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hi, is there (or will there be) any support for training and doing stuff on gpu using ML.net too or no???
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@FIVIL
Great question. GPUs are excellent for accelerating DNN training and inference. Currently we do not offer DNN learners. At this point, we do not have GPU-accelerated components available. However, ML.NET is a general-purpose machine learning framework. It can support a variety of machine learning algorithms, including neural nets such as Cognitive Toolkit (CNTK). Adding support for leading DNN packages within ML.NET is on our roadmap. We will likely support GPU training and inference in the future for specific learners and transforms that can benefit from it.
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I agree, this should be priority. I have a GPU Crypto-mining farm and It would be nice to use that from time to time for machine learning computation aswell.
And I think serious data / applications would really want it, I think this should be first priority
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Any progress on adding GPU support? I agree with the sentiment that without GPU support, this is not a viable solution for serious ML applications. I wish there were a better .NET based framework for using CNTK which does support GPU. Seems natural the ML.NET would be exactly that, but wince there is no support for this currently, it's a big problem.
agreed. the recent resurgence of popularity in AI/ML/deep-learning is because today's GPUs are far more accessible than super-computing centers. if you build it, they will come.
@FIVIL
Great question. GPUs are excellent for accelerating DNN training and inference. Currently we do not offer DNN learners. At this point, we do not have GPU-accelerated components available. However, ML.NET is a general-purpose machine learning framework. It can support a variety of machine learning algorithms, including neural nets such as Cognitive Toolkit (CNTK). Adding support for leading DNN packages within ML.NET is on our roadmap. We will likely support GPU training and inference in the future for specific learners and transforms that can benefit from it.
People on this thread asking for GPU support, can you please specify the scenario where this might be beneficial for you? Please note adding DNN training support is on our roadmap in the coming months and that WILL have GPU support but I want to be sure if there is some other scenario that I might be missing that might benefit from GPUs?
People on this thread asking for GPU support, can you please specify the scenario where this might be beneficial for you? Please note adding DNN training support is on our roadmap in the coming months and that WILL have GPU support but I want to be sure if there is some other scenario that I might be missing that might benefit from GPUs?
are you saying we should train models that need GPU in another framework and then consume it in ML.NET
@Mercurial no, DNN in ML.NET will support training on the GPU. And you can also consume it on the GPU. So you won't need to train on another framework.
@Mercurial no, DNN in ML.NET will support training on the GPU. And you can also consume it on the GPU. So you won't need to train on another framework.
ah I see, thanks @ebarsoumMS I guess just needs more understanding on this part!
@Mercurial Where did I suggest you should train models in another framework and then consume in ML.NET? I think I was clear that we plan to add DNN support in the coming month and it WILL have GPU support.
Reacted by Gabriel Freire, Andrey Demidov, Nikolas Kilian, Marcel Bradea and Clark Alesna
@Mercurial Where did I suggest you should train models in another framework and then consume in ML.NET? I think I was clear that we plan to add DNN support in the coming month and it WILL have GPU support.
Anyone on this thread looking for GPU acceleration for prediction in a non-DNN-based model? Would be interested in discussing your use case further, if so.
I've migrated to NVIDIA Triton Server and GRPC for inference. It decouples inferencing from my .NET code and is way better for model management, versioning and load-balancing across multiple GPUs. It's also much faster than Tensorflow Serving, should anyone wonder.
Activity
glebuk commentedon May 9, 2018
@FIVIL
Great question. GPUs are excellent for accelerating DNN training and inference. Currently we do not offer DNN learners. At this point, we do not have GPU-accelerated components available. However, ML.NET is a general-purpose machine learning framework. It can support a variety of machine learning algorithms, including neural nets such as Cognitive Toolkit (CNTK). Adding support for leading DNN packages within ML.NET is on our roadmap. We will likely support GPU training and inference in the future for specific learners and transforms that can benefit from it.
forki commentedon May 9, 2018
In the readme you say this lib is used by Bing & Co. - is this really true given that you don't support GPUs?
ashic commentedon May 9, 2018
R has GPU support for a few things besides neural nets:
http://www.r-tutor.com/gpu-computing
Mercurial commentedon May 12, 2018
I agree, this should be priority. I have a GPU Crypto-mining farm and It would be nice to use that from time to time for machine learning computation aswell.
And I think serious data / applications would really want it, I think this should be first priority
turowicz commentedon Dec 14, 2018
Sorry to say, but
No GPU support = no real use in production
KRcpl88 commentedon Jan 2, 2019
Any progress on adding GPU support? I agree with the sentiment that without GPU support, this is not a viable solution for serious ML applications. I wish there were a better .NET based framework for using CNTK which does support GPU. Seems natural the ML.NET would be exactly that, but wince there is no support for this currently, it's a big problem.
isandburn commentedon Jan 3, 2019
agreed. the recent resurgence of popularity in AI/ML/deep-learning is because today's GPUs are far more accessible than super-computing centers. if you build it, they will come.
Made changes to Have non-calibrated scoring for binary classifiers (d…
code migration from machinelearning-automl to machinelearning repo fe…
deepakkumar1984 commentedon Jun 8, 2019
Working on a high level wrapper to build and execute kernel written in C# on special hardware: https://www.tech-quantum.com/gpu-accelerated-programming-with-amplifier/
https://github.com/tech-quantum/Amplifier.NET
codemzs commentedon Jun 8, 2019
People on this thread asking for GPU support, can you please specify the scenario where this might be beneficial for you? Please note adding DNN training support is on our roadmap in the coming months and that WILL have GPU support but I want to be sure if there is some other scenario that I might be missing that might benefit from GPUs?
turowicz commentedon Jun 11, 2019
Realtime footage analysis at 4K resolution and 60 frames per second.
Mercurial commentedon Jun 11, 2019
are you saying we should train models that need GPU in another framework and then consume it in ML.NET
ebarsoumMS commentedon Jun 14, 2019
@Mercurial no, DNN in ML.NET will support training on the GPU. And you can also consume it on the GPU. So you won't need to train on another framework.
Mercurial commentedon Jun 14, 2019
ah I see, thanks @ebarsoumMS I guess just needs more understanding on this part!
codemzs commentedon Jun 30, 2019
@Mercurial Where did I suggest you should train models in another framework and then consume in ML.NET? I think I was clear that we plan to add DNN support in the coming month and it WILL have GPU support.
Made changes to Have non-calibrated scoring for binary classifiers (d…
Added onnx export functionality for LpNormNormalizingTransformer (#4161)
Added Onnx export functionality to PCATransformer (#4188)
ganik commentedon Jan 10, 2020
closing this as it original question has been answered
Mercurial commentedon Jan 10, 2020
Thanks for the clarification
gvashishtha commentedon Feb 10, 2020
Anyone on this thread looking for GPU acceleration for prediction in a non-DNN-based model? Would be interested in discussing your use case further, if so.
turowicz commentedon Apr 6, 2020
I've migrated to NVIDIA Triton Server and GRPC for inference. It decouples inferencing from my .NET code and is way better for model management, versioning and load-balancing across multiple GPUs. It's also much faster than Tensorflow Serving, should anyone wonder.
harishsk commentedon Apr 7, 2020
closing this as it original question has been answered