Deploy doc preview for PR 1837 (d821db1)

Author: cuda-python-bot

docs/pr-preview/pr-1837/cuda-bindings/latest/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 370c1b5b6cca50801ae9a598001e10f3
+config: f1919d8af14b4e4c194465556f885e50
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/pr-preview/pr-1837/cuda-bindings/latest/_sources/contribute.rst.txt

@@ -4,12 +4,17 @@
 Contributing
 ============
 
-Thank you for your interest in contributing to ``cuda-bindings``! Based on the type of contribution, it will fall into two categories:
-
-1. You want to report a bug, feature request, or documentation issue
-    - File an `issue <https://github.com/NVIDIA/cuda-python/issues/new/choose>`_ describing what you encountered or what you want to see changed.
-    - The NVIDIA team will evaluate the issues and triage them, scheduling
-    them for a release. If you believe the issue needs priority attention
-    comment on the issue to notify the team.
-2. You want to implement a feature, improvement, or bug fix:
-    - At this time we do not accept code contributions.
+Thank you for your interest in contributing to ``cuda-bindings``! Based on the
+type of contribution, it will fall into two categories:
+
+1. You want to report a bug, feature request, or documentation issue.
+
+   File an `issue <https://github.com/NVIDIA/cuda-python/issues/new/choose>`_
+   describing what you encountered or what you want to see changed. The NVIDIA
+   team will evaluate the issue, triage it, and schedule it for a release. If
+   you believe the issue needs priority attention, comment on the issue to
+   notify the team.
+
+2. You want to implement a feature, improvement, or bug fix.
+
+   At this time we do not accept code contributions.

docs/pr-preview/pr-1837/cuda-bindings/latest/_sources/examples.rst.txt

@@ -0,0 +1,68 @@
+.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+.. SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+Examples
+========
+
+This page links to the ``cuda.bindings`` examples shipped in the
+`cuda-python repository <https://github.com/NVIDIA/cuda-python/tree/|cuda_bindings_github_ref|/cuda_bindings/examples>`_.
+Use it as a quick index when you want a runnable sample for a specific API area
+or CUDA feature.
+
+Introduction
+------------
+
+- `clock_nvrtc.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/0_Introduction/clock_nvrtc.py>`_
+  uses NVRTC-compiled CUDA code and the device clock to time a reduction
+  kernel.
+- `simple_cubemap_texture.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/0_Introduction/simple_cubemap_texture.py>`_
+  demonstrates cubemap texture sampling and transformation.
+- `simple_p2p.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/0_Introduction/simple_p2p.py>`_
+  shows peer-to-peer memory access and transfers between multiple GPUs.
+- `simple_zero_copy.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/0_Introduction/simple_zero_copy.py>`_
+  uses zero-copy mapped host memory for vector addition.
+- `system_wide_atomics.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/0_Introduction/system_wide_atomics.py>`_
+  demonstrates system-wide atomic operations on managed memory.
+- `vector_add_drv.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/0_Introduction/vector_add_drv.py>`_
+  uses the CUDA Driver API and unified virtual addressing for vector addition.
+- `vector_add_mmap.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/0_Introduction/vector_add_mmap.py>`_
+  uses virtual memory management APIs such as ``cuMemCreate`` and
+  ``cuMemMap`` for vector addition.
+
+Concepts and techniques
+-----------------------
+
+- `stream_ordered_allocation.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/2_Concepts_and_Techniques/stream_ordered_allocation.py>`_
+  demonstrates ``cudaMallocAsync`` and ``cudaFreeAsync`` together with
+  memory-pool release thresholds.
+
+CUDA features
+-------------
+
+- `global_to_shmem_async_copy.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/3_CUDA_Features/global_to_shmem_async_copy.py>`_
+  compares asynchronous global-to-shared-memory copy strategies in matrix
+  multiplication kernels.
+- `simple_cuda_graphs.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/3_CUDA_Features/simple_cuda_graphs.py>`_
+  shows both manual CUDA graph construction and stream-capture-based replay.
+
+Libraries and tools
+-------------------
+
+- `conjugate_gradient_multi_block_cg.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/4_CUDA_Libraries/conjugate_gradient_multi_block_cg.py>`_
+  implements a conjugate-gradient solver with cooperative groups and
+  multi-block synchronization.
+- `nvidia_smi.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/4_CUDA_Libraries/nvidia_smi.py>`_
+  uses NVML to implement a Python subset of ``nvidia-smi``.
+
+Advanced and interoperability
+-----------------------------
+
+- `iso_fd_modelling.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/extra/iso_fd_modelling.py>`_
+  runs isotropic finite-difference wave propagation across multiple GPUs with
+  peer-to-peer halo exchange.
+- `jit_program.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/extra/jit_program.py>`_
+  JIT-compiles a SAXPY kernel with NVRTC and launches it through the Driver
+  API.
+- `numba_emm_plugin.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/extra/numba_emm_plugin.py>`_
+  shows how to back Numba's EMM interface with the NVIDIA CUDA Python Driver
+  API.

docs/pr-preview/pr-1837/cuda-bindings/latest/_sources/index.rst.txt

@@ -11,6 +11,7 @@
    release
    install
    overview
+   examples
    motivation
    environment_variables
    api

docs/pr-preview/pr-1837/cuda-bindings/latest/_sources/install.rst.txt

@@ -78,7 +78,7 @@ Installing from Source
 ----------------------
 
 Requirements
-^^^^^^^^^^^^
+~~~~~~~~~~~~
 
 * CUDA Toolkit headers[^1]
 * CUDA Runtime static library[^2]
@@ -100,7 +100,7 @@ See `Environment Variables <environment_variables.rst>`_ for a description of ot
    Only ``cydriver``, ``cyruntime`` and ``cynvrtc`` are impacted by the header requirement.
 
