist and are not recreated again and again. :) (I have done an edit and hope it is more clear now. Is it?)
– Khaj
Nov 15 '18 at 11:25
Sorry, mistake in the formulation. I would like to serve my models such that they do persist and are not recreated again and again. :) (I have done an edit and hope it is more clear now. Is it?)
– Khaj
Nov 15 '18 at 11:25
add a comment |
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So you would want to serve your models in Tensorflow but not persist them?
– Rico
Nov 15 '18 at 4:35
Sorry, mistake in the formulation. I would like to serve my models such that they do persist and are not recreated again and again. :) (I have done an edit and hope it is more clear now. Is it?)
– Khaj
Nov 15 '18 at 11:25