I had same problem. After I changed
`set_cuda_target_arch('sm_62')`
to
`set_cuda_target_arch('sm_37')` like AWS EC2 p2 you used,
I could run the code with no problem.
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wow thank you! I'll try it.
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I have tuned , but it wasn't useful. I use nvidia 1080ti, cuda.
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have you tried using AutoTVM to tune these ops? Which platform are you using?
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Dynamic input shape will involve dynamic shape kernel codegen, which is still
WIP.
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Good questions in general.
1. This would be a problem if you have limited memory in the machine.
2. Currently AutoTVM doesn't have an official API to export tasks. At this
moment, one solution I can think of is writing a script to extract necessary
task information and save it to a JSON file.
maybe group=1 fallback to a dense convolution. group=32 become a depthwise
convolution. tvm has tuned optimal schedules for dense conv and depthwise conv
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`export CUDA_VISIBLE_DEVICES="your gpu id"`
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ok, thinks ,any way.
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@ [comaniac](https://discuss.tvm.ai/u/comaniac)
any advice? Thank you!
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have you solved this problem?
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No, the input shape needs to be fixed.
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Same error with Jetson Xavier
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relay.frontend.from_pytorch set fixed input size, but I need input size can
change at inference，is there any way to handle this?
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