Main merge release/26.10

CONTRIBUTING.md

@@ -32,7 +32,7 @@ conda install cudf -c rapidsai-nightly -c conda-forge
 ```
 
 3. Build and view the docs locally following the instructions in the [Building
-documentation docs](https://docs.rapids.ai/api/cudf/stable/developer_guide/documentation/#building-documentation)
+documentation docs](https://docs.nvidia.com/cudf/latest/cudf/developer_guide/documentation/#building-documentation)
 4. Follow steps 7-10 in the section [Your first issue](#your-first-issue)
 
 ## Code contributions
@@ -325,9 +325,9 @@ This will bring up an interactive prompt to select which spelling fixes to apply
 
 ## Developer Guidelines
 
-The [C++ Developer Guide](cpp/doxygen/developer_guide/DEVELOPER_GUIDE.md) includes details on contributing to libcudf C++ code.
+The [C++ Developer Guide](https://docs.nvidia.com/cudf/latest/libcudf/developer_guide/) includes details on contributing to libcudf C++ code.
 
-The [Python Developer Guide](https://docs.rapids.ai/api/cudf/stable/cudf/developer_guide/) includes details on contributing to cuDF Python code.
+The [Python Developer Guide](https://docs.nvidia.com/cudf/latest/developer_guide/) includes details on contributing to cuDF Python code.
 
 
 ## Attribution

README.md

@@ -1,21 +1,22 @@
-# <div align="left"><img src="img/rapids_logo.png" width="90px"/>&nbsp;cuDF - A GPU-accelerated DataFrame library for tabular data processing</div>
+# NVIDIA cuDF: A GPU-accelerated DataFrame library for tabular data processing
 
-cuDF (pronounced "KOO-dee-eff") is an [Apache 2.0 licensed](LICENSE), GPU-accelerated DataFrame library
-for tabular data processing. The cuDF library is one part of the [RAPIDS](https://rapids.ai/) GPU
-Accelerated Data Science suite of libraries.
+NVIDIA cuDF (pronounced "KOO-dee-eff") is an [Apache 2.0 licensed](LICENSE), GPU-accelerated DataFrame library
+for tabular data processing. The cuDF library is one part of the [NVIDIA
+CUDA-X](https://developer.nvidia.com/cuda/cuda-x-libraries) suite of GPU
+Accelerated libraries.
 
 ## About
 
 cuDF is composed of multiple libraries including:
 
-* [libcudf](https://docs.rapids.ai/api/libcudf/stable/): A CUDA C++ library with [Apache Arrow](https://arrow.apache.org/) compliant
+* [libcudf](https://docs.nvidia.com/cudf/latest/libcudf/): A CUDA C++ library with [Apache Arrow](https://arrow.apache.org/) compliant
 data structures and fundamental algorithms for tabular data.
-* [pylibcudf](https://docs.rapids.ai/api/cudf/stable/pylibcudf/): A Python library providing [Cython](https://cython.org/) bindings for libcudf.
-* [cudf](https://docs.rapids.ai/api/cudf/stable/cudf/): A Python library providing
+* [pylibcudf](https://docs.nvidia.com/cudf/latest/pylibcudf/): A Python library providing [Cython](https://cython.org/) bindings for libcudf.
+* [cudf](https://docs.nvidia.com/cudf/latest/cudf/): A Python library providing
     - A DataFrame library mirroring the [pandas](https://pandas.pydata.org/) API
-    - A zero-code change accelerator, [cudf.pandas](https://docs.rapids.ai/api/cudf/stable/cudf_pandas/), for existing pandas code.
-* [cudf-polars](https://docs.rapids.ai/api/cudf/stable/cudf_polars/): A Python library providing a GPU engine for [Polars](https://pola.rs/)
-* [dask-cudf](https://docs.rapids.ai/api/dask-cudf/stable/): A Python library providing a GPU backend for [Dask](https://www.dask.org/) DataFrames
+    - A zero-code change accelerator, [cudf.pandas](https://docs.nvidia.com/cudf/latest/cudf_pandas/), for existing pandas code.