 Editable Install
-^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~
 
 You can use:
 

docs/pr-preview/pr-1837/cuda-bindings/latest/_sources/module/generated/cuda.bindings.nvml.CoolerInfo_v1.rst.txt

@@ -26,7 +26,7 @@
 
    .. autosummary::
 
-      ~CoolerInfo_v1.ind_ex
+      ~CoolerInfo_v1.index
       ~CoolerInfo_v1.ptr
       ~CoolerInfo_v1.signal_type
       ~CoolerInfo_v1.target

docs/pr-preview/pr-1837/cuda-bindings/latest/_sources/module/generated/cuda.bindings.nvml.PlatformInfo_v1.rst.txt

@@ -27,10 +27,10 @@
    .. autosummary::
 
       ~PlatformInfo_v1.chassis_physical_slot_number
-      ~PlatformInfo_v1.compute_slot_ind_ex
+      ~PlatformInfo_v1.compute_slot_index
       ~PlatformInfo_v1.ib_guid
       ~PlatformInfo_v1.module_id
-      ~PlatformInfo_v1.node_ind_ex
+      ~PlatformInfo_v1.node_index
       ~PlatformInfo_v1.peer_type
       ~PlatformInfo_v1.ptr
       ~PlatformInfo_v1.rack_guid

docs/pr-preview/pr-1837/cuda-bindings/latest/_sources/module/generated/cuda.bindings.nvml.PlatformInfo_v2.rst.txt

@@ -33,7 +33,7 @@
       ~PlatformInfo_v2.peer_type
       ~PlatformInfo_v2.ptr
       ~PlatformInfo_v2.slot_number
-      ~PlatformInfo_v2.tray_ind_ex
+      ~PlatformInfo_v2.tray_index
       ~PlatformInfo_v2.version
 
 

docs/pr-preview/pr-1837/cuda-bindings/latest/_sources/overview.rst.txt

@@ -25,13 +25,14 @@ code into
 `PTX <https://docs.nvidia.com/cuda/parallel-thread-execution/index.html>`_ and
 then extract the function to be called at a later point in the application. You
 construct your device code in the form of a string and compile it with
-`NVRTC <http://docs.nvidia.com/cuda/nvrtc/index.html>`_, a runtime compilation
+`NVRTC <https://docs.nvidia.com/cuda/nvrtc/index.html>`_, a runtime compilation
 library for CUDA C++. Using the NVIDIA `Driver
-API <http://docs.nvidia.com/cuda/cuda-driver-api/index.html>`_, manually create a
+API <https://docs.nvidia.com/cuda/cuda-driver-api/index.html>`_, manually create a
 CUDA context and all required resources on the GPU, then launch the compiled
 CUDA C++ code and retrieve the results from the GPU. Now that you have an
 overview, jump into a commonly used example for parallel programming:
-`SAXPY <https://developer.nvidia.com/blog/six-ways-saxpy/>`_.
+`SAXPY <https://developer.nvidia.com/blog/six-ways-saxpy/>`_. For more
+end-to-end samples, see the :doc:`examples` page.
 
 The first thing to do is import the `Driver
 API <https://docs.nvidia.com/cuda/cuda-driver-api/index.html>`_ and
@@ -427,7 +428,7 @@ Putting it all together:
    )
 
 The final step is to construct a ``kernelParams`` argument that fulfills all of the launch API conditions. This is made easy because each array object comes
-with a `ctypes <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.ctypes.html#numpy.ndarray.ctypes>`_ data attribute that returns the underlying ``void*`` pointer value.
+with NumPy's `ctypes data attribute <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.ctypes.html#numpy.ndarray.ctypes>`_ that returns the underlying ``void*`` pointer value.
 
 By having the final array object contain all pointers, we fulfill the contiguous array requirement:
 
@@ -520,7 +521,10 @@ CUDA objects
 
 Certain CUDA kernels use native CUDA types as their parameters such as ``cudaTextureObject_t``. These types require special handling since they're neither a primitive ctype nor a custom user type. Since ``cuda.bindings`` exposes each of them as Python classes, they each implement ``getPtr()`` and ``__int__()``. These two callables used to support the NumPy and ctypes approach. The difference between each call is further described under `Tips and Tricks <https://nvidia.github.io/cuda-python/cuda-bindings/latest/tips_and_tricks.html#>`_.
 
-For this example, lets use the ``transformKernel`` from `examples/0_Introduction/simpleCubemapTexture_test.py <https://github.com/NVIDIA/cuda-python/blob/main/cuda_bindings/examples/0_Introduction/simpleCubemapTexture_test.py>`_:
+For this example, lets use the ``transformKernel`` from
+`simple_cubemap_texture.py <https://github.com/NVIDIA/cuda-python/blob/|cuda_bindings_github_ref|/cuda_bindings/examples/0_Introduction/simple_cubemap_texture.py>`_.
+The :doc:`examples` page links to more samples covering textures, graphs,
+memory mapping, and multi-GPU workflows.
 
 .. code-block:: python
 

docs/pr-preview/pr-1837/cuda-bindings/latest/_sources/release/11.8.6-notes.rst.txt

@@ -2,7 +2,7 @@
 .. SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
 
 ``cuda-bindings`` 11.8.6 Release notes
-====================================
+==========================================
 
 Released on January 24, 2025.