Packages
OneCache provides LRU-based caching decorators for both synchronous and asynchronous Python functions, with optional per-entry TTL expiration and configurable memory limits.
Install it if you need simple LRU caching with optional TTL and don't want to manage an external cache service.
Provides Intel's oneAPI Collective Communications Library runtime for distributed training workloads, enabling scalable communication across multiple compute nodes.
Provides Intel's oneAPI Collective Communications Library for distributed training, enabling scalable communication across multiple compute nodes in machine learning and HPC workloads.
Official Python SDK for OneLogin's API, enabling programmatic management of users, roles, groups, apps, and OAuth2 authentication against OneLogin's identity platform.
Provides C and Data Parallel C++ (DPC++) interfaces to Intel oneAPI Math Kernel Library for optimized mathematical computations on Intel CPUs and GPUs.
However, be aware of the proprietary license (review Intel's terms), the manual MKLROOT configuration requirement, and that this is a library package requiring…
Provides optimized C and Data Parallel C++ (DPC++) interfaces to Intel's oneAPI Math Kernel Library for high-performance linear algebra and mathematical computing on Intel CPUs and GPUs.
Not recommended for pure Python workflows (use NumPy/SciPy instead) or if you need cross-platform portability beyond Linux and Windows.
Provides optimized C and Data Parallel C++ (DPC++) interfaces to Intel's oneAPI Math Kernel Library for high-performance mathematical computing on Intel CPUs and GPUs.
However, verify Python version compatibility first (unspecified in metadata) and review Intel's license terms, particularly if redistribution is planned.
Provides optimized C and Data Parallel C++ interfaces to Intel's oneAPI Math Kernel Library for high-performance linear algebra and mathematical computing on Intel CPUs and GPUs.
Provides C and Data Parallel C++ (DPC++) interfaces to Intel oneAPI Math Kernel Library for optimized mathematical computations on Intel CPUs and GPUs.
However, be prepared for medium install friction (three runtime dependencies) and manual MKLROOT configuration.
Provides C and Data Parallel C++ (DPC++) interfaces to Intel's oneAPI Math Kernel Library for optimized sparse linear algebra operations on Intel CPUs and GPUs.
Wrapper around the 1Password CLI that lets you query and retrieve secrets, documents, and credentials from a 1Password vault programmatically.
Programmatic Python interface to read and manage secrets, items, and vaults in 1Password via local desktop app or service account authentication.
Provides Python access to 1Password vaults through a self-hosted 1Password Connect server, allowing applications to read and write secrets programmatically.
Install it if you need programmatic access to 1Password vaults from Python.
A Python client library for the OneSignal API that enables sending personalized messages and managing customer engagement through OneSignal's messaging platform.
However, install only after verifying the license terms—the unclear license status is a real blocker for production or commercial use.
Official Python client library for the Onfido API, enabling identity verification and applicant management workflows through a generated OpenAPI interface.
Install it if you are integrating Onfido identity verification into a Python application.
Provides Python bindings to the Oniguruma regex engine via CFFI, enabling pattern matching and searching with Oniguruma's regex features.
ONNX provides an open-source format and runtime for representing and executing AI models across different frameworks and hardware platforms, enabling model interoperability and inference.
Install it if you need to work with ONNX models, export models to ONNX format, or build cross-framework inference pipelines.
Automatic Speech Recognition using ONNX models with minimal dependencies, supporting multiple modern ASR architectures and running on CPUs, GPUs, and edge devices.
Install it if you need ASR inference in Python without framework overhead.
ONNX GraphSurgeon lets you programmatically create and modify ONNX neural network models by working with an intermediate representation of graphs, nodes, and tensors.
Install it if you need to create or modify ONNX graphs in code.
onnx-ir provides an in-memory intermediate representation for ONNX models that supports the full ONNX specification, enabling graph construction, analysis, and transformation without requiring protobuf after initial conversion.
Parse, analyze, optimize, and profile ONNX neural network models with support for shape inference, quantization analysis, memory compression, and LLM-specific workloads.
onnx-weekly provides a Python package for working with ONNX (Open Neural Network Exchange) models—an open format for representing AI models with a computation graph, built-in operators, and standard data types for model interoperability and inference.
However, if you need production stability, use the stable onnx package instead.
Converts ONNX model files to JSON format, either writing to a file or returning a dictionary representation of the model structure.
However, do not rely on it for long-term maintenance or support for cutting-edge ONNX features—it is abandoned.
Converts ONNX model files to LiteRT, TensorFlow, PyTorch, TorchScript, and other formats, with support for direct conversion from LiteRT back to PyTorch.
Converts ONNX models to PyTorch modules with a simple API, supporting a limited but growing set of operations and popular model architectures.
However, verify that your specific ONNX model's operations are supported before committing to it—the converter does not support all ONNX operations, and maintenance…
Provides common utilities and functions for converting machine learning models from various AI frameworks to ONNX format, enabling interoperability between different framework converters.
Install it if you are building or using ONNX converters, especially when working with multiple frameworks or planning to leverage existing converter ecosystems.
Converts machine learning models from multiple frameworks (TensorFlow, scikit-learn, Core ML, LightGBM, XGBoost, H2O, CatBoost, Spark ML, libsvm) into ONNX format for cross-platform inference.
Applies graph-level optimizations to ONNX models, including a library of prepackaged passes for common transformations like operator fusion and constant elimination.
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.
Extends ONNX Runtime with custom operators for pre- and post-processing in vision, text, and NLP models, available as a C/C++ library with Python, Java, and C# bindings.
Runs small and large language models and multi-modal models on-device and in the cloud using ONNX Runtime as the execution backend.
Install only if you already have or plan to convert models to ONNX format; it is not a model hub or download tool.
Executes ONNX machine learning models on GPU hardware, providing inference acceleration for neural networks and other machine learning workloads.
Enables ONNX Runtime to accelerate machine learning model inference on Intel hardware (CPUs, integrated/discrete GPUs, and NPUs) using OpenVINO optimizations.
Write ONNX functions and models in Python syntax, then convert them to ONNX graphs; includes tools for optimization and pattern-based graph rewriting.
The eager-mode debugger is a real productivity gain for model development, though not for production inference.
Simplifies ONNX neural network models by running constant folding, shape inference, and graph optimization passes to reduce redundant operators and produce smaller, faster models.
OnnxSlim reduces the size and operator count of ONNX models while preserving accuracy and improving inference speed through optimization techniques.
OnnxTR extracts and recognizes text from documents (PDFs, images, webpages) using ONNX-based deep learning models, localizing and identifying words without requiring PyTorch or TensorFlow.
Async Python client for ONVIF IP cameras using asyncio and aiohttp, enabling awaitable SOAP calls to camera services like device management and PTZ control.
A command-line utility that provides developer tools for the Onyx project, including Docker container management, backend service orchestration, database administration, and OpenAPI schema generation.
Not recommended for non-Onyx use cases.
opack2 parses the opack binary format, a serialization format used in iOS and related systems, providing programmatic access to opack-encoded data.
However, do not install if you expect ongoing support, bug fixes, or feature development—the package has not been updated since 2024-12-15 and shows no signs of…