Packages
Provides standard schema, statistics, and problem statement representations for machine learning metadata that can be used with TensorFlow for data validation, exploration, and transformation.
Provides quantization, pruning, and clustering techniques to reduce model size and improve inference performance for TensorFlow and Keras models.
TensorFlow Probability provides probabilistic modeling, statistical inference, and Bayesian machine learning tools integrated with TensorFlow, including distributions, variational inference, MCMC sampling, and neural network layers with uncertainty quantification.
TensorFlow Recommenders provides a Keras-based library for building recommender system models, covering the full workflow from data preparation through training, evaluation, and deployment.
Provides Python client APIs to communicate with TensorFlow Serving, a production machine learning model serving system using gRPC for deployment and inference.
Install only if you already have or plan to run a TensorFlow Serving instance; it is a client library, not a standalone serving system.
TensorFlow Text provides text preprocessing operations and tokenizers that run within the TensorFlow computation graph, enabling consistent text handling across training and inference without external preprocessing scripts.
Install it if you are building NLP models with TensorFlow and need tokenization or text normalization; ensure your tensorflow-text version matches your TensorFlow…
TensorFlow Transform preprocesses data with full-pass operations like normalization, vocabulary generation, and bucketing, exporting a reusable TensorFlow graph for consistent training and serving.
Converts TensorFlow models to TensorFlow.js format for browser and Node.js deployment, with CLI tools and a wizard for model conversion workflows.
Serializes and deserializes PyTorch modules and tensors to/from HTTP, HTTPS, S3, Redis, and local filesystem endpoints with streaming support for fast model loading.
Tensorlake provides a Python SDK for building agentic applications using isolated Firecracker MicroVM sandboxes and a serverless orchestration runtime with automatic scaling.
However, verify the unclear license status before production use, and confirm pricing and scaling limits align with your workload.
TensorLy performs tensor decomposition, tensor learning, and tensor algebra operations with a pluggable backend system that lets you compute using NumPy, PyTorch, JAX, TensorFlow, CuPy, or Paddle.
TensorRT compiles and optimizes deep learning models for deployment on NVIDIA GPUs, reducing latency and memory footprint through layer fusion, quantization, and kernel auto-tuning.
However, installation requires CUDA toolkit, system-level build tools, and the TensorRT GA build v11.2.1.2; proprietary licensing requires license review for…
Provides Python bindings for NVIDIA TensorRT 11, enabling high-performance deep learning inference on CUDA 12 GPUs through optimized model compilation and execution.
Provides Python bindings for NVIDIA TensorRT 11.2.1.2 compiled for CUDA 12, enabling deep learning inference acceleration on x86_64 Linux and Windows systems.
Provides NVIDIA TensorRT libraries for CUDA 12 environments, enabling optimized deep learning model inference on NVIDIA GPUs.
Provides Python bindings for NVIDIA TensorRT, a deep learning inference library that compiles and optimizes neural network models for deployment on NVIDIA GPUs.
Provides Python bindings for NVIDIA TensorRT 11.2.1.2 compiled for CUDA 13, enabling deep learning inference acceleration on compatible GPUs.
Provides NVIDIA TensorRT libraries for GPU-accelerated deep learning inference, packaged for CUDA 13 environments.
TensorStore reads and writes large multi-dimensional arrays across multiple storage backends (local filesystem, cloud storage, S3, HTTP) with a uniform API supporting zarr and N5 formats.
Install it if you need a uniform API for multi-backend array storage; skip it if your arrays fit in local memory and you're not crossing storage boundaries.
Tentaclio opens and manages streams across multiple protocols (file, FTP, SFTP, S3, HTTP/HTTPS) and database connections using a unified URL-based interface, with automatic credential injection and pandas integration.
Bundles PostgreSQL connectivity and schema support for the tentaclio data access framework, providing a ready-to-use PostgreSQL backend.
Provides S3 storage integration for tentaclio by bundling tentaclio and boto3 as a single installable package.
Provides Python bindings to execute SQL scripts and procedural logic against Teradata databases with DevOps-focused features like query banding and logging.
teradataml provides Python access to analytic functions running on Teradata Vantage, enabling data analysis, machine learning, and data transformation without writing SQL.
However, verify the Teradata License Agreement terms for your use case first, and confirm that optional dependencies (scikit-learn, lightgbm) are available if you…
Teradata ModelOps Client provides a CLI and SDK for managing machine learning model lifecycle—training, evaluation, deployment, and versioning—within Teradata's data platform.
However, the proprietary license restricts use to internal purposes tied to a Teradata database license, and Windows users must manually install OpenSSL.
A PEP-249 compliant Python database driver that enables applications to connect to and query Teradata Database systems.
However, review the proprietary Teradata License Agreement before use in commercial or open-source redistribution contexts, and note that beta features carry no…
Provides a SQLAlchemy dialect that enables Python applications to connect to and query Teradata databases using SQLAlchemy's ORM and SQL expression language.
However, the proprietary license is unclear and maintenance is aging (241 days since last release).
Adds ANSI color and text formatting (bold, underline, blink, reverse, etc.) to terminal output, with support for named colors, RGB tuples, and automatic terminal capability detection.
Install it if you need ANSI color formatting in any Python CLI, script, or logging system.
Terminado provides a Tornado websocket backend that connects server-side terminal processes to the Xterm.js browser-based terminal emulator, enabling interactive shell access over the web.
Terminal-Bench provides a benchmark suite and execution harness for evaluating AI agents' ability to complete real-world terminal tasks autonomously, from code compilation to server setup.
Renders formatted ASCII tables in terminal output from nested lists of strings, with support for multi-line rows and automatic column alignment.
Renders formatted tables in terminal output from nested lists of strings, supporting multi-line rows with ASCII or other box-drawing styles.
Renders animated visual effects for text displayed in the terminal, supporting character movement, color gradients, and complex animations through an inline effects engine.
Install it if you need animated text in CLI tools or want to experiment with terminal graphics; skip it if your application requires cross-platform GUI rendering or…
termplotlib renders line plots, histograms, and bar charts directly in the terminal using ASCII or Unicode characters, with a matplotlib-like API.
terraform-compliance is a BDD-based test framework that validates Terraform infrastructure code against security and compliance policies before deployment, using human-readable Gherkin syntax to define and enforce infrastructure standards.
A wrapper script that runs Terraform against LocalStack by automatically configuring AWS provider endpoints to point to a local LocalStack instance.
Install it if you use Terraform and LocalStack together for local development or testing; skip it if you don't use LocalStack or don't test Terraform locally.
Provides Python bindings to Tesla's Fleet API for controlling vehicles and energy sites, with support for signed commands, local Bluetooth communication, and routing/failover across cloud and local backends.
Install it if you're building Tesla integrations; skip it if you only need read-only telemetry or cannot upgrade to Python 3.13.
An async Python library for interacting with the Tesla API, designed to enable integration with Home Assistant and other automation platforms.
Computes dark matter halo concentration parameters from simulated particle data using Voronoi tessellation, a non-parametric technique that does not assume spherical symmetry.
tesserocr wraps Tesseract's C++ OCR engine via Cython, extracting text and metadata from images with support for Pillow objects and concurrent processing through Python's threading module.