by Team PyTorch

Welcome to the thriving PyTorch ecosystem, where a wealth of tools and libraries await, purpose-built to elevate your experience in deep learning as a developer or researcher. The Ecosystem Tools pages host many projects from experts spanning academia, industry, application development, and machine learning.

Initially, PyTorch aimed to establish a thriving community, enabling developers to access each other’s tools, engage in meaningful discussions, and explore the wealth of resources available within the community.

Today, the PyTorch ecosystem has grown to feature over 100 projects tailored to your needs, providing robust support, enhanced speed, and effortless integration with PyTorch. If your project aligns with our mission, we invite you to submit it and join this dynamic ecosystem.

New this month, we’ve moved all of our Ecosystem blogs over to our website to host a space where our community can show off the latest innovations with our users. Read on to hear about the latest projects in the ecosystem!

Explore the Latest Tools and Frameworks in the Ecosystem

As we continue into 2024, we’re thrilled to showcase an impressive array of ecosystem tools that significantly enrich the PyTorch community. These tools cover a wide range of domains, including pose estimation, profiling, and even quantum computing. Let’s explore each one to witness firsthand how they are reshaping the PyTorch landscape, opening up exciting possibilities for developers.


Anomalib is a deep learning library that aims to collect state-of-the-art anomaly detection algorithms for benchmarking on both public and private datasets. Anomalib provides several ready-to-use implementations of anomaly detection algorithms described in the recent literature, as well as a set of tools that facilitate the development and implementation of custom models. The library has a strong focus on image-based anomaly detection, where the goal of the algorithm is to identify anomalous images, or anomalous pixel regions within images in a dataset. Anomalib is constantly updated with the latest algorithms and training/inference extensions.


Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you’re looking for a simple inference solution or training your own diffusion models, Diffusers is a modular toolbox that supports both.


Pomegranate is a versatile machine learning library that integrates seamlessly with PyTorch. It provides a wide range of probabilistic models and tools for probabilistic modeling tasks. Pomegranate empowers users to build complex models such as hidden Markov models (HMMs), Bayesian networks, and Gaussian mixture models (GMMs). By combining the strengths of PyTorch and Pomegranate, developers can leverage the power of deep learning and probabilistic modeling to tackle various machine learning challenges.


PyPose is a PyTorch-based library designed for pose estimation tasks. With PyPose, developers can efficiently train and deploy models for human pose estimation, a fundamental computer vision problem. By leveraging PyTorch’s flexibility and performance, PyPose simplifies the process of building accurate pose estimation models. Its intuitive APIs and pre-trained models make it an excellent choice for researchers and developers exploring human pose estimation applications.


A python toolbox/library for data mining on partially-observed time series with PyTorch, including SOTA models supporting tasks of imputation, classification, clustering, and forecasting on incomplete (irregularly-sampled) multivariate time series with missing values.

OctoML Profiler

OctoML Profiler is a performance profiling tool that aids in optimizing PyTorch models. This tool helps developers identify performance bottlenecks and inefficiencies within their deep learning models. By providing insights into memory usage, compute time, and data movement, the OctoML Profiler enables developers to fine-tune their models for improved efficiency. With this valuable feedback, developers can optimize their models for deployment on various hardware platforms.

Open Compass

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features include: Comprehensive support for models and datasets, efficient distributed evaluation, diversified evaluation paradigms, modular design with high extensibility and experiment management and reporting mechanism.


Renate is a PyTorch-based library for neural architecture search (NAS). It simplifies the process of automatically searching for optimal neural network architectures tailored to specific tasks. Renate leverages techniques like reinforcement learning and evolutionary algorithms to efficiently explore the architecture space. By using Renate, developers can save significant time and resources while discovering highly performant models.


RoMa is a standalone library to handle rotation representations with PyTorch (rotation matrices, quaternions, rotation vectors, etc). It aims for robustness, ease-of-use, and efficiency.


Substra is an open source federated learning (FL) software. It enables the training and validation of machine learning models on distributed datasets. It provides a flexible Python interface and a web application to run federated learning training at scale. Substra’s main usage is in production environments. It has already been deployed and used by hospitals and biotech companies. Substra can also be used on a single machine to perform FL simulations and debug code.


TorchQuantum is a powerful library that combines the PyTorch framework with quantum computing concepts. It enables developers to explore quantum machine learning algorithms and build hybrid classical-quantum models. By integrating the principles of quantum computing into PyTorch, TorchQuantum opens up new possibilities for solving complex problems that traditional deep learning approaches may struggle with.


The TIAToolbox (Text-Image-Augmentation Toolbox) is a PyTorch library designed to augment text and image data for deep learning tasks. It offers a comprehensive set of tools for data augmentation, including transformations, noise injection, and image/text synthesis. By applying TIAToolbox, developers can enrich their training datasets, improve model generalization, and enhance the robustness of their deep learning models.


torchdistill is a coding-free framework built on PyTorch for reproducible deep learning and knowledge distillation studies. The framework is designed to enable users to design experiments by declarative PyYAML configuration files and supports high-level module abstractions.


TorchOpt is a PyTorch library focused on optimization algorithms for deep learning. It provides a collection of state-of-the-art optimization techniques, such as stochastic gradient descent (SGD) variants, adaptive learning rate methods, and optimization schedules. TorchOpt empowers developers to fine-tune their models efficiently, converge faster, and achieve better performance in various deep learning tasks.


USB, or Unified Speech-to-Text Benchmark, is a PyTorch-based toolkit for training and evaluating speech recognition models. It provides standardized datasets and evaluation metrics to facilitate fair and accurate comparisons between different speech recognition architectures. By using USB, researchers and developers can benchmark their models against state-of-the-art systems and drive advancements in the field of automatic speech recognition.


Zeus is the current state-of-the-art in deep learning energy measurement and optimization. It has monitor components that allow users to measure GPU energy consumption and optimizer components that automatically optimize DNN or GPU knobs based on measurements from the monitor component.

Be Part of Our Ecosystem

Our diverse ecosystem tools are instrumental in PyTorch’s success.. They provide essential support for tasks such as pose estimation, probabilistic modeling, performance profiling, model interpretability, speech recognition, quantum computing, data augmentation, optimization, and neural architecture search.

Leveraging these tools empowers developers and researchers to accelerate their deep learning workflows and unlock new possibilities in the field of AI.

Have a tool that would be a good fit for the PyTorch Ecosystem? If you can answer the below questions, we’d love for you to submit your tool for review.

  1. Does your project complement PyTorch, enhancing user experience, introducing new capabilities, or accelerating training and inference processes?
    • Examples could include visualization tools, a kernel library or a framework that sits on top to enable research in a particular area such as NLP.
  2. Is the project ready for broad developer usage?
    • For example, is the project stable, will it be maintained, and is there adequate supporting infrastructure, documentation, and technical support to allow a developer to successfully use it?

Thank you to all of our contributors and collaborators in our ecosystem! Here’s to a great 2024.