gemini-code-assist[bot] commented on PR #18404:
URL: https://github.com/apache/tvm/pull/18404#issuecomment-3465813271

   ## Summary of Changes
   
   Hello @tlopex, I'm Gemini Code Assist[^1]! I'm currently reviewing this pull 
request and will post my feedback shortly. In the meantime, here's a summary to 
help you and other reviewers quickly get up to speed!
   
   This pull request introduces a new, detailed tutorial aimed at clarifying 
the process of exporting and loading compiled Relax executables. It provides a 
practical guide for users to convert their AI models, originating from 
frameworks like PyTorch or ONNX, into deployable shared libraries. The tutorial 
covers the entire workflow from compilation to execution within the TVM 
runtime, addressing common deployment challenges and enabling efficient model 
inference across various target devices, including remote systems.
   
   ### Highlights
   
   * **New Tutorial Added**: A comprehensive tutorial has been added that 
demonstrates how to export compiled Relax modules to shared libraries (`.so` 
files).
   * **Loading and Execution**: The tutorial details the process of loading 
these exported shared libraries back into the TVM runtime and executing them, 
covering both interactive and standalone script usage.
   * **Model Deployment Workflow**: It illustrates how to transform Relax 
programs, including those imported from PyTorch or ONNX models, into deployable 
artifacts using `tvm.relax` APIs.
   * **Parameter Management**: Guidance is provided on how to separate and save 
model parameters for flexible deployment, with an alternative method for 
embedding parameters directly into the shared library.
   * **Advanced Deployment Scenarios**: The tutorial includes instructions for 
running models on GPU devices and deploying to remote targets, such as ARM 
Linux devices, utilizing TVM's RPC mechanism.
   
   
   
   
   
   
   
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