Comments (7)
i solved the issue. The reason is why data is not prepared when GPU need to compute. Now i put the data and label into files, GPU can get data immediately when the training stage.
Although your code decouples data processing and training networks. But it caused serial training instead of parallelism, and the main expenses were placed on the cpu. It is possible when the amount of data is small, but it is extremely inefficient when the data is slightly larger.
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Not sure what this means, I haven't experienced this error. Could you give me some steps to reproduce the error?
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i got the same issue plus resource-exhausted.
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Not sure what this means, I haven't experienced this error. Could you give me some steps to reproduce the error?
11%|███▍ | 1058/10000 [9:15:04<77:40:33, 31.27s/it]
Is this state normal with GPU ? I think this speed is a bit slow and GPU-Util is zero with 10000 img of 100*100, batchsize=32 ,32layer Net. thx~~
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Hey there, I believe you are right that the timing you show is quite slow. I think there may be a problem with your cuda setup, unrelated to the code here. This would probably cause zero GPU utilization. Nothing in this project directly edits what is being used in the gpu. That is done behind the scenes by tensorflow
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Hey there, I believe you are right that the timing you show is quite slow. I think there may be a problem with your cuda setup, unrelated to the code here. This would probably cause zero GPU utilization. Nothing in this project directly edits what is being used in the gpu. That is done behind the scenes by tensorflow
thx~~little brother ,hahah, Could u tell me your tensorflow-version , CUDA and cudnn version? (i use python2.7 env)
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I see, good catch. Do you have code for this? If so, please make a PR and I'll review it
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Related Issues (20)
- error in train.py HOT 4
- about training HOT 1
- I've implemented a Keras version of EDSR HOT 1
- training problem for tensorflow HOT 1
- What are the changes compared to the original paper? HOT 3
- test error HOT 2
- test_size in load_dataset
- train error HOT 3
- train error HOT 7
- ResourceExhaustedError HOT 1
- after 1 epoch, you don't shuffle? HOT 6
- train_setOnly 200
- How to control the training epochs in the codes ? HOT 2
- test error HOT 2
- 14k times------loss suddenly increase HOT 3
- Did you use the GAN structure? HOT 5
- how to training MDSR HOT 2
- test error
- out of range HOT 4
- Versions?
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