Packages
Computes Short Term Objective Intelligibility (STOI) as a PyTorch loss function for training speech enhancement models, wrapping the classical and extended STOI metrics in differentiable form.
However, the dormant maintenance status (no releases for 683 days) and the documented approximation—not exact replication—of the original metric mean you should…
Integrates PyTorch profiling data with TensorBoard, providing GPU timeline tracing and performance diagnostics for ML workloads through a TensorBoard plugin interface.
However, the last release was in October 2023; verify compatibility with your current PyTorch version before relying on it for new projects.
TorchAO applies quantization and sparsity techniques to PyTorch models for faster training and inference with reduced memory usage, working natively with torch.compile() and FSDP2.
Provides PyTorch-based audio processing, transforms, and dataloaders for machine learning tasks, with GPU acceleration and autograd support for trainable audio features.
torchax is a PyTorch backend that runs PyTorch code on Google Cloud TPUs via JAX, with bidirectional interoperability allowing you to call JAX functions from PyTorch and vice versa.
However, it is in alpha (Development Status :: 3), so expect incomplete operation coverage and potential API changes.
Provides differentiable GPU-capable solvers for controlled differential equations (CDEs), enabling Neural Controlled Differential Equation models for irregular time series with support for backpropagation and memory-efficient adjoint methods.
However, proceed with caution: the project is aging (last release October 2021, no recent commits), so compatibility with the latest PyTorch versions and long-term…
TorchCodec decodes and encodes videos, audio, and images to and from tensors on CPU and CUDA GPUs, wrapping FFmpeg for video/audio and providing native image codecs.
PyTorch implementation of the CREPE pitch tracker that estimates fundamental frequency and periodicity from audio signals using pre-trained neural network models.
Provides PyTorch-based ODE solvers with backpropagation support through the adjoint method, enabling differentiable solutions to ordinary differential equations with constant memory cost.
TorchEval provides a collection of PyTorch model metrics with both functional and class-based interfaces, supporting single-process, multi-GPU, and distributed training scenarios.
TorchFCPE estimates fundamental frequency (pitch) from audio using a PyTorch implementation of Fast Context-based Pitch Estimation, suitable for speech and music analysis.
However, maintenance is aging—the last release was March 2024 with no recent commits—so expect limited support for bugs or compatibility issues with newer PyTorch…
Provides per-step fault tolerance for PyTorch distributed training, allowing jobs to recover from worker failures without restarting the entire training process.
However, verify the BSD 3-Clause license applies to your use case (metadata is unclear), and be aware that medium install friction (Rust compilation, system…
TorchGeo provides PyTorch datasets, samplers, transforms, and pre-trained models for geospatial and remote sensing data, handling multispectral satellite imagery with automatic coordinate reference system alignment.
Generates detailed summaries of PyTorch neural network models, showing layer names, input/output shapes, parameter counts, and computational operations—similar to Keras's model.summary() API.
Install it if you develop or analyze PyTorch models and want structured visibility into layer structure and computational cost.
TorchIO reads, preprocesses, augments, and samples 3D medical images for deep learning with PyTorch, offering both standard computer vision transforms and domain-specific medical imaging operations like MRI artifact simulation.
Install it if you're building medical imaging models with PyTorch.
Provides PyTorch implementations of librosa audio feature extraction functions, enabling GPU-accelerated computation of spectrograms, mel-spectrograms, and STFT/ISTFT transforms with numerical compatibility to the original librosa.
However, verify that PyTorch is available in your environment, and be aware that no updates have shipped since 2023-02-21—if you encounter bugs or incompatibilities…
Torchmetrics provides a collection of PyTorch metrics implementations with automatic batch accumulation and multi-device synchronization, designed for distributed training workflows.
Counts multiply-accumulate operations (MACs) in PyTorch models by tracing the computation graph, providing both aggregate and per-operator breakdowns.
TorchRL is a PyTorch-native toolkit for building reinforcement learning systems with composable components for environments, policies, collectors, replay buffers, and loss functions.
However, verify the license status before committing to proprietary use, and ensure your target platform has compatible wheels (Python 3.10–3.14, macOS/Linux/Windows).
torchrunx distributes PyTorch training functions across multiple GPUs or machines using a functional API, eliminating the need for CLI-based launchers.
