Dask, Tensorflow serving (and Kubernetes and Streamz)
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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 at 15:45
Khaj
143113
add a comment |
up vote
3
down vote
favorite
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 at 15:45
Khaj
143113
So you would want to serve your models in Tensorflow but not persist them?
– Rico
Nov 15 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 at 11:25
add a comment |
up vote
3
down vote
favorite
up vote
3
down vote
favorite
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 at 15:45
Khaj
143113
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 at 15:45
Khaj
143113
asked Nov 14 at 15:45
Khaj
143113
edited Nov 15 at 11:25
asked Nov 14 at 15:45
Khaj
143113
asked Nov 14 at 15:45
Khaj
143113
asked Nov 14 at 15:45
Khaj
143113
143113
So you would want to serve your models in Tensorflow but not persist them?
– Rico
Nov 15 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 at 11:25
add a comment |
So you would want to serve your models in Tensorflow but not persist them?
– Rico
Nov 15 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 at 11:25
So you would want to serve your models in Tensorflow but not persist them?
– Rico
Nov 15 at 4:35
So you would want to serve your models in Tensorflow but not persist them?
– Rico
Nov 15 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 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 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 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 at 11:25