Search Results for "multi%20seat"
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Showing 131 open source projects for "multi%20seat"
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Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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Retool your internal operationsBuild internal software that meets enterprise security standards without waiting on engineering resources. Retool connects to your databases, APIs, and data sources while maintaining the permissions and controls you need. Create custom dashboards, admin tools, and workflows from natural language prompts—all deployed in your cloud with security baked in. Stop duct-taping operations together, start building in Retool.
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ONNX Runtime
ONNX Runtime: cross-platform, high performance ML inferencing
...ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Support for a variety of frameworks, operating systems and hardware platforms. Built-in optimizations that deliver up to 17X faster inferencing and up to 1.4X faster training. -
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Label Studio
Label Studio is a multi-type data labeling and annotation tool
...It can be used to prepare raw data or improve existing training data to get more accurate ML models. The frontend part of Label Studio app lies in the frontend/ folder and written in React JSX. Multi-user labeling sign up and login, when you create an annotation it's tied to your account. Configurable label formats let you customize the visual interface to meet your specific labeling needs. Support for multiple data types including images, audio, text, HTML, time-series, and video. -
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ViZDoom
Doom-based AI research platform for reinforcement learning
...This means compatibility with a huge range of tools and resources that can be used to create custom scenarios, availability of detailed documentation of the engine and tools and support of Doom community. Async and sync single-player and multi-player modes. Fast (up to 7000 fps in sync mode, single-threaded). Lightweight (few MBs). Customizable resolution and rendering parameters. Access to the depth buffer (3D vision). Automatic labeling of game objects visible in the frame. Access to the list of actors/objects and map geometry.ViZDoom API is reinforcement learning friendly (suitable also for learning from demonstration, apprenticeship learning or apprenticeship via inverse reinforcement learning. -
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YOLOv5
YOLOv5 is the world's most loved vision AI
Introducing Ultralytics YOLOv8, the latest version of the acclaimed real-time object detection and image segmentation model. YOLOv8 is built on cutting-edge advancements in deep learning and computer vision, offering unparalleled performance in terms of speed and accuracy. Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs. Explore the YOLOv8 Docs, a comprehensive resource designed to help... -
Outgrown Windows Task Scheduler?Windows Task Scheduler wasn't built for complex, cross-platform automation. Get a free diagnostic that shows exactly where things are failing and provides remediation recommendations. Interactive HTML report delivered in minutes.
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. ... -
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pycm
Multi-class confusion matrix library in Python
PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. -
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DataFrame
C++ DataFrame for statistical, Financial, and ML analysis
...You can join, merge, and group-by the data. You can run various statistical, summarization, financial, and ML algorithms on the data. You can add your custom algorithms easily. You can multi-column sort, custom pick, and delete the data. DataFrame also includes a large collection of analytical algorithms in the form of visitors. These are from basic stats such as Mean, and Std Deviation and return, … to more involved analysis such as Affinity Propagation, Polynomial Fit, and Fast Fourier transform of arbitrary length … including a good collection of trading indicators. ... -
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OpenCV
Open Source Computer Vision Library
OpenCV (Open Source Computer Vision Library) is a comprehensive open-source library for computer vision, machine learning, and image processing. It enables developers to build real-time vision applications ranging from facial recognition to object tracking. OpenCV supports a wide range of programming languages including C++, Python, and Java, and is optimized for both CPU and GPU operations. -
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Google AI Edge Gallery
A gallery that showcases on-device ML/GenAI use cases
Gallery is a curated collection of on-device machine learning examples, demo apps, and model artifacts designed to help developers experiment with and deploy ML at the edge. The project bundles runnable samples that show how to run TensorFlow Lite/Edge TPU models (and similar lightweight runtimes) on mobile and embedded platforms, demonstrating common tasks like image classification, object detection, audio recognition, and pose estimation. Each sample is intended to be both a learning aid... -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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cuML
RAPIDS Machine Learning Library
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU... -
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Basic Pitch
A lightweight audio-to-MIDI converter with pitch bend detection
...It's small, easy-to-use, pip install-able and npm install-able via its sibling repo. Basic Pitch may be simple, but it's is far from "basic"! basic-pitch is efficient and easy to use, and its multi pitch support, its ability to generalize across instruments, and its note accuracy compete with much larger and more resource-hungry AMT systems. Provide a compatible audio file and a basic-pitch will generate a MIDI file, complete with pitch bends. The basic pitch is instrument-agnostic and supports polyphonic instruments, so you can freely enjoy transcription of all your favorite music, no matter what instrument is used. ... -
