Packages
A REST API client library for interacting with F5 BIG-IP iControl REST endpoints from Python code.
F5-TTS generates natural-sounding speech from text using flow-matching diffusion transformers, with support for multi-speaker and multi-style synthesis from reference audio.
However, it is not suitable for CPU-only environments due to inference speed, and requires careful PyTorch setup for your specific GPU architecture (NVIDIA, AMD,…
f90nml reads, writes, and modifies Fortran namelist files, converting them to and from Python dictionaries and other formats like JSON and YAML.
Provides a lightweight Flash-Attention-3 forward-only kernel compiled to a Python wheel, optimized for inference workloads on CUDA hardware without backward pass or optional features.
fab-classic provides SSH-based remote command execution and file transfer for deployment and systems administration tasks, built on paramiko.
Not recommended for new projects.
FabIO reads and writes 2D X-ray detector images in 30 different formats from vendors like Mar, Dectris, ADSC, and Hamamatsu, exposing image data as numpy arrays and headers as Python dictionaries.
Fable Library for Python provides runtime support and type definitions for F# code compiled to Python, including Rust-based extensions for unsigned integer types and performance-critical operations.
Fabric executes shell commands remotely over SSH and returns results as Python objects, building on Invoke for command execution and Paramiko for SSH protocol support.
Install it if you need programmatic remote command execution.
Abstracts Microsoft Fabric API interactions for code-first CI/CD automation, letting developers deploy and manage Source Controlled Fabric workspaces without direct API calls.
Fabric executes shell commands remotely over SSH and returns Python objects, building on Invoke and Paramiko to provide a high-level interface for remote command execution and system administration tasks.
Install it if you need to automate tasks across remote systems from Python code.
Fabric3 is a Python 2.7 and 3.4+ compatible fork of Fabric that enables remote command execution and application deployment over SSH from Python code.
Fabricatio is a Python library for building LLM-powered multi-agent applications using an event-driven architecture, with Rust-backed performance for core operations and PyO3 bindings.
Not recommended if you need a mature, heavily documented framework—the project is young (first release February 2025) and has an extensive TODO list indicating…
Face is a command-line application framework that builds CLI parsers and interfaces from Python code, handling argument parsing and command structure for you.
Detects 2D and 3D facial landmarks from images using deep learning, supporting multiple face detection backends and GPU acceleration via PyTorch.
Install it if facial geometry extraction is core to your application.
Detects, locates, and identifies faces in images using deep learning, with both Python API and command-line interface for batch processing.
Provides pre-trained deep learning models for face detection, recognition, and encoding used by the face_recognition package.
Provides Python bindings to Facebook's Business APIs, bundling Marketing API, Pages, Business Manager, and Instagram endpoints into a single SDK for managing ad campaigns, accounts, and related business operations.
A Python client library for the Facebook Graph API that handles authentication and API requests to Facebook services.
Python client library for controlling iOS devices via Facebook's WebDriverAgent, enabling automated UI testing and interaction with iOS apps through a network API.
However, install it only if you already have WebDriverAgent running on your target iOS device or simulator—the package is a client library, not a standalone tool.
Provides pretrained PyTorch models for face detection using MTCNN and face recognition using Inception ResNet V1, with automatic model downloading and caching.
Install it if you need pretrained face detection and recognition models and can accept that bug fixes or compatibility updates may lag behind new PyTorch releases.
Generates summary statistics for dataset features and creates interactive visualizations of data distributions, supporting both numeric and categorical columns from pandas DataFrames or TensorFlow records.
However, it is abandoned and unmaintained since May 2023, so compatibility with current versions of numpy, pandas, protobuf, and Jupyter is uncertain.
Facexlib provides a collection of PyTorch-based face analysis functions including detection, alignment, recognition, parsing, matting, head pose estimation, tracking, and quality assessment.
However, verify that the original licenses of the specific functions you use align with your project, and be aware that no active development means you may need to…
A Python client library for connecting to and sending commands to Factorio game servers via RCON protocol, with optional async support.
factory_boy replaces static test fixtures with declarative factories that generate customized test objects on demand, supporting multiple build strategies and ORM integration.
Install it if you write tests for ORM-backed applications or complex object hierarchies.
Generates, validates, and extracts Factur-X, Order-X, and UBL electronic invoicing XML from PDF documents, supporting European e-invoicing standards EN 16931 and ZUGFeRD.
Install it if you need to generate, validate, or extract European e-invoicing standards (Factur-X, Order-X, UBL) or integrate EN 16931 compliance into invoice workflows.
Provides Font Awesome 6.2.0 icons as SVG elements for use in Shiny for Python applications.
The main caveat is dormant maintenance—no updates since 2024-01-16—so it is suitable for stable use but not for projects requiring active support or newer Font…
Provides pre-trained transformer protein language models (ESM-2, ESMFold, ESM-1v, MSA Transformer, ESM-IF1) for protein structure prediction, embedding generation, variant effect prediction, and inverse folding directly from sequence.
However, the repository is archived and unmaintained since February 2024, so there will be no bug fixes or compatibility updates.
Provides pretrained machine learning models for predicting molecular and materials properties, integrated with ASE for structure relaxation, molecular dynamics, and quantum chemistry calculations.
Fairlearn assesses and mitigates fairness issues in machine learning models by providing metrics to identify which groups are negatively impacted and algorithms to reduce unfairness across various AI tasks.
Install it if fairness assessment or mitigation is part of your model development workflow.
FairScale extends PyTorch with distributed training primitives and optimizations for scaling model training across multiple machines or GPUs.
Faiss provides GPU-accelerated similarity search and clustering for dense vectors, supporting exact and approximate nearest-neighbor queries on datasets from memory to billions of vectors.
fake-factory is a deprecated package that generated fake data for bootstrapping databases, creating test documents, and anonymizing production data.
Generates realistic User-Agent strings and HTTP headers locally without making internet requests, supporting customization by browser (Chrome, Firefox, Opera) and OS (Windows, macOS, Linux).
Generates realistic-looking HTTP request headers with randomized user agents, accept languages, and referrer fields that mimic real browser behavior.
Generates random or browser-specific user-agent strings from a pre-packaged database of real-world agents, with filtering by browser, OS, platform type, and minimum version.
However, the repository is now archived and abandoned, so the bundled user-agent database will not receive updates for new browser versions.
Faker generates realistic fake data—names, addresses, emails, phone numbers, and more—for testing, database bootstrapping, and data anonymization across multiple locales.
Install it if you need realistic fake data for tests, prototyping, or anonymization; skip it only if you have no use for generated test data.
Generates fake E164-formatted phone numbers for use with the Faker library in testing and development.
Generates fake data for educational institutions and academic contexts by extending Faker with education-specific providers like institution names.
However, verify that its methods meet your needs and that it remains compatible with your Faker version, since no updates are planned.
Adds enum value generation to the Faker library, allowing you to randomly select from Python enum members during test data generation.
Extends Faker with methods to generate realistic fake nonprofit organization data, including nonprofit names and related attributes for testing and development.