Deploy doc preview for PR 1775 (ee96758)

Author: cuda-python-bot

docs/pr-preview/pr-1775/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: 6960a050838e746484186d8fc3093d38
+config: 24345538fb45fdacdb53c98901e8386a
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/pr-preview/pr-1775/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-1775/cuda-bindings/latest/_sources/environment_variables.rst.txt

@@ -15,7 +15,14 @@ Runtime Environment Variables
 Build-Time Environment Variables
 --------------------------------
 
-- ``CUDA_HOME`` or ``CUDA_PATH``: Specifies the location of the CUDA Toolkit.
+- ``CUDA_PATH`` or ``CUDA_HOME``: Specifies the location of the CUDA Toolkit. If both are set, ``CUDA_PATH`` takes precedence.
+
+  .. note::
+     The ``CUDA_PATH`` > ``CUDA_HOME`` priority is determined by ``cuda-pathfinder``.
+     Earlier versions of ``cuda-pathfinder`` (before 1.5.0) used the opposite order
+     (``CUDA_HOME`` > ``CUDA_PATH``). See the
+     `cuda-pathfinder 1.5.0 release notes <https://nvidia.github.io/cuda-python/cuda-pathfinder/latest/release/1.5.0-notes.html>`_
+     for details and migration guidance.
 
 - ``CUDA_PYTHON_PARSER_CACHING`` : bool, toggles the caching of parsed header files during the cuda-bindings build process. If caching is enabled (``CUDA_PYTHON_PARSER_CACHING`` is True), the cache path is set to ./cache_<library_name>, where <library_name> is derived from the cuda toolkit libraries used to build cuda-bindings.
 

docs/pr-preview/pr-1775/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-1775/cuda-bindings/latest/_sources/index.rst.txt

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

docs/pr-preview/pr-1775/cuda-bindings/latest/_sources/install.rst.txt

@@ -78,7 +78,7 @@ Installing from Source
 ----------------------
 
 Requirements
-^^^^^^^^^^^^
+~~~~~~~~~~~~
 
 * CUDA Toolkit headers[^1]
 * CUDA Runtime static library[^2]
@@ -87,11 +87,11 @@ Requirements
 
 [^2]: The CUDA Runtime static library (``libcudart_static.a`` on Linux, ``cudart_static.lib`` on Windows) is part of the CUDA Toolkit. If using conda packages, it is contained in the ``cuda-cudart-static`` package.
 
-Source builds require that the provided CUDA headers are of the same major.minor version as the ``cuda.bindings`` you're trying to build. Despite this requirement, note that the minor version compatibility is still maintained. Use the ``CUDA_HOME`` (or ``CUDA_PATH``) environment variable to specify the location of your headers. For example, if your headers are located in ``/usr/local/cuda/include``, then you should set ``CUDA_HOME`` with:
+Source builds require that the provided CUDA headers are of the same major.minor version as the ``cuda.bindings`` you're trying to build. Despite this requirement, note that the minor version compatibility is still maintained. Use the ``CUDA_PATH`` (or ``CUDA_HOME``) environment variable to specify the location of your headers. If both are set, ``CUDA_PATH`` takes precedence. For example, if your headers are located in ``/usr/local/cuda/include``, then you should set ``CUDA_PATH`` with:
 
 .. code-block:: console
 
-   $ export CUDA_HOME=/usr/local/cuda
+   $ export CUDA_PATH=/usr/local/cuda
 
 See `Environment Variables <environment_variables.rst>`_ for a description of other build-time environment variables.
 
@@ -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-1775/cuda-bindings/latest/_sources/module/driver.rst.txt

@@ -1,4 +1,4 @@
-.. SPDX-FileCopyrightText: Copyright (c) 2021-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+.. SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 .. SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
 
 ------
@@ -480,7 +480,7 @@ Data types used by CUDA driver
     .. autoattribute:: cuda.bindings.driver.CUstreamBatchMemOpType.CU_STREAM_MEM_OP_ATOMIC_REDUCTION
 
 
-        Perform a atomic reduction. See :py:obj:`~.CUstreamBatchMemOpParams`::atomicReduction
+        Perform a atomic reduction. See :py:obj:`~.CUstreamBatchMemOpParams.atomicReduction`
 
 
     .. autoattribute:: cuda.bindings.driver.CUstreamBatchMemOpType.CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES
@@ -3555,7 +3555,7 @@ Data types used by CUDA driver
 
         Valid for graph nodes, launches. This attribute is graphs-only, and passing it to a launch in a non-capturing stream will result in an error. 
 
