Mixing
Can i distribute training and inference DNN architecture over cloud and edge devices?
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
edited Jan 3 at 0:29
feliks
1,015216
asked Jan 2 at 11:51
ienxienx
32
add a comment |
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
edited Jan 3 at 0:29
feliks
1,015216
asked Jan 2 at 11:51
ienxienx
32
add a comment |
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
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
edited Jan 3 at 0:29
feliks
1,015216
asked Jan 2 at 11:51
ienxienx
32
edited Jan 3 at 0:29
feliks
1,015216
asked Jan 2 at 11:51
ienxienx
32
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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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.
answered Jan 2 at 13:24
feliksfeliks
1,015216
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
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.
answered Jan 2 at 13:24
feliksfeliks
1,015216
add a comment |
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.
answered Jan 2 at 13:24
feliksfeliks
1,015216
add a comment |
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.
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.
answered Jan 2 at 13:24
feliksfeliks
1,015216
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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