Packages
telepath serializes Python objects to JSON-compatible format for transmission to JavaScript, then deserializes them in the browser with matching JavaScript implementations, enabling bidirectional Python-JavaScript data exchange.
TeleSign SDK provides Python bindings to send SMS and voice messages, verify phone numbers, and perform identity checks via the TeleSign communications platform API.
Python SDK for Telesign's user verification, digital identity, and omnichannel communications APIs, handling authentication and requests to Telesign services.
Install only if you have a Telesign account and need to integrate their verification or messaging services.
Telethon is an asyncio-based Python library that implements the MTProto protocol to interact with Telegram's API as a user account or bot, providing an alternative to Telegram's official bot API.
Telnet server, client, and protocol library for Python 3.9+ with asyncio and blocking API support, including CLI utilities and a backport of the removed telnetlib module for Python 3.13+.
Install it if you need telnet support, are migrating from the standard library, or are building MUD/BBS infrastructure.
Provides a Python client library for the Telnyx REST API, offering both synchronous and asynchronous interfaces with full type definitions for requests and responses.
Provides a unified Python API to generate temporary email addresses and retrieve messages from multiple disposable email services, abstracting away the differences between individual provider implementations.
However, it depends entirely on third-party sites remaining accessible and unchanged.
Temporarily override environment variables within a scoped context, automatically restoring the original state when the block exits.
Tempita is a lightweight templating engine that processes text templates with variable substitution, conditionals, loops, and template inheritance using a simple {{...}} syntax.
Provides utilities, constants, and routines for working with dates, times, and scheduling in Python, including timing measurement, event scheduling, and UTC-aware datetime handling.
Temporal Python SDK provides a framework for building durable, distributed workflows and activities that execute asynchronously and survive failures through automatic retry and state replay.
Tenacity is a retry library that adds automatic retry behavior to functions and code blocks, with configurable stop conditions, wait strategies, and exception handling.
Integrates Celery task queuing with multi-tenant Django applications to run asynchronous tasks within isolated tenant schemas, automatically managing schema context across task execution.
Install only if you are already committed to both Celery and a multi-tenant architecture; it adds no value as a standalone tool.
Official SDK for accessing Tencent Cloud services from Python applications via HTTP requests.
Provides Python bindings and common utilities for accessing Tencent Cloud services like CVM and CBS through the official SDK.
Provides Python bindings to interact with Tencent Cloud's WSA (Workspace Security Architecture) service and related cloud infrastructure APIs like CVM and CBS.
Tendo provides utility functions for Python that fill gaps in the standard library, including Unicode file handling, console logging coloring, symlink support on Windows, process output redirection (tee), and an improved execfile implementation.
TenSEAL performs homomorphic encryption operations on tensors, enabling arithmetic and linear algebra on encrypted data without decryption using the BFV and CKKS encryption schemes.
A high-performance grep-compatible command-line search tool with Rust-backed execution, GPU acceleration support, and intelligent routing via AST/NLP analysis.
However, verify the license terms before use—the metadata does not clarify licensing.
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…
Provides fast data loading and serving for TensorBoard, the web application suite for inspecting TensorFlow runs and training visualizations.
Provides a TensorBoard plugin and standalone server for profiling and visualizing ML model performance across multiple devices, showing execution timelines, memory usage, and computational graphs.
The main gotcha is the internet requirement for full UI rendering; if you profile in offline environments, verify that limitation first.
Adds the What-If Tool, an interactive visual interface for exploring and understanding ML model behavior, as a plugin to TensorBoard.
However, do not rely on this package for new features or bug fixes; it is abandoned and has not been updated since 2022-01-05.
Writes experiment metrics, graphs, embeddings, and media to TensorBoard event files without requiring TensorFlow, supporting multiple tensor frameworks and cloud storage backends.
TensorDict is a batched, nested dictionary container that behaves like a PyTorch tensor, allowing you to slice, reshape, move, and perform arithmetic on structured data while keeping all nested tensors synchronized.
TensorDict is a batched, nested dictionary container for PyTorch tensors that behaves like a single tensor—enabling slicing, reshaping, device transfer, and arithmetic operations across all leaves simultaneously while maintaining a shared batch size.
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.
TensorFlow for aarch64 is a machine learning framework that enables numerical computation and deep learning across CPUs, GPUs, TPUs, and edge devices on ARM-based systems.
TensorFlow Addons provides experimental operators, layers, metrics, losses, and optimizers that extend core TensorFlow with functionality not yet ready for the main library.
However, the dormant status (no releases in 990 days) means you should verify compatibility with your current TensorFlow and Python versions before committing.
TensorFlow CPU provides a machine learning and numerical computation framework for building and training models on CPU-based systems, with support for Python 3.10 through 3.13.
TensorFlow CPU for AWS is a machine learning framework optimized for numerical computation on CPU-based AWS infrastructure, supporting deployment across diverse platforms and devices.
However, verify that this AWS-specific variant offers advantages over the standard TensorFlow CPU distribution, and check whether version 2.15.1 (released 2024-03-14)…
TensorFlow Data Validation (TFDV) computes summary statistics, detects anomalies, and generates data schemas for machine learning datasets at scale using Apache Beam and TensorFlow.
Provides access to many public datasets as tf.data.Datasets, handling download, preparation, and loading for TensorFlow workflows.
Install it if you work with TensorFlow and need quick access to public datasets; skip it only if you manage datasets entirely through custom pipelines.
Trains and deploys decision forest models (Random Forests, Gradient Boosted Trees) within TensorFlow for classification, regression, and ranking tasks.
Not recommended for Windows users without WSL, or for projects not already committed to TensorFlow.
TensorFlow Estimator provides a high-level API for building and training machine learning models, encapsulating training, evaluation, prediction, and model export workflows.
TensorFlow Graphics provides differentiable graphics and geometry layers—cameras, reflectance models, spatial transformations, mesh operations—that integrate into neural networks for 3D vision and graphics tasks.
Downloads and loads pre-trained TensorFlow SavedModels from TensorFlow Hub (now redirected to Kaggle Models) for reuse in TensorFlow programs with minimal code.
However, be aware that maintenance is dormant and many models from the original tfhub.dev have been deleted; verify your models exist before committing to this…
Intel-optimized TensorFlow for Windows that accelerates numerical computation and machine learning workloads using oneDNN primitives for Intel architecture.
Install only if you are on Windows; the medium dependency footprint is standard for TensorFlow and the oneDNN environment variable must be set explicitly to activate…
Extends TensorFlow with support for file systems and data formats not built into TensorFlow, including HTTP/HTTPS access and automatic decompression for remote datasets.
Install only if you have a specific need for extended I/O capabilities; it is not required for standard TensorFlow workflows.
Provides Google Cloud Storage filesystem support for TensorFlow, enabling direct reading and writing of data from GCS buckets within TensorFlow pipelines without local downloads.
Install only if your TensorFlow version matches the compatibility table (0.37.1 requires TensorFlow 2.16.x).