-         :py:obj:`~.CUlaunchAttributeValue`::deviceUpdatableKernelNode::deviceUpdatable can only be set to 0 or 1. Setting the field to 1 indicates that the corresponding kernel node should be device-updatable. On success, a handle will be returned via :py:obj:`~.CUlaunchAttributeValue`::deviceUpdatableKernelNode::devNode which can be passed to the various device-side update functions to update the node's kernel parameters from within another kernel. For more information on the types of device updates that can be made, as well as the relevant limitations thereof, see :py:obj:`~.cudaGraphKernelNodeUpdatesApply`. 
+         :py:obj:`~.CUlaunchAttributeValue.deviceUpdatableKernelNode.deviceUpdatable` can only be set to 0 or 1. Setting the field to 1 indicates that the corresponding kernel node should be device-updatable. On success, a handle will be returned via :py:obj:`~.CUlaunchAttributeValue.deviceUpdatableKernelNode.devNode` which can be passed to the various device-side update functions to update the node's kernel parameters from within another kernel. For more information on the types of device updates that can be made, as well as the relevant limitations thereof, see :py:obj:`~.cudaGraphKernelNodeUpdatesApply`. 
 
          Nodes which are device-updatable have additional restrictions compared to regular kernel nodes. Firstly, device-updatable nodes cannot be removed from their graph via :py:obj:`~.cuGraphDestroyNode`. Additionally, once opted-in to this functionality, a node cannot opt out, and any attempt to set the deviceUpdatable attribute to 0 will result in an error. Device-updatable kernel nodes also cannot have their attributes copied to/from another kernel node via :py:obj:`~.cuGraphKernelNodeCopyAttributes`. Graphs containing one or more device-updatable nodes also do not allow multiple instantiation, and neither the graph nor its instantiated version can be passed to :py:obj:`~.cuGraphExecUpdate`. 
 
@@ -3579,7 +3579,7 @@ Data types used by CUDA driver
 
          This attribute is a hint only. CUDA makes no functional or performance guarantee. Its applicability can be affected by many different factors, including driver version (i.e. CUDA doesn't guarantee the performance characteristics will be maintained between driver versions or a driver update could alter or regress previously observed perf characteristics.) It also doesn't guarantee a successful result, i.e. applying the attribute may not improve the performance of either the targeted kernel or the encapsulating application. 
 
-         Valid values for :py:obj:`~.CUlaunchAttributeValue`::nvlinkUtilCentricScheduling are 0 (disabled) and 1 (enabled).
+         Valid values for :py:obj:`~.CUlaunchAttributeValue.nvlinkUtilCentricScheduling` are 0 (disabled) and 1 (enabled).
 
 
     .. autoattribute:: cuda.bindings.driver.CUlaunchAttributeID.CU_LAUNCH_ATTRIBUTE_PORTABLE_CLUSTER_SIZE_MODE
@@ -7752,6 +7752,14 @@ Checkpoint and restore capabilities are currently restricted to Linux.
 .. autofunction:: cuda.bindings.driver.cuCheckpointProcessRestore
 .. autofunction:: cuda.bindings.driver.cuCheckpointProcessUnlock
 
+Profiler Control
+----------------
+
+This section describes the profiler control functions of the low-level CUDA driver application programming interface.
+
+.. autofunction:: cuda.bindings.driver.cuProfilerStart
+.. autofunction:: cuda.bindings.driver.cuProfilerStop
+
 EGL Interoperability
 --------------------
 
@@ -7798,14 +7806,6 @@ This section describes the OpenGL interoperability functions of the low-level CU
 .. autofunction:: cuda.bindings.driver.cuGraphicsGLRegisterImage
 .. autofunction:: cuda.bindings.driver.cuGLGetDevices
 
-Profiler Control
-----------------
-
-This section describes the profiler control functions of the low-level CUDA driver application programming interface.
-
-.. autofunction:: cuda.bindings.driver.cuProfilerStart
-.. autofunction:: cuda.bindings.driver.cuProfilerStop
-
 VDPAU Interoperability
 ----------------------
 

docs/pr-preview/pr-1775/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-1775/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-1775/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