What is the difference between tensorflow serving Dockerfile and Dockerfile.devel?
Why are there two different docker files for tensorflow serving - Dockerfile & Dockerfile.devel - for both CPU and GPUs?
Which one is necessary for deploying and testing?
python docker tensorflow dockerfile tensorflow-serving
asked Nov 14 '18 at 6:38
Ashiq KSAshiq KS
328
add a comment |
Why are there two different docker files for tensorflow serving - Dockerfile & Dockerfile.devel - for both CPU and GPUs?
Which one is necessary for deploying and testing?
python docker tensorflow dockerfile tensorflow-serving
asked Nov 14 '18 at 6:38
Ashiq KSAshiq KS
328
add a comment |
Why are there two different docker files for tensorflow serving - Dockerfile & Dockerfile.devel - for both CPU and GPUs?
Which one is necessary for deploying and testing?
python docker tensorflow dockerfile tensorflow-serving
asked Nov 14 '18 at 6:38
Ashiq KSAshiq KS
328
Why are there two different docker files for tensorflow serving - Dockerfile & Dockerfile.devel - for both CPU and GPUs?
Which one is necessary for deploying and testing?
python docker tensorflow dockerfile tensorflow-serving
python docker tensorflow dockerfile tensorflow-serving
asked Nov 14 '18 at 6:38
Ashiq KSAshiq KS
328
asked Nov 14 '18 at 6:38
Ashiq KSAshiq KS
328
edited Nov 14 '18 at 6:48
Ashiq KS
asked Nov 14 '18 at 6:38
Ashiq KSAshiq KS
328
asked Nov 14 '18 at 6:38
Ashiq KSAshiq KS
328
asked Nov 14 '18 at 6:38
Ashiq KSAshiq KS
328
328
add a comment |
add a comment |
1 Answer
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a Dockerfile is a file where your write the configurations to create a docker image.
The tensorflow/serving cpu and gpu are docker images which means they are already configured to work with tensorflow, tensorflow_model_server and, in the case of gpu, with CUDA.
If you have a GPU, then you can use a tensorflow/serving gpu version which would reduce the latency of your predictions. If you don't have a GPU, then you can use a tensorflow/serving cpu version which would do exactly the same but will be slower.
answered Nov 14 '18 at 20:14
Rodrigo LozaRodrigo Loza
586
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
a Dockerfile is a file where your write the configurations to create a docker image.
The tensorflow/serving cpu and gpu are docker images which means they are already configured to work with tensorflow, tensorflow_model_server and, in the case of gpu, with CUDA.
If you have a GPU, then you can use a tensorflow/serving gpu version which would reduce the latency of your predictions. If you don't have a GPU, then you can use a tensorflow/serving cpu version which would do exactly the same but will be slower.
answered Nov 14 '18 at 20:14
Rodrigo LozaRodrigo Loza
586
add a comment |
a Dockerfile is a file where your write the configurations to create a docker image.
The tensorflow/serving cpu and gpu are docker images which means they are already configured to work with tensorflow, tensorflow_model_server and, in the case of gpu, with CUDA.
If you have a GPU, then you can use a tensorflow/serving gpu version which would reduce the latency of your predictions. If you don't have a GPU, then you can use a tensorflow/serving cpu version which would do exactly the same but will be slower.
answered Nov 14 '18 at 20:14
Rodrigo LozaRodrigo Loza
586
add a comment |
a Dockerfile is a file where your write the configurations to create a docker image.
The tensorflow/serving cpu and gpu are docker images which means they are already configured to work with tensorflow, tensorflow_model_server and, in the case of gpu, with CUDA.
If you have a GPU, then you can use a tensorflow/serving gpu version which would reduce the latency of your predictions. If you don't have a GPU, then you can use a tensorflow/serving cpu version which would do exactly the same but will be slower.
answered Nov 14 '18 at 20:14
Rodrigo LozaRodrigo Loza
586
a Dockerfile is a file where your write the configurations to create a docker image.
The tensorflow/serving cpu and gpu are docker images which means they are already configured to work with tensorflow, tensorflow_model_server and, in the case of gpu, with CUDA.
If you have a GPU, then you can use a tensorflow/serving gpu version which would reduce the latency of your predictions. If you don't have a GPU, then you can use a tensorflow/serving cpu version which would do exactly the same but will be slower.
answered Nov 14 '18 at 20:14
Rodrigo LozaRodrigo Loza
586
answered Nov 14 '18 at 20:14
Rodrigo LozaRodrigo Loza
586
answered Nov 14 '18 at 20:14
Rodrigo LozaRodrigo Loza
586
answered Nov 14 '18 at 20:14
Rodrigo LozaRodrigo Loza
586
586
add a comment |
add a comment |
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