Packages
NetworkX provides data structures and algorithms for creating, analyzing, and manipulating graphs and networks, supporting everything from simple undirected graphs to complex directed and weighted networks.
kiwisolver is a Python binding to a fast C++ implementation of the Cassowary constraint solver, enabling you to solve systems of linear constraints and inequalities.
Install it if you need to solve constraint systems; skip it if you only need simple linear algebra.
SymPy is a Python library for symbolic mathematics, performing algebraic manipulation, calculus, equation solving, and mathematical expression simplification without numerical approximation.
ContourPy calculates contours of 2D quadrilateral grids using C++11 algorithms wrapped in Python, offering serial and multithreaded implementations without requiring Matplotlib as a dependency.
PyTorch provides GPU-accelerated tensor computation and automatic differentiation for building and training deep neural networks in Python.
onnxruntime loads and executes Open Neural Network Exchange (ONNX) models with a focus on inference performance across CPUs and accelerators.
Install it if you have ONNX models to run in production or development.
Provides NVIDIA's NCCL runtime library for GPU collective communication operations including all-reduce, all-gather, reduce, broadcast, and reduce-scatter.
Install only if your system has compatible NVIDIA GPUs and CUDA 12 already installed.
Provides NVIDIA CUBLAS native runtime libraries for GPU-accelerated linear algebra operations in CUDA-enabled environments.
Provides NVIDIA CUDA NVRTC (NVIDIA Runtime Compilation) native runtime libraries for Python, enabling runtime compilation of CUDA kernels on x86_64 Linux, ARM64 Linux, and Windows platforms.
Provides NVIDIA's JIT LTO compiler library for Python, enabling just-in-time and link-time optimization compilation functionality for CUDA-based applications.
Provides cuDNN runtime libraries for GPU-accelerated deep neural network primitives, requiring CUDA 13 and nvidia-cublas as dependencies.
Install only if you have CUDA 13 and nvidia-cublas available; otherwise, installation will not resolve the underlying GPU dependencies.
Provides NVIDIA's Collective Communication Library (NCCL) runtime for GPU-accelerated collective operations like all-reduce, all-gather, reduce, broadcast, and reduce-scatter across multiple GPUs using PCIe, NVLink, NVswitch, InfiniBand, or TCP/IP.
However, verify that your framework (PyTorch, TensorFlow, etc.) declares it as a dependency rather than installing it standalone.
NVSHMEM provides a global address space for GPU cluster communication, enabling fine-grained GPU-initiated and CPU-initiated operations across multiple GPUs' memory via a parallel programming interface based on OpenSHMEM.
However, the unclear license status and lack of public documentation are concerns—verify licensing terms and review NVIDIA's CUDA Zone documentation before committing…
Provides NVIDIA CUSPARSE native runtime libraries for GPU-accelerated sparse matrix operations in CUDA applications.
Install only if you have NVIDIA GPU hardware and the CUDA toolkit already set up; this is a runtime library, not a standalone tool.
Provides NVIDIA CUFFT native runtime libraries for GPU-accelerated Fast Fourier Transform computations on CUDA-enabled hardware.
Provides CUDA solver native runtime libraries for GPU-accelerated linear algebra operations on NVIDIA hardware.
Provides NVIDIA CURAND native runtime libraries for GPU-accelerated random number generation in Python on Linux, Windows, and aarch64 platforms.
Provides NVIDIA CUDA Runtime native libraries for GPU-accelerated computing on Windows and Linux systems.
Provides CUDA profiling runtime libraries that enable third-party tools to access GPU profiling APIs on NVIDIA hardware.
However, it requires an NVIDIA GPU and CUDA environment, and the unclear license should be confirmed before commercial deployment.
Parse, simplify, and compare boolean expressions with variables and AND/OR/NOT operators, with support for custom tokenizers and domain-specific languages.
Provides Python bindings for NVIDIA's cuFile GPUDirect storage access libraries, enabling direct GPU-to-storage I/O without CPU involvement.
However, verify the unclear license terms before use in production, and confirm that your system has the required cuFile runtime libraries installed.
Provides a Python API for annotating events and code ranges in applications to capture performance profiling data visible in NVIDIA's Visual Profiler.
Install only if you have CUDA and Visual Profiler available; otherwise it provides no value.
TensorBoard is a web-based visualization suite for inspecting TensorFlow training runs, displaying scalar metrics, histograms, images, and computational graphs from event log files.
Install it if you train TensorFlow models and want to inspect metrics, graphs, or compare runs; it is not necessary if you use a different ML framework or logging…
pyproj provides a Python interface to PROJ, enabling cartographic projections and coordinate system transformations for geospatial applications.
Provides NVIDIA CUBLAS native runtime libraries for CUDA 12, enabling GPU-accelerated linear algebra operations in Python applications.
Provides NVIDIA CUDA NVRTC native runtime libraries for compiling CUDA code at runtime on Windows and Linux systems.
Provides NVIDIA CUSPARSE native runtime libraries for GPU-accelerated sparse matrix operations on CUDA 12 systems.
Provides cuDNN runtime libraries for GPU-accelerated deep neural network operations, requiring an NVIDIA GPU and CUDA 12 environment.
Provides NVIDIA's JIT LTO compiler library for CUDA 12, enabling just-in-time compilation and link-time optimization in GPU-accelerated applications.
Provides NVIDIA CUFFT native runtime libraries for CUDA 12, enabling GPU-accelerated Fast Fourier Transform computations in Python applications.
However, verify NVIDIA's proprietary license terms for your use case, and ensure your system has compatible NVIDIA hardware and drivers installed before proceeding.
Provides CUDA solver native runtime libraries for GPU-accelerated linear algebra operations on NVIDIA hardware.
Provides GPU profiling runtime libraries for CUDA 12, enabling third-party tools to access NVIDIA GPU profiling APIs.
Install only if a higher-level tool or framework explicitly lists it as a dependency.
Provides NVIDIA CURAND native runtime libraries for CUDA 12, enabling GPU-accelerated random number generation on supported hardware.
However, verify that your application actually requires it (it is typically pulled in transitively by higher-level libraries) and that you have an NVIDIA GPU and CUDA…
Provides NVIDIA CUDA 12 runtime native libraries for GPU-accelerated computing on Linux, Windows, and ARM64 systems.
Provides a C-based API for annotating events, code ranges, and resources in applications to enable capture and visualization through NVIDIA's Visual Profiler.
However, the aging maintenance status (435 days since last release) suggests limited active development—verify that CUDA 12.x is your target version and that the…
Provides fast data loading and serving for TensorBoard, the web application suite for inspecting TensorFlow runs and training visualizations.
TensorFlow is an open-source machine learning framework for building and training neural networks and other numerical computation models, deployable across CPUs, GPUs, TPUs, and edge devices.
Optuna is a hyperparameter optimization framework that automates the search for optimal hyperparameter values in machine learning models using a define-by-run API and state-of-the-art sampling algorithms.
Provides Python bindings for NVIDIA's cuFile GPUDirect storage access libraries, enabling direct GPU-to-storage I/O without CPU involvement for CUDA 12 environments.
PyPika is a Python API for building SQL queries programmatically using a builder pattern, eliminating string formatting and concatenation while maintaining flexibility for complex queries.
Install it if you need to build queries dynamically or support multiple SQL dialects from Python code.