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📝 WalkthroughWalkthroughThe changes consolidate and reorganize nvForest documentation by moving detailed build instructions from BUILD.md to a new comprehensive docs/source/build.rst file, adding new getting-started guides with Python and C++ usage examples, restructuring the documentation index with updated toctree entries, removing the supported versions documentation, and updating build system paths for Doxygen configuration. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~12 minutes Tip Try Coding Plans. Let us write the prompt for your AI agent so you can ship faster (with fewer bugs). Note 🎁 Summarized by CodeRabbit FreeYour organization is on the Free plan. CodeRabbit will generate a high-level summary and a walkthrough for each pull request. For a comprehensive line-by-line review, please upgrade your subscription to CodeRabbit Pro by visiting https://app.coderabbit.ai/login. Comment |
Co-authored-by: James Lamb <jaylamb20@gmail.com>
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/merge |
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/ok to test 596d49f |
csadorf
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Not sure how this got merged, but there are some things that need to be fixed.
| It is possible to build nvForest without GPU support; in this case, the CUDA toolkit is not required. | ||
| To build nvForest without GPU, set the CMake option ``NVFOREST_ENABLE_GPU=ON``. |
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We should automatically run the code in these files in a follow-up. I know that we are note, because there are some minor syntax errors within the code right now.
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I'm sorry that I missed the syntax errors. I will pay more attention to the details in the future. Also, I will follow up by creating an automated CI pipeline to run the example code.
| fm = nvforest.load_model("/path/to/xgboost_model.ubj", device="gpu" | ||
| model_type="xgboost_ubj") |
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missing comma
| fm = nvforest.load_model("/path/to/xgboost_model.ubj", device="gpu" | |
| model_type="xgboost_ubj") | |
| fm = nvforest.load_model("/path/to/xgboost_model.ubj", device="gpu", | |
| model_type="xgboost_ubj") |
| The model will reside in the GPU memory. | ||
| * :py:class:`~nvforest.CPUForestInferenceClassifier`: a classification model, to run on CPU. | ||
| The model object will reside in the main memory. | ||
| * :py:class:`~nvforest.CPUForestInferenceRegressor`: a classification model, to run on CPU. |
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| * :py:class:`~nvforest.CPUForestInferenceRegressor`: a classification model, to run on CPU. | |
| * :py:class:`~nvforest.CPUForestInferenceRegressor`: a regression model, to run on CPU. |
| std::unique_ptr<treelite::Model> treelite_model | ||
| = treelite::model_loader::LoadXGBoostModelUBJSON( | ||
| "/path/to/xgboost_model.ubj", "{}") |
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missing semicolon
| std::unique_ptr<treelite::Model> treelite_model | |
| = treelite::model_loader::LoadXGBoostModelUBJSON( | |
| "/path/to/xgboost_model.ubj", "{}") | |
| std::unique_ptr<treelite::Model> treelite_model | |
| = treelite::model_loader::LoadXGBoostModelUBJSON( | |
| "/path/to/xgboost_model.ubj", "{}"); |
| - [ON, OFF] | ||
| - ON | ||
| - Use detection of conda environment for dependencies. If set to ON, and no value for CMAKE_INSTALL_PREFIX is passed, then it will assign it to $CONDA_PREFIX (to install in the active environment). | ||
| * - DIABLE_DEPRECATION_WARNINGS |
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typo
| * - DIABLE_DEPRECATION_WARNINGS | |
| * - DISABLE_DEPRECATION_WARNINGS |
Might be related to the codeowner team names. |
* Address comments from #48 * Set up doctest Authors: - Philip Hyunsu Cho (https://github.com/hcho3) - Simon Adorf (https://github.com/csadorf) Approvers: - Simon Adorf (https://github.com/csadorf) - James Lamb (https://github.com/jameslamb) URL: #60
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