Can i distribute training and inference DNN architecture over cloud and edge devices?

-1

I'm doing research about distributed DNN, from what I got, we can distribute DNN computation over many GPUs and also we can do on our mobile devices. Inference architectures are usually single platform, so either exist on mobile or in the cloud.

My question is:

Can we distribute training and inference phase in DNN architecture in a joint platform (in both cloud and mobile)? if it possible, how to do that?

tensorflow

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edited Jan 3 at 0:29

feliks

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asked Jan 2 at 11:51

ienxienx

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add a comment | 

-1

I'm doing research about distributed DNN, from what I got, we can distribute DNN computation over many GPUs and also we can do on our mobile devices. Inference architectures are usually single platform, so either exist on mobile or in the cloud.

My question is:

Can we distribute training and inference phase in DNN architecture in a joint platform (in both cloud and mobile)? if it possible, how to do that?

tensorflow

share|improve this question

edited Jan 3 at 0:29

feliks

1,015216

asked Jan 2 at 11:51

ienxienx

32

add a comment | 

-1

-1

-1

I'm doing research about distributed DNN, from what I got, we can distribute DNN computation over many GPUs and also we can do on our mobile devices. Inference architectures are usually single platform, so either exist on mobile or in the cloud.

My question is:

Can we distribute training and inference phase in DNN architecture in a joint platform (in both cloud and mobile)? if it possible, how to do that?

tensorflow

share|improve this question

edited Jan 3 at 0:29

feliks

1,015216

asked Jan 2 at 11:51

ienxienx

32

I'm doing research about distributed DNN, from what I got, we can distribute DNN computation over many GPUs and also we can do on our mobile devices. Inference architectures are usually single platform, so either exist on mobile or in the cloud.

My question is:

Can we distribute training and inference phase in DNN architecture in a joint platform (in both cloud and mobile)? if it possible, how to do that?

tensorflow

tensorflow

share|improve this question

edited Jan 3 at 0:29

feliks

1,015216

asked Jan 2 at 11:51

ienxienx

32

share|improve this question

edited Jan 3 at 0:29

feliks

1,015216

asked Jan 2 at 11:51

ienxienx

32

share|improve this question

share|improve this question

edited Jan 3 at 0:29

feliks

1,015216

edited Jan 3 at 0:29

feliks

1,015216

edited Jan 3 at 0:29

feliks

1,015216

1,015216

asked Jan 2 at 11:51

ienxienx

32

asked Jan 2 at 11:51

ienxienx

32

asked Jan 2 at 11:51

ienxienx

32

32

add a comment | 

add a comment | 

1 Answer
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-1

There's a plethora of options to choose from, depending on your framework. Horovod is mostly framework agnostic and can be used for distributed training. It also satisfies your need to use cloud services. Although it is entirely possible to create an own framework using Distributed Tensorflow, you should be aware that this is a more low-level approach than Horovod and therefore is missing some bells and whistles.

Distributed inference on the other hand is not as common, since inference itself does not require as much computational power as training, and is embarassingly parallelizable most of the time.

share|improve this answer

edited Jan 2 at 13:32

answered Jan 2 at 13:24

feliksfeliks

1,015216

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-1

There's a plethora of options to choose from, depending on your framework. Horovod is mostly framework agnostic and can be used for distributed training. It also satisfies your need to use cloud services. Although it is entirely possible to create an own framework using Distributed Tensorflow, you should be aware that this is a more low-level approach than Horovod and therefore is missing some bells and whistles.

Distributed inference on the other hand is not as common, since inference itself does not require as much computational power as training, and is embarassingly parallelizable most of the time.

share|improve this answer

edited Jan 2 at 13:32

answered Jan 2 at 13:24

feliksfeliks

1,015216

add a comment | 

-1

There's a plethora of options to choose from, depending on your framework. Horovod is mostly framework agnostic and can be used for distributed training. It also satisfies your need to use cloud services. Although it is entirely possible to create an own framework using Distributed Tensorflow, you should be aware that this is a more low-level approach than Horovod and therefore is missing some bells and whistles.

Distributed inference on the other hand is not as common, since inference itself does not require as much computational power as training, and is embarassingly parallelizable most of the time.

share|improve this answer

edited Jan 2 at 13:32

answered Jan 2 at 13:24

feliksfeliks

1,015216

add a comment | 

-1

-1

-1

There's a plethora of options to choose from, depending on your framework. Horovod is mostly framework agnostic and can be used for distributed training. It also satisfies your need to use cloud services. Although it is entirely possible to create an own framework using Distributed Tensorflow, you should be aware that this is a more low-level approach than Horovod and therefore is missing some bells and whistles.

Distributed inference on the other hand is not as common, since inference itself does not require as much computational power as training, and is embarassingly parallelizable most of the time.

share|improve this answer

edited Jan 2 at 13:32

answered Jan 2 at 13:24

feliksfeliks

1,015216

There's a plethora of options to choose from, depending on your framework. Horovod is mostly framework agnostic and can be used for distributed training. It also satisfies your need to use cloud services. Although it is entirely possible to create an own framework using Distributed Tensorflow, you should be aware that this is a more low-level approach than Horovod and therefore is missing some bells and whistles.

Distributed inference on the other hand is not as common, since inference itself does not require as much computational power as training, and is embarassingly parallelizable most of the time.

share|improve this answer

edited Jan 2 at 13:32

answered Jan 2 at 13:24

feliksfeliks

1,015216

share|improve this answer

share|improve this answer

edited Jan 2 at 13:32

edited Jan 2 at 13:32

edited Jan 2 at 13:32

answered Jan 2 at 13:24

feliksfeliks

1,015216

answered Jan 2 at 13:24

feliksfeliks

1,015216

answered Jan 2 at 13:24

feliksfeliks

1,015216

1,015216

add a comment | 

add a comment | 

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