How can I increase CUDA load on a Tensorflow deep learning task after reducing batch size to fit the GPU?
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I am running this TensorFlow task for Swahili-to-English on an NVidia GeForce 1060 GPU with 6GB of VRAM:
https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb
I have to reduce the batch size to get the example code not to run out of memory on the GPU. With trial and error, I find a batch size (minimum is 1) where the code runs and uses the most memory. I notice that, as I reduce the batch size, the CUDA core load as reported by Windows Task Manager GPU view goes down.
The application described in the link above creates a complex TensorFlow network. I don't know whether Tensorflow creates one copy of the network or multiple copies to load the GPU.
If it can create multiple copies, is there a TensorFlow switch for that? I don't think memory speed should be a bottleneck in feeding the GPU. That is, I should be able to optimize between batch size or number of jobs resident in the GPU, and number of compute networks in the GPU.
Is there an easy way in TensorFlow to assess the size in CUDA cores of a compute network?
What are the factors I can use to optimize CUDA load in a small GPU with a large deep learning task?
machine-learning tensorflow machine-translation
asked 16 mins ago
Lars EricsonLars Ericson
1334
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add a comment |
$begingroup$
I am running this TensorFlow task for Swahili-to-English on an NVidia GeForce 1060 GPU with 6GB of VRAM:
https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb
I have to reduce the batch size to get the example code not to run out of memory on the GPU. With trial and error, I find a batch size (minimum is 1) where the code runs and uses the most memory. I notice that, as I reduce the batch size, the CUDA core load as reported by Windows Task Manager GPU view goes down.
The application described in the link above creates a complex TensorFlow network. I don't know whether Tensorflow creates one copy of the network or multiple copies to load the GPU.
If it can create multiple copies, is there a TensorFlow switch for that? I don't think memory speed should be a bottleneck in feeding the GPU. That is, I should be able to optimize between batch size or number of jobs resident in the GPU, and number of compute networks in the GPU.
Is there an easy way in TensorFlow to assess the size in CUDA cores of a compute network?
What are the factors I can use to optimize CUDA load in a small GPU with a large deep learning task?
machine-learning tensorflow machine-translation
asked 16 mins ago
Lars EricsonLars Ericson
1334
$endgroup$
add a comment |
$begingroup$
I am running this TensorFlow task for Swahili-to-English on an NVidia GeForce 1060 GPU with 6GB of VRAM:
https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb
I have to reduce the batch size to get the example code not to run out of memory on the GPU. With trial and error, I find a batch size (minimum is 1) where the code runs and uses the most memory. I notice that, as I reduce the batch size, the CUDA core load as reported by Windows Task Manager GPU view goes down.
The application described in the link above creates a complex TensorFlow network. I don't know whether Tensorflow creates one copy of the network or multiple copies to load the GPU.
If it can create multiple copies, is there a TensorFlow switch for that? I don't think memory speed should be a bottleneck in feeding the GPU. That is, I should be able to optimize between batch size or number of jobs resident in the GPU, and number of compute networks in the GPU.
Is there an easy way in TensorFlow to assess the size in CUDA cores of a compute network?
What are the factors I can use to optimize CUDA load in a small GPU with a large deep learning task?
machine-learning tensorflow machine-translation
asked 16 mins ago
Lars EricsonLars Ericson
1334
$endgroup$
I am running this TensorFlow task for Swahili-to-English on an NVidia GeForce 1060 GPU with 6GB of VRAM:
https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb
I have to reduce the batch size to get the example code not to run out of memory on the GPU. With trial and error, I find a batch size (minimum is 1) where the code runs and uses the most memory. I notice that, as I reduce the batch size, the CUDA core load as reported by Windows Task Manager GPU view goes down.
The application described in the link above creates a complex TensorFlow network. I don't know whether Tensorflow creates one copy of the network or multiple copies to load the GPU.
If it can create multiple copies, is there a TensorFlow switch for that? I don't think memory speed should be a bottleneck in feeding the GPU. That is, I should be able to optimize between batch size or number of jobs resident in the GPU, and number of compute networks in the GPU.
Is there an easy way in TensorFlow to assess the size in CUDA cores of a compute network?
What are the factors I can use to optimize CUDA load in a small GPU with a large deep learning task?
machine-learning tensorflow machine-translation
machine-learning tensorflow machine-translation
asked 16 mins ago
Lars EricsonLars Ericson
1334
asked 16 mins ago
Lars EricsonLars Ericson
1334
asked 16 mins ago
Lars EricsonLars Ericson
1334
asked 16 mins ago
Lars EricsonLars Ericson
1334
asked 16 mins ago
Lars EricsonLars Ericson
1334
1334
add a comment |
add a comment |
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