Fix typos and grammar in documentation

cluster2600 · claude · cluster2600 · commit f61bf930ed49 · 2025-07-24T09:01:28.000+02:00
Documentation fixes: - Fix capitalization (pythonic → Pythonic) for consistency across repo - Fix grammar (re-structured → restructured, bring up → introduce) - Fix subject-verb agreement (list interfaces are → is) - Fix SECURITY.md header reference (nvmath-python → CUDA Python) - Fix spelling errors (transferring, absence) - Fix verb forms (Set up, with vs against) - Fix directory paths (test/cython → tests/cython) Note: No dependency version bumps per maintainer request 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
diff --git a/README.md b/README.md
@@ -5,23 +5,23 @@ CUDA Python is the home for accessing NVIDIA’s CUDA platform from Python. It c
 * [cuda.core](https://nvidia.github.io/cuda-python/cuda-core/latest): Pythonic access to CUDA Runtime and other core functionalities
 * [cuda.bindings](https://nvidia.github.io/cuda-python/cuda-bindings/latest): Low-level Python bindings to CUDA C APIs
 * [cuda.cccl.cooperative](https://nvidia.github.io/cccl/cuda_cooperative/): A Python module providing CCCL's reusable block-wide and warp-wide *device* primitives for use within Numba CUDA kernels
-* [cuda.cccl.parallel](https://nvidia.github.io/cccl/cuda_parallel/): A Python module for easy access to CCCL's highly efficient and customizable parallel algorithms, like `sort`, `scan`, `reduce`, `transform`, etc, that are callable on the *host*
+* [cuda.cccl.parallel](https://nvidia.github.io/cccl/cuda_parallel/): A Python module for easy access to CCCL's highly efficient and customizable parallel algorithms, like `sort`, `scan`, `reduce`, `transform`, etc. that are callable on the *host*
 * [numba.cuda](https://nvidia.github.io/numba-cuda/): Numba's target for CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model.
 * [nvmath-python](https://docs.nvidia.com/cuda/nvmath-python/latest): Pythonic access to NVIDIA CPU & GPU Math Libraries, with both [*host*](https://docs.nvidia.com/cuda/nvmath-python/latest/overview.html#host-apis) and [*device* (nvmath.device)](https://docs.nvidia.com/cuda/nvmath-python/latest/overview.html#device-apis) APIs. It also provides low-level Python bindings to host C APIs ([nvmath.bindings](https://docs.nvidia.com/cuda/nvmath-python/latest/bindings/index.html)).
 
-CUDA Python is currently undergoing an overhaul to improve existing and bring up new components. All of the previously available functionalities from the `cuda-python` package will continue to be available, please refer to the [cuda.bindings](https://nvidia.github.io/cuda-python/cuda-bindings/latest) documentation for installation guide and further detail.
+CUDA Python is currently undergoing an overhaul to improve existing and introduce new components. All of the previously available functionalities from the `cuda-python` package will continue to be available, please refer to the [cuda.bindings](https://nvidia.github.io/cuda-python/cuda-bindings/latest) documentation for installation guide and further detail.
 
 ## cuda-python as a metapackage
 
-`cuda-python` is being re-structured to become a metapackage that contains a collection of subpackages. Each subpackage is versioned independently, allowing installation of each component as needed.
+`cuda-python` is being restructured to become a metapackage that contains a collection of subpackages. Each subpackage is versioned independently, allowing installation of each component as needed.
 
 ### Subpackage: `cuda.core`
 
-The `cuda.core` package offers idiomatic, pythonic access to CUDA Runtime and other functionalities.
+The `cuda.core` package offers idiomatic, Pythonic access to CUDA Runtime and other functionalities.
 
 The goals are to
 
-1. Provide **idiomatic ("pythonic")** access to CUDA Driver, Runtime, and JIT compiler toolchain
+1. Provide **idiomatic ("Pythonic")** access to CUDA Driver, Runtime, and JIT compiler toolchain
 2. Focus on **developer productivity** by ensuring end-to-end CUDA development can be performed quickly and entirely in Python
 3. **Avoid homegrown** Python abstractions for CUDA for new Python GPU libraries starting from scratch
 4. **Ease** developer **burden of maintaining** and catching up with latest CUDA features
@@ -31,7 +31,7 @@ The goals are to
 
 The `cuda.bindings` package is a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python.
 
-The list of available interfaces are:
+The list of available interfaces is:
 
 * CUDA Driver
 * CUDA Runtime
diff --git a/SECURITY.md b/SECURITY.md
@@ -6,9 +6,9 @@ including all source code repositories managed through our organization.
 If you need to report a security issue, please use the appropriate contact points outlined
 below. **Please do not report security vulnerabilities through GitHub/GitLab.**
 
-## Reporting Potential Security Vulnerability in nvmath-python
+## Reporting Potential Security Vulnerability in CUDA Python
 
-To report a potential security vulnerability in nvmath-python:
+To report a potential security vulnerability in CUDA Python:
 
 - Web: [Security Vulnerability Submission
   Form](https://www.nvidia.com/object/submit-security-vulnerability.html)
diff --git a/cuda_core/README.md b/cuda_core/README.md
@@ -1,4 +1,4 @@
-# `cuda.core`: (experimental) pythonic CUDA module
+# `cuda.core`: (experimental) Pythonic CUDA module
 
 Currently under active development; see [the documentation](https://nvidia.github.io/cuda-python/cuda-core/latest/) for more details.
 
@@ -13,16 +13,16 @@ This subpackage adheres to the developing practices described in the parent meta
 ## Testing
 
 To run these tests:
-* `python -m pytest tests/` against editable installations
-* `pytest tests/` against installed packages
+* `python -m pytest tests/` with editable installations
+* `pytest tests/` with installed packages
 
 ### Cython Unit Tests
 
 Cython tests are located in `tests/cython` and need to be built. These builds have the same CUDA Toolkit header requirements as [those of cuda.bindings](https://nvidia.github.io/cuda-python/cuda-bindings/latest/install.html#requirements) where the major.minor version must match `cuda.bindings`. To build them:
 
-1. Setup environment variable `CUDA_HOME` with the path to the CUDA Toolkit installation.
-2. Run `build_tests` script located in `test/cython` appropriate to your platform. This will both cythonize the tests and build them.
+1. Set up environment variable `CUDA_HOME` with the path to the CUDA Toolkit installation.
+2. Run `build_tests` script located in `tests/cython` appropriate to your platform. This will both cythonize the tests and build them.
 
 To run these tests:
-* `python -m pytest tests/cython/` against editable installations
-* `pytest tests/cython/` against installed packages
+* `python -m pytest tests/cython/` with editable installations
+* `pytest tests/cython/` with installed packages
diff --git a/cuda_core/docs/source/getting-started.md b/cuda_core/docs/source/getting-started.md
@@ -8,7 +8,7 @@ including:
 - Compiling and launching CUDA kernels
 - Asynchronous concurrent execution with CUDA graphs, streams and events
 - Coordinating work across multiple CUDA devices
-- Allocating, transfering, and managing device memory
+- Allocating, transferring, and managing device memory
 - Runtime linking of device code with Link-Time Optimization (LTO)
 - and much more!
 
@@ -94,7 +94,7 @@ s.sync()
 ```
 
 This example demonstrates one of the core workflows enabled by `cuda.core`: compiling and launching CUDA code.
-Note the clean, Pythonic interface, and absense of any direct calls to the CUDA runtime/driver APIs.
+Note the clean, Pythonic interface, and absence of any direct calls to the CUDA runtime/driver APIs.
 
 ## Examples and Recipes
 
