Search Results for "performance"
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Showing 107 open source projects for "performance"
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NannyML
Detecting silent model failure. NannyML estimates performance
NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data science, empowering data scientist to quickly understand and automatically detect silent model failure. ... -
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Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. ... -
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OpenRLHF
An Easy-to-use, Scalable and High-performance RLHF Framework
OpenRLHF is an easy-to-use, scalable, and high-performance framework for Reinforcement Learning with Human Feedback (RLHF). It supports various training techniques and model architectures. -
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BoxMOT
Pluggable SOTA multi-object tracking modules for segmentation
...The framework supports integration with detection, segmentation, and pose estimation models that produce bounding box outputs. It also includes evaluation tools and benchmarking pipelines that allow researchers to test tracking performance on standard datasets such as MOT17 and MOT20. The system offers different performance modes that balance computational efficiency with tracking accuracy depending on the application requirements. -
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face.evoLVe
High-Performance Face Recognition Library on PaddlePaddle & PyTorch
face.evoLVe is a high-performance face recognition library designed for research and real-world applications in computer vision. The project provides a comprehensive framework for building and training modern face recognition models using deep learning architectures. It includes components for face alignment, landmark localization, data preprocessing, and model training pipelines that allow developers to construct end-to-end facial recognition systems. -
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StatsForecast
Fast forecasting with statistical and econometric models
...Its internal implementation leverages numba to compile performance-critical code to optimized machine-level instructions, which makes the models much faster than many traditional Python counterparts. -
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Intel Extension for PyTorch
A Python package for extending the official PyTorch
Intel® Extension for PyTorch* extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) Vector Neural Network Instructions (VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs through the PyTorch* xpu device. -
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FlashAttention
Fast and memory-efficient exact attention
FlashAttention is a high-performance deep learning optimization library that reimplements the attention mechanism used in transformer models to be significantly faster and more memory-efficient than standard implementations. It achieves this by using IO-aware algorithms that minimize memory reads and writes, reducing the quadratic memory overhead typically associated with attention operations. -
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skfolio
Python library for portfolio optimization built on top of scikit-learn
...It supports a wide range of allocation methods, from classical mean-variance optimization to modern techniques that rely on clustering, factor models, and risk-based allocations. The framework also includes tools for evaluating portfolio performance under different market conditions, enabling users to test robustness and reduce the risk of overfitting. -
11
RF-DETR
RF-DETR is a real-time object detection and segmentation
...Developed by Roboflow, the project builds upon modern vision transformer backbones such as DINOv2 to achieve strong accuracy while maintaining efficient inference speeds suitable for real-time applications. The model is designed to detect objects and segment them within images or video streams using a unified detection pipeline. RF-DETR emphasizes strong performance across both accuracy and latency benchmarks, allowing developers to deploy high-quality detection models in applications that require immediate processing such as robotics, autonomous systems, and industrial inspection. The repository includes Python packages, training scripts, and model configurations that enable researchers and engineers to train and deploy detection models on custom datasets. -
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PyBroker
Algorithmic Trading in Python with Machine Learning
...This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance. -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
Albumentations is a computer vision tool that boosts the performance of deep convolutional neural networks. Albumentations is a Python library for fast and flexible image augmentations. Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection. ... -
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x-transformers
A simple but complete full-attention transformer
A simple but complete full-attention transformer with a set of promising experimental features from various papers. Proposes adding learned memory key/values prior to attending. They were able to remove feedforwards altogether and attain a similar performance to the original transformers. I have found that keeping the feedforwards and adding the memory key/values leads to even better performance. Proposes adding learned tokens, akin to CLS tokens, named memory tokens, that is passed through the attention layers alongside the input tokens. You can also use the l2 normalized embeddings proposed as part of fixnorm. ... -
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Evaluate
A library for easily evaluating machine learning models and datasets
Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized. -
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imgclsmob Deep learning networks
Sandbox for training deep learning networks
...It includes implementations of models used for tasks such as image classification, object detection, semantic segmentation, and pose estimation. The repository also contains scripts that help train models, evaluate performance, and convert trained networks between different frameworks. Several deep learning frameworks are supported, allowing researchers to experiment with architectures in different environments. The project is frequently used by developers who want to study modern convolutional neural network designs and compare their performance across datasets. -
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TensorFlow Quantum
Open-source Python framework for hybrid quantum-classical ml learning
...TensorFlow Quantum integrates with the Cirq quantum computing framework to define and manipulate quantum circuits, while leveraging TensorFlow’s infrastructure for optimization, automatic differentiation, and large-scale computation. The library also supports high-performance simulation of quantum circuits, enabling researchers to test and evaluate quantum models even without direct access to quantum hardware. -
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OpenVINO Notebooks
Jupyter notebook tutorials for OpenVINO
...The repository provides practical tutorials that guide developers through various AI workflows including computer vision, natural language processing, and generative AI tasks. Each notebook demonstrates how to run pre-trained models, optimize inference performance, and deploy models across hardware such as CPUs, GPUs, and specialized accelerators. The tutorials also illustrate how OpenVINO integrates with models from frameworks like PyTorch, TensorFlow, and ONNX to accelerate inference workloads. Many notebooks include end-to-end examples that show how to prepare input data, load optimized models, run inference, and visualize results. ... -
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Colossal-AI
Making large AI models cheaper, faster and more accessible
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing. Together with better performance come larger model sizes. This imposes challenges to the memory wall of the current accelerator hardware such as GPU. It is never ideal to train large models such as Vision Transformer, BERT, and GPT on a single GPU or a single machine. -
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Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that... -
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TensorFlow
TensorFlow is an open source library for machine learning
...TensorFlow expresses its computations as dataflow graphs, with each node in the graph representing an operation. Nodes take tensors—multidimensional arrays—as input and produce tensors as output. The framework allows for these algorithms to be run in C++ for better performance, while the multiple levels of APIs let the user determine how high or low they wish the level of abstraction to be in the models produced. Tensorflow can also be used for research and production with TensorFlow Extended. -
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Transformer Engine
A library for accelerating Transformer models on NVIDIA GPUs
Transformer Engine (TE) is a library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper GPUs, to provide better performance with lower memory utilization in both training and inference. TE provides a collection of highly optimized building blocks for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your framework-specific code. TE also includes a framework-agnostic C++ API that can be integrated with other deep-learning libraries to enable FP8 support for Transformers. ... -
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TensorFlow Datasets
TFDS is a collection of datasets ready to use with TensorFlow,
TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data. Datasets , enabling easy-to-use and high-performance input pipelines. To get started see the guide and our list of datasets. -
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Made With ML
Learn how to develop, deploy and iterate on production-grade ML
...The project focuses on bridging the gap between experimental machine learning notebooks and real-world software systems that can be deployed, monitored, and maintained at scale. It provides structured lessons and practical code examples that demonstrate how to design machine learning workflows, manage datasets, train models, evaluate performance, and deploy inference services. The repository organizes these concepts into modular Python scripts that follow software engineering best practices such as testing, configuration management, logging, and version control. Through a combination of tutorials, notebooks, and production-ready scripts, the project demonstrates how machine learning applications should be developed as maintainable systems rather than isolated experiments. -
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HDBSCAN
A high performance implementation of HDBSCAN clustering
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster...
