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View Code? Open in Web Editor NEWANT framework's model database that provides DNN models for the various range of IoT devices
License: Apache License 2.0
ANT framework's model database that provides DNN models for the various range of IoT devices
License: Apache License 2.0
Performing a sparse kernel is essential to measure the latency of various compressed models.
Currently, TVM supports model convert only using dense kernel.
It is necessary to find out the sparsity of the model and convert it using sparse kernel accordingly.
Does the version of prerequisites such as llvm and cudnn matter?
Can you confirm what models are available?
This repository has no license file.
If you extend TVM to implement Latency Predictor, you must specify your license referring to TVM's license.
TVM seems to use "Apache 2.0" License.
I think that Apache 2.0 license is proper for this repository.
There are many dependencies to use in several environments like
BLAS libraries, CUDNN, etc.
We have to specify how to install dependencies in README.
How often is the model database updated with new DNN models?
How can someone contribute to this project? Are there specific guidelines or areas where help is needed?
How does the kernel database tune the kernel codes of the compressed models?
Hi,
I want to ask how the predictor will predict the latency of a DNN model.
By machine learning or some other method?
And how many and what kinds of factors do you think counts to predict latency now?
As now, ANT model DB repository includes the source code of TVM.
As I think, the reason may be that the model profiler (latency predictor) has very strong dependency on TVM.
However, it is not easy to identify what is the source code of the model profiler.
For the maintenance in the long term, we need to separate the TVM code from this repository.
And then, we need to make TVM as a git submodule
.
As model database and latency predictor are implemented, someone can want to use these modules.
However, it seems that there is no readable documentation in this repository.
I think that any readable documentation in the code repository or Github Wiki is required.
Are there any security measures in place for the model and kernel databases, especially considering the deployment of models on IoT devices?
Hi,
I installed requirements and now try to run test codes.
When I run test.py, this error message occurs and running fails.
AttributeError: 'XGBoostCostModel' object has no attribute 'pool'
My Environment is ubuntu18.04 and NVIDIA GPU.
Can you tell me how to fix this?
To implement a latency predictor based on the regression model, we have to consider many cases.
So, it seems a need for kernel DB.
What types of DNN models are included in the model database, and how are they optimized for IoT devices?
I'm new to programming and this field. Could you provide detailed instructions on how to set up and run the dashboard using dashboard-run.py?
We've already developed the model database,
but the web-based model dashboard have not been uploaded to this repository.
To provide the users easy model selection UI, we need web-based model dashboard.
Hello,
Model dashboard seems to be uploaded.
However, since there is no guide to launch the model dashboard, I cannot use it.
Could you upload the guide to the README.md?
Are there any resources, like documentation or tutorials, available for new users to understand and use the ANT Model DB effectively?
In order to help users to choose the appropriate model, it is necessary to provide profiled information on the models.
So we decided to manage the model database along with the latency profiler.
The model DB needs to be implemented in this repository.
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