@kevinthesun Any hints on the issues above?
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Thanks for your reply.
I will take a look at how to use debug_executor to enable such a function.
Also, I am asking this question because this is somehow related to pipeline
execution.
Thus, I am still wondering is it possible for that user can we register
operations in Relay IR as new outputs
Hi there,
When following the [quantization
tutorial](https://tvm.apache.org/docs/how_to/deploy_models/deploy_prequantized.html),
the generated tvmir includes a lot of comment information like
```
fn (%input: Tensor[(1, 3, 224, 224), float32], %features.0.0_weight:
Tensor[(32, 3, 3, 3), float
https://github.com/AndrewZhaoLuo/TVM-Sandbox/blob/main/relay/graph_debugger_example.py
Here is an example of using graph debugger. My apologies as it isn't very
complete and the tensor dumps you'll have to manually associate.
If you use graph debugger there is also an interesting function
`g
I heard that's possible with `debug_executor`. But I've never tried it. Can you
take a look?
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@fPecc thanks for your questions! Here are some answers.
[quote="fPecc, post:1, topic:12091"]
* AutoTVM running on the host computer
* Multiple boards connected to the same host computer running the AutoTVM
schedules.
[/quote]
We've demonstrated the first one, but not the second one yet. It sh
TF2 was supported now
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Hi all!
I have currently been trying to understand the relationship between this
components, in order to integrate a custom accelerator + board into TVM.
As a reference, this is what I would like to try:
* AutoTVM running on the host computer
* Multiple boards connected to the same host compu