Solves stochastic differential equations (SDEs) with GPU support and efficient backpropagation through PyTorch, enabling gradient-based learning of SDE-based models.
Provides pretrained neural network models for image super-resolution (upscaling low-resolution images to higher resolution) using PyTorch, including EDSR, CARN, RDN, RCAN, and NinaSR architectures.
However, verify that pretrained weights remain accessible and that it works with your PyTorch version—no updates have landed since August 2022, so compatibility with…
Displays a Keras-style summary of PyTorch neural network models, showing layer types, output shapes, and parameter counts without requiring manual inspection of the model structure.
torchtext provides text datasets, preprocessing transforms, and pre-trained models for natural language processing tasks in PyTorch, including tokenizers, vocabulary management, and example workflows.
However, the abandoned maintenance status (paused since September 2023, archived repository) means no new features or dataset support will be added.
torchtitan is a PyTorch-native platform for training large generative AI models at scale, providing distributed training infrastructure with support for multiple parallelism techniques, checkpointing, and quantization.
TNT provides training utilities and tools for PyTorch models, including abstractions for common training workflows, distributed training support, and monitoring integrations.
However, the Pre-Alpha status and 814-day release gap mean the API may shift and updates are infrequent—verify that its abstractions match your workflow before…
torchtune is a PyTorch library for fine-tuning, post-training, and experimenting with large language models using recipes for SFT, knowledge distillation, DPO, PPO, GRPO, and quantization-aware training.
Adds type annotations for PyTorch tensor shape, dtype, layout, and dimension names with optional runtime checking via typeguard.
However, be aware that the package is aging and the author recommends jaxtyping for new projects due to better static type checker support.
Torchvision provides pre-built datasets, model architectures, and image transformation utilities for computer vision tasks within the PyTorch ecosystem.
Generates visual diagrams of PyTorch neural network computation graphs and autograd traces, showing layer connections and tensor flow through the model.
However, maintenance is dormant (620 days since last release), so verify compatibility with your PyTorch version before relying on it for production workflows.
TorchX is a universal job launcher that submits PyTorch training and ML pipeline workloads to multiple schedulers including Kubernetes, Slurm, AWS Batch, Docker, and local execution.
TorchXRayVision provides pre-trained deep learning models and unified dataset interfaces for chest X-ray analysis, enabling rapid classification and feature extraction on medical imaging data.
Tornado is a Python web framework and asynchronous networking library that uses non-blocking I/O to handle tens of thousands of concurrent connections, suitable for long polling, WebSockets, and long-lived client connections.
Install it if you are building WebSocket applications, real-time systems, or APIs that must scale to many simultaneous clients without thread-per-connection overhead.
Tortoise ORM is an async-native Object-Relational Mapper for Python that lets you define and query databases using high-level object-oriented code instead of raw SQL, with support for SQLite, MySQL, PostgreSQL, Microsoft SQL Server, and Oracle.
Install it if you are building async applications and prefer ORM abstraction over raw SQL; skip it if you need synchronous database access or prefer a different query…
A Python SDK for interacting with Volcengine's TOS (Tinder Object Storage) service, providing bucket and object operations like create, upload, download, and delete.
However, resolve the unclear license terms before use in commercial projects, and investigate the high install friction to ensure it does not block your deployment…
Enables visualization of TOSA neural network model files within the Model Explorer interface, acting as an adapter plugin for the ai-edge-model-explorer framework.
TOSA Tools provides serialization, reference implementation, and optional MLIR translation for the Tensor Operator Set Architecture specification, enabling you to read, write, and work with TOSA graphs and operators.
A Python client library for authenticating with and controlling TotalConnect2 alarm systems, supporting arming/disarming and status queries.
Install it if you need programmatic control of a TotalConnect alarm system from Python—particularly if you're building a Home Assistant integration or custom automation.
Total Perspective Vortex (TPV) provides dynamic job routing rules for Galaxy that direct tools, users, and roles to appropriate compute destinations based on configurable YAML policies.
Install it if Galaxy's static job configuration is limiting your deployment; skip it if you run a minimal single-machine Galaxy.
Automated segmentation of anatomical structures in CT and MR medical images, identifying and labeling 117 classes in CT or 50 classes in MR scans using deep learning.