+* [cudf-polars](https://docs.nvidia.com/cudf/latest/cudf_polars/): A Python library providing a GPU engine for [Polars](https://pola.rs/)
+* [dask-cudf](https://docs.nvidia.com/dask-cudf/latest/): A Python library providing a GPU backend for [Dask](https://www.dask.org/) DataFrames
 
 Notable projects that use cuDF include:
 

conda/recipes/libcudf/recipe.yaml

@@ -209,7 +209,7 @@ outputs:
           - libzlib
           - libnvcomp
     about:
-      homepage: https://rapids.ai/
+      homepage: https://docs.nvidia.com/cudf/
       license: Apache-2.0
       summary: libcudf_kafka library
 
@@ -253,7 +253,7 @@ outputs:
           - libzlib
           - libnvcomp
     about:
-      homepage: https://rapids.ai/
+      homepage: https://docs.nvidia.com/cudf/
       license: Apache-2.0
       summary: libcudf-streaming library
 
@@ -433,6 +433,6 @@ outputs:
           - librmm
           - libnvcomp
     about:
-      homepage: https://rapids.ai/
+      homepage: https://docs.nvidia.com/cudf/
       license: Apache-2.0
       summary: libcudf-streaming test & benchmark executables

cpp/doxygen/developer_guide/DEVELOPER_GUIDE.md

@@ -26,7 +26,7 @@ A column is an array of data of a single type. Along with Tables, columns are th
 structures used in libcudf. Most libcudf algorithms operate on columns. Columns may have a validity
 mask representing whether each element is valid or null (invalid). Columns of nested types are
 supported, meaning that a column may have child columns. A column is the C++ equivalent to a cuDF
-Python [Series](https://docs.rapids.ai/api/cudf/stable/user_guide/api_docs/api/cudf.series/).
+Python [Series](https://docs.nvidia.com/cudf/latest/cudf/api_docs/series/).
 
 ### Element
 
@@ -41,7 +41,7 @@ A type representing a single element of a data type.
 A table is a collection of columns that all have the same number of elements (rows). A table may
 also have zero columns while still carrying a row count, mirroring an `(N, 0)` DataFrame. A table is
 the C++ equivalent to a cuDF Python
-[DataFrame](https://docs.rapids.ai/api/cudf/stable/user_guide/api_docs/api/cudf.dataframe/).
+[DataFrame](https://docs.nvidia.com/cudf/latest/cudf/api_docs/dataframe/).
 
 ### View
 
@@ -674,7 +674,7 @@ cudf::detail::copy_if(
 
 ## Memory Allocation
 
-Device [memory resources](#rmmdevice_memory_resource) are used in libcudf to abstract and control
+Device [memory resources](#memory-resources) are used in libcudf to abstract and control
 how device memory is allocated.
 
 ### Output Memory

cpp/doxygen/developer_guide/DOCUMENTATION.md

@@ -1,6 +1,6 @@
 # libcudf C++ Documentation Guide
 
-These guidelines apply to documenting all libcudf C++ source files using doxygen style formatting although only public APIs and classes are actually [published](https://docs.rapids.ai/api/libcudf/stable/index.html).
+These guidelines apply to documenting all libcudf C++ source files using doxygen style formatting although only public APIs and classes are actually [published](https://docs.nvidia.com/cudf/latest/libcudf/api_docs/).
 
 ## Copyright License
 
@@ -438,4 +438,5 @@ Then open `<IP address>:8000` in your local web browser, inserting the IP addres
 
 The doxygen output is intended for building documentation only for the public APIs and classes.
 For example, the output should not include documentation for `detail` or `/src` files, and these directories are excluded in the `Doxyfile` configuration.
-When published by the build/CI system, the doxygen output will appear on our external [RAPIDS web site](https://docs.rapids.ai/api/libcudf/stable/index.html).
+When published by the build/CI system, the doxygen output will appear as
+part of the [cuDF documentation](https://docs.nvidia.com/cudf/latest/libcudf/).