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CatBoost
High-performance library for gradient boosting on decision trees
CatBoost is a fast, high-performance open source library for gradient boosting on decision trees. It is a machine learning method with plenty of applications, including ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. CatBoost offers superior performance over other GBDT libraries on many datasets, and has several superb features. It has best in class prediction speed, supports both numerical and categorical features, has a fast and scalable... -
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deepfakes_faceswap
Deepfakes Software For All
Faceswap is the leading free and open source multi-platform deepfakes software. When faceswapping was first developed and published, the technology was groundbreaking, it was a huge step in AI development. It was also completely ignored outside of academia because the code was confusing and fragmentary. It required a thorough understanding of complicated AI techniques and took a lot of effort to figure it out. -
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PyGAD
Source code of PyGAD, Python 3 library for building genetic algorithms
PyGAD is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine learning algorithms. It supports Keras and PyTorch. PyGAD supports optimizing both single-objective and multi-objective problems. PyGAD supports different types of crossover, mutation, and parent selection. PyGAD allows different types of problems to be optimized using the genetic algorithm by customizing the fitness function. -
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spaCy models
Models for the spaCy Natural Language Processing (NLP) library
spaCy is designed to help you do real work, to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive. spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. If your application needs to process entire web dumps, spaCy is the library you want to be using. Since its release in 2015, spaCy has become an industry... -
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OpenVINO
OpenVINO™ Toolkit repository
...Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi. -
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NVIDIA NeMo
Toolkit for conversational AI
...Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. NeMo uses PyTorch Lightning for easy and performant multi-GPU/multi-node mixed-precision training. Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC. NGC collection of pre-trained speech processing models. -
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PyTorch Forecasting
Time series forecasting with PyTorch
PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with... -
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CARLA Simulator
Open-source simulator for autonomous driving research.
CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites, environmental conditions, full control of all static and dynamic actors, maps generation and much more. Multiple clients... -
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TorchRec
Pytorch domain library for recommendation systems
TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs. Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism. The TorchRec sharder can shard embedding tables with different sharding strategies including data-parallel, table-wise, row-wise, table-wise-row-wise, and column-wise sharding. The TorchRec planner can automatically generate optimized sharding plans for models. ... -
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DeepXDE
A library for scientific machine learning & physics-informed learning
...PINN with hard constraints (hPINN): solving inverse design/topology optimization [SIAM J. Sci. Comput.] Residual-based adaptive sampling [SIAM Rev., arXiv] Gradient-enhanced PINN (gPINN) [Comput. Methods Appl. Mech. Eng.] PINN with multi-scale Fourier features [Comput. Methods Appl. Mech. Eng.] -
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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
...DeepPavlov is an open-source framework for chatbots and virtual assistants development. It has comprehensive and flexible tools that let developers and NLP researchers create production-ready conversational skills and complex multi-skill conversational assistants. Use BERT and other state-of-the-art deep learning models to solve classification, NER, Q&A and other NLP tasks. DeepPavlov Agent allows building industrial solutions with multi-skill integration via API services. -
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MLRun
Machine Learning automation and tracking
MLRun is an open MLOps framework for quickly building and managing continuous ML and generative AI applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous... -
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PyTensor
Python library for defining and optimizing mathematical expressions
PyTensor is a fork of Aesara, a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays. PyTensor is based on Theano, which has been powering large-scale computationally intensive scientific investigations since 2007. A hackable, pure-Python codebase. Extensible graph framework is suitable for rapid development of custom operators and symbolic optimizations. Implements an extensible graph transpilation framework that currently provides compilation via C, JAX, and Numba. ... -
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mlforecast
Scalable machine learning for time series forecasting
...Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates those automatically based on a simple configuration. It supports multi-series forecasting, meaning you can train one model that forecasts many time series at once (common in retail, demand forecasting, etc.), rather than one model per series. The library is built to scale: behind the scenes, it can leverage distributed computing frameworks (Spark, Dask, Ray) when datasets or the number of series grow large.
