Comments (1)
Yes, so basically you have a Axodox.MachineLearning nuget package which you can add to your own projects to use the existing functionality of the library.
But sometimes you need to add functionalities so it suits your specific needs. In this case the simplest solution would be that you add the new features, build the project generate a new nuget package, and then finally add the new nuget to your project, but this is not very convenient.
Instead of this you create an environment variable and point it to your local copy of the library repo, in which case the nuget package will reference your local version of the package instead of its own files. Allowing you to develop seamlessly.
For even better dev experience you can even add the projects which make up the library to your own solution, so they automatically will get built if you change them. This allows you to develop as if all code were in a single app.
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Related Issues (15)
- Projects with Win32 target fail to build
- LoRA support HOT 5
- Failed to set to the execution context HOT 1
- Should StableDiffusionTest.cpp generate an image?
- Could you compile and release a Windows CLI.exe for your project, please?
- NuGet fails with fresh Unreal Engine 5.2 Project HOT 2
- hi,What kind of library is this, is onnx running on the onnxruntime? HOT 1
- custom_op_cliptok.onnx HOT 2
- Has iOS support and what model is supported? HOT 1
- LCM support HOT 1
- [Feature Request] Support v-prediction
- Can't build project, looks like there's at least one unlisted dependency HOT 7
- Is it possible to build the project in mac arm model or linux platform? HOT 3
- Is it possible to use library without WinRt? HOT